





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

_lowerCamelCase 					=      logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       field(
							    default=1_28					,	metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Overwrite the cached preprocessed datasets or not."}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }					,	)




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Train language if it is different from the evaluation language."}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}					,	)
							lowerCamelCase_							     =       field(
							    default="main"					,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}					,	)






def 					SCREAMING_SNAKE_CASE__				(							):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE__								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("""run_xnli"""       , UpperCamelCase__     )

		# Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       , datefmt="""%m/%d/%Y %H:%M:%S"""       , handlers=[logging.StreamHandler(sys.stdout     )]       , )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		SCREAMING_SNAKE_CASE__								=   training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase__     )
		datasets.utils.logging.set_verbosity(UpperCamelCase__     )
		transformers.utils.logging.set_verbosity(UpperCamelCase__     )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
		    + f'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'''     )
		logger.info(f'''Training/evaluation parameters {training_args}'''     )

		# Detecting last checkpoint.
		SCREAMING_SNAKE_CASE__								=   None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				SCREAMING_SNAKE_CASE__								=   get_last_checkpoint(training_args.output_dir     )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
						    """Use --overwrite_output_dir to overcome."""     )
				elif last_checkpoint is not None:
						logger.info(
						    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""     )

    # Set seed before initializing model.
		set_seed(training_args.seed     )

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						SCREAMING_SNAKE_CASE__								=   load_dataset(
						    """xnli"""       , model_args.language       , split="""train"""       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
				else:
						SCREAMING_SNAKE_CASE__								=   load_dataset(
						    """xnli"""       , model_args.train_language       , split="""train"""       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
				SCREAMING_SNAKE_CASE__								=   train_dataset.features["""label"""].names

		if training_args.do_eval:
				SCREAMING_SNAKE_CASE__								=   load_dataset(
				    """xnli"""       , model_args.language       , split="""validation"""       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
				SCREAMING_SNAKE_CASE__								=   eval_dataset.features["""label"""].names

		if training_args.do_predict:
				SCREAMING_SNAKE_CASE__								=   load_dataset(
				    """xnli"""       , model_args.language       , split="""test"""       , cache_dir=model_args.cache_dir       , use_auth_token=True if model_args.use_auth_token else None       , )
				SCREAMING_SNAKE_CASE__								=   predict_dataset.features["""label"""].names

		# Labels
		SCREAMING_SNAKE_CASE__								=   len(UpperCamelCase__     )

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       , num_labels=UpperCamelCase__       , idalabel={str(UpperCamelCase__     ): label for i, label in enumerate(UpperCamelCase__     )}       , labelaid={label: i for i, label in enumerate(UpperCamelCase__     )}       , finetuning_task="""xnli"""       , cache_dir=model_args.cache_dir       , revision=model_args.model_revision       , use_auth_token=True if model_args.use_auth_token else None       , )
		SCREAMING_SNAKE_CASE__								=   AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       , do_lower_case=model_args.do_lower_case       , cache_dir=model_args.cache_dir       , use_fast=model_args.use_fast_tokenizer       , revision=model_args.model_revision       , use_auth_token=True if model_args.use_auth_token else None       , )
		SCREAMING_SNAKE_CASE__								=   AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path       , from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )       , config=UpperCamelCase__       , cache_dir=model_args.cache_dir       , revision=model_args.model_revision       , use_auth_token=True if model_args.use_auth_token else None       , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes       , )

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				SCREAMING_SNAKE_CASE__								=   """max_length"""
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				SCREAMING_SNAKE_CASE__								=   False

		def preprocess_function(UpperCamelCase__:				Any     ):
				# Tokenize the texts
				return tokenizer(
				    examples["""premise"""]       , examples["""hypothesis"""]       , padding=UpperCamelCase__       , max_length=data_args.max_seq_length       , truncation=UpperCamelCase__       , )

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_train_samples     )
						SCREAMING_SNAKE_CASE__								=   train_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""train dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   train_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , load_from_cache_file=not data_args.overwrite_cache       , desc="""Running tokenizer on train dataset"""       , )
				# Log a few random samples from the training set:
				for index in random.sample(range(len(UpperCamelCase__     )     )       , 3     ):
						logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''     )

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_eval_samples     )
						SCREAMING_SNAKE_CASE__								=   eval_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""validation dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   eval_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , load_from_cache_file=not data_args.overwrite_cache       , desc="""Running tokenizer on validation dataset"""       , )

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_predict_samples     )
						SCREAMING_SNAKE_CASE__								=   predict_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""prediction dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   predict_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , load_from_cache_file=not data_args.overwrite_cache       , desc="""Running tokenizer on prediction dataset"""       , )

    # Get the metric function
		SCREAMING_SNAKE_CASE__								=   evaluate.load("""xnli"""     )

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(UpperCamelCase__:				EvalPrediction     ):
				SCREAMING_SNAKE_CASE__								=   p.predictions[0] if isinstance(p.predictions       , UpperCamelCase__     ) else p.predictions
				SCREAMING_SNAKE_CASE__								=   np.argmax(UpperCamelCase__       , axis=1     )
				return metric.compute(predictions=UpperCamelCase__       , references=p.label_ids     )

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				SCREAMING_SNAKE_CASE__								=   default_data_collator
		elif training_args.fpaa:
				SCREAMING_SNAKE_CASE__								=   DataCollatorWithPadding(UpperCamelCase__       , pad_to_multiple_of=8     )
		else:
				SCREAMING_SNAKE_CASE__								=   None

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE__								=   Trainer(
		    model=UpperCamelCase__       , args=UpperCamelCase__       , train_dataset=train_dataset if training_args.do_train else None       , eval_dataset=eval_dataset if training_args.do_eval else None       , compute_metrics=UpperCamelCase__       , tokenizer=UpperCamelCase__       , data_collator=UpperCamelCase__       , )

		# Training
		if training_args.do_train:
				SCREAMING_SNAKE_CASE__								=   None
				if training_args.resume_from_checkpoint is not None:
						SCREAMING_SNAKE_CASE__								=   training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						SCREAMING_SNAKE_CASE__								=   last_checkpoint
				SCREAMING_SNAKE_CASE__								=   trainer.train(resume_from_checkpoint=UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   train_result.metrics
				SCREAMING_SNAKE_CASE__								=   (
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCamelCase__     )
				)
				SCREAMING_SNAKE_CASE__								=   min(UpperCamelCase__       , len(UpperCamelCase__     )     )

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics("""train"""       , UpperCamelCase__     )
				trainer.save_metrics("""train"""       , UpperCamelCase__     )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""     )
				SCREAMING_SNAKE_CASE__								=   trainer.evaluate(eval_dataset=UpperCamelCase__     )

				SCREAMING_SNAKE_CASE__								=   data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   min(UpperCamelCase__       , len(UpperCamelCase__     )     )

				trainer.log_metrics("""eval"""       , UpperCamelCase__     )
				trainer.save_metrics("""eval"""       , UpperCamelCase__     )

		# Prediction
		if training_args.do_predict:
				logger.info("""*** Predict ***"""     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   trainer.predict(UpperCamelCase__       , metric_key_prefix="""predict"""     )

				SCREAMING_SNAKE_CASE__								=   (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase__     )
				)
				SCREAMING_SNAKE_CASE__								=   min(UpperCamelCase__       , len(UpperCamelCase__     )     )

				trainer.log_metrics("""predict"""       , UpperCamelCase__     )
				trainer.save_metrics("""predict"""       , UpperCamelCase__     )

				SCREAMING_SNAKE_CASE__								=   np.argmax(UpperCamelCase__       , axis=1     )
				SCREAMING_SNAKE_CASE__								=   os.path.join(training_args.output_dir       , """predictions.txt"""     )
				if trainer.is_world_process_zero():
						with open(UpperCamelCase__       , """w"""     ) as writer:
								writer.write("""index\tprediction\n"""     )
								for index, item in enumerate(UpperCamelCase__     ):
										SCREAMING_SNAKE_CASE__								=   label_list[item]
										writer.write(f'''{index}\t{item}\n'''     )


if __name__ == "__main__":
	main()

'''simple docstring'''




import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import MaskFormerSwinBackbone
      from transformers.models.maskformer import MaskFormerSwinModel





class 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=[1, 2, 1]		,     SCREAMING_SNAKE_CASE=[2, 2, 4]		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=2.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-5		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=10		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"]		,     SCREAMING_SNAKE_CASE=[1, 2, 3]		,     )					->							Optional[int]:






         """simple docstring"""






         A			:  Any   =		parent
         A			:  List[Any]   =		batch_size
         A			:  Any   =		image_size
         A			:  Optional[Any]   =		patch_size
         A			:  Dict   =		num_channels
         A			:  Union[str, Any]   =		embed_dim
         A			:  Optional[int]   =		depths
         A			:  int   =		num_heads
         A			:  Tuple   =		window_size
         A			:  str   =		mlp_ratio
         A			:  Optional[Any]   =		qkv_bias
         A			:  Optional[int]   =		hidden_dropout_prob
         A			:  Any   =		attention_probs_dropout_prob
         A			:  List[Any]   =		drop_path_rate
         A			:  Tuple   =		hidden_act
         A			:  Any   =		use_absolute_embeddings
         A			:  Optional[int]   =		patch_norm
         A			:  List[str]   =		layer_norm_eps
         A			:  int   =		initializer_range
         A			:  Tuple   =		is_training
         A			:  int   =		scope
         A			:  List[Any]   =		use_labels
         A			:  List[str]   =		type_sequence_label_size
         A			:  int   =		encoder_stride
         A			:  List[Any]   =		out_features
         A			:  str   =		out_indices





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Optional[int]   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         A			:  Union[str, Any]   =		None
         if self.use_labels:
             A			:  int   =		ids_tensor([self.batch_size]		,     self.type_sequence_label_size      )

         A			:  str   =		self.get_config()

         return config, pixel_values, labels





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         return MaskFormerSwinConfig(
             image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     window_size=self.window_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     use_absolute_embeddings=self.use_absolute_embeddings		,     path_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     encoder_stride=self.encoder_stride		,     out_features=self.out_features		,     out_indices=self.out_indices		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         A			:  List[Any]   =		MaskFormerSwinModel(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      )

         A			:  Tuple   =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
         A			:  Optional[int]   =		int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, expected_seq_len, expected_dim)      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Any:






         """simple docstring"""






         A			:  Tuple   =		MaskFormerSwinBackbone(config=SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()
         A			:  Union[str, Any]   =		model(SCREAMING_SNAKE_CASE      )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps      )		,     len(config.out_features      )      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )		,     [13, 16, 16, 16]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )		,     len(config.out_features      )      )
         self.parent.assertListEqual(model.channels		,     [16, 32, 64]      )

         # verify ValueError
         with self.parent.assertRaises(SCREAMING_SNAKE_CASE      ):
             A			:  str   =		['''stem''']
             A			:  Dict   =		MaskFormerSwinBackbone(config=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		self.prepare_config_and_inputs()
         A,    A,    A			:  int   =		config_and_inputs
         A			:  List[str]   =		{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__          =						{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		MaskFormerSwinModelTester(self      )
         A			:  int   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     embed_dim=37      )





     @require_torch_multi_gpu
     @unittest.skip(
         reason=(
             '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
             ''' `nn.DataParallel`'''
         )      )
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         return





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE      )





     @unittest.skip('''Swin does not use inputs_embeds'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         pass





     @unittest.skip('''Swin does not support feedforward chunking'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  Optional[int]   =		model_class(SCREAMING_SNAKE_CASE      )
             self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module)      )
             A			:  int   =		model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE		,     nn.Linear      )      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  List[str]   =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A			:  List[Any]   =		model_class(SCREAMING_SNAKE_CASE      )
             A			:  List[str]   =		inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             A			:  Optional[Any]   =		[*signature.parameters.keys()]

             A			:  Union[str, Any]   =		['''pixel_values''']
             self.assertListEqual(arg_names[:1]		,     SCREAMING_SNAKE_CASE      )





     @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         pass





     @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Optional[int]:






         """simple docstring"""






         A			:  Tuple   =		model_class(SCREAMING_SNAKE_CASE      )
         model.to(SCREAMING_SNAKE_CASE      )
         model.eval()

         with torch.no_grad():
             A			:  Any   =		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )      )

         A			:  List[str]   =		outputs.hidden_states

         A			:  str   =		getattr(
             self.model_tester		,     '''expected_num_hidden_layers'''		,     len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )

         # Swin has a different seq_length
         A			:  List[str]   =		(
             config.patch_size
             if isinstance(config.patch_size		,     collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         A			:  Optional[Any]   =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      )		,     [num_patches, self.model_tester.embed_dim]		,     )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

         A			:  Union[str, Any]   =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,     collections.abc.Iterable      )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
             A			:  str   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True

             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  Any   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  Union[str, Any]   =		3

         A			:  List[str]   =		(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size		,     collections.abc.Iterable      )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         A			:  Dict   =		(
             config.patch_size
             if isinstance(config.patch_size		,     collections.abc.Iterable      )
             else (config.patch_size, config.patch_size)
         )

         A			:  Any   =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         A			:  Any   =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
             A			:  Optional[Any]   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     (padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             A			:  Optional[Any]   =		True
             self.check_hidden_states_output(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     (padded_height, padded_width)      )





     @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         pass





     @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         pass





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A,    A			:  str   =		self.model_tester.prepare_config_and_inputs_for_common()

         def set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      ):
             A			:  List[str]   =		0
             return t

         def check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE={}      ):
             with torch.no_grad():
                 A			:  List[Any]   =		model(**SCREAMING_SNAKE_CASE		,     return_dict=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )
                 A			:  str   =		model(**SCREAMING_SNAKE_CASE		,     return_dict=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      ).to_tuple()

                 def recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                     if isinstance(SCREAMING_SNAKE_CASE		,     (List, Tuple)      ):
                         for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                             recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                     elif isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                         for tuple_iterable_value, dict_iterable_value in zip(
                             tuple_object.values()		,     dict_object.values()      ):
                             recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
                     elif tuple_object is None:
                         return
                     else:
                         self.assertTrue(
                             torch.allclose(
                                 set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      )		,     set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE      )		,     atol=1e-5      )		,     msg=(
                                 '''Tuple and dict output are not equal. Difference:'''
                                 F' {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:'
                                 F' {torch.isnan(SCREAMING_SNAKE_CASE      ).any()} and `inf`: {torch.isinf(SCREAMING_SNAKE_CASE      )}. Dict has'
                                 F' `nan`: {torch.isnan(SCREAMING_SNAKE_CASE      ).any()} and `inf`: {torch.isinf(SCREAMING_SNAKE_CASE      )}.'
                             )		,     )

                 recursive_check(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         for model_class in self.all_model_classes:
             A			:  Tuple   =		model_class(SCREAMING_SNAKE_CASE      )
             model.to(SCREAMING_SNAKE_CASE      )
             model.eval()

             A			:  List[str]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             A			:  int   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             A			:  Union[str, Any]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  int   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

             A			:  Tuple   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             A			:  Any   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     {'''output_hidden_states''': True}      )

             A			:  Optional[int]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             A			:  Dict   =		self._prepare_for_class(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     return_labels=SCREAMING_SNAKE_CASE      )
             check_equivalence(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     {'''output_hidden_states''': True}      )





@require_torch
class 					A							(				unittest.TestCase       ,     __snake_case		):
     __magic_name__          =						(MaskFormerSwinBackbone,) if is_torch_available() else ()
     __magic_name__          =						MaskFormerSwinConfig





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Dict   =		MaskFormerSwinModelTester(self      )





     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  str   =		inputs_dict['''pixel_values'''].shape[0]

         for backbone_class in self.all_model_classes:
             A			:  Any   =		backbone_class(SCREAMING_SNAKE_CASE      )
             backbone.to(SCREAMING_SNAKE_CASE      )
             backbone.eval()

             A			:  Dict   =		backbone(**SCREAMING_SNAKE_CASE      )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps		,     SCREAMING_SNAKE_CASE      )
             self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
             for feature_map, n_channels in zip(outputs.feature_maps		,     backbone.channels      ):
                 self.assertTrue(feature_map.shape[:2]		,     (batch_size, n_channels)      )
             self.assertIsNone(outputs.hidden_states      )
             self.assertIsNone(outputs.attentions      )

             # Test output_hidden_states=True
             A			:  Optional[int]   =		backbone(**SCREAMING_SNAKE_CASE		,     output_hidden_states=SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(outputs.hidden_states      )
             self.assertTrue(len(outputs.hidden_states      )		,     len(backbone.stage_names      )      )
             # We skip the stem layer
             for hidden_states, n_channels in zip(outputs.hidden_states[1:]		,     backbone.channels      ):
                 for hidden_state in hidden_states:
                     # Hidden states are in the format (batch_size, (height * width), n_channels)
                     A,    A,    A			:  List[Any]   =		hidden_state.shape
                     self.assertTrue((h_batch_size, h_n_channels)		,     (batch_size, n_channels)      )

            # Test output_attentions=True
             if self.has_attentions:
                 A			:  Dict   =		backbone(**SCREAMING_SNAKE_CASE		,     output_attentions=SCREAMING_SNAKE_CASE      )
                 self.assertIsNotNone(outputs.attentions      )




