
'''simple docstring'''
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase__						       =      "true"




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=82 ,       SCREAMING_SNAKE_CASE__=16			)  ->							int:


		'''simple docstring'''


		set_seed(42			)
		snake_case  :       Union[str, Any]     			=						RegressionModel()
		snake_case  :       Tuple     			=						deepcopy(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Union[str, Any]     			=						RegressionDataset(length=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[Any]     			=						DataLoader(SCREAMING_SNAKE_CASE__ ,       batch_size=SCREAMING_SNAKE_CASE__			)
		model.to(accelerator.device			)
		snake_case			,snake_case  :       List[Any]     			=						accelerator.prepare(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		return model, ddp_model, dataloader




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=False			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Dict     			=						AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''			)
		snake_case  :       Tuple     			=						load_dataset('''glue''' ,       '''mrpc''' ,       split='''validation'''			)

		def tokenize_function(SCREAMING_SNAKE_CASE__			):
				snake_case  :       List[Any]     			=						tokenizer(examples['''sentence1'''] ,       examples['''sentence2'''] ,       truncation=SCREAMING_SNAKE_CASE__ ,       max_length=SCREAMING_SNAKE_CASE__			)
				return outputs

		with accelerator.main_process_first():
				snake_case  :       List[Any]     			=						dataset.map(
				    SCREAMING_SNAKE_CASE__ ,       batched=SCREAMING_SNAKE_CASE__ ,       remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,       )

		snake_case  :       List[str]     			=						tokenized_datasets.rename_column('''label''' ,       '''labels'''			)

		def collate_fn(SCREAMING_SNAKE_CASE__			):
				if use_longest:
						return tokenizer.pad(SCREAMING_SNAKE_CASE__ ,       padding='''longest''' ,       return_tensors='''pt'''			)
				return tokenizer.pad(SCREAMING_SNAKE_CASE__ ,       padding='''max_length''' ,       max_length=128 ,       return_tensors='''pt'''			)

		return DataLoader(SCREAMING_SNAKE_CASE__ ,       shuffle=SCREAMING_SNAKE_CASE__ ,       collate_fn=SCREAMING_SNAKE_CASE__ ,       batch_size=16			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       int     			=						Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__ ,       split_batches=SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[str]     			=						get_dataloader(SCREAMING_SNAKE_CASE__ ,       not dispatch_batches			)
		snake_case  :       Tuple     			=						AutoModelForSequenceClassification.from_pretrained(
		    '''hf-internal-testing/mrpc-bert-base-cased''' ,       return_dict=SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case  :       Optional[int]     			=						accelerator.prepare(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						[]
		for batch in dataloader:
				snake_case			,snake_case  :       List[Any]     			=						batch.values()
				with torch.no_grad():
						snake_case  :       Optional[int]     			=						model(SCREAMING_SNAKE_CASE__			)
						snake_case			,snake_case  :       str     			=						accelerator.gather_for_metrics((logit, target)			)
						logits_and_targets.append((logit, target)			)
		snake_case			,snake_case  :       Optional[int]     			=						[], []
		for logit, targ in logits_and_targets:
				logits.append(SCREAMING_SNAKE_CASE__			)
				targs.append(SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case  :       List[str]     			=						torch.cat(SCREAMING_SNAKE_CASE__			), torch.cat(SCREAMING_SNAKE_CASE__			)
		return logits, targs




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=82 ,       SCREAMING_SNAKE_CASE__=False ,       SCREAMING_SNAKE_CASE__=False ,       SCREAMING_SNAKE_CASE__=16			)  ->							Tuple:


		'''simple docstring'''


		snake_case			,snake_case			,snake_case  :       Dict     			=						get_basic_setup(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case  :       List[Any]     			=						generate_predictions(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		assert (
		    len(SCREAMING_SNAKE_CASE__			) == num_samples
		), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__			)}'




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ = False ,       SCREAMING_SNAKE_CASE__ = False			)  ->							Dict:


		'''simple docstring'''


		snake_case  :       Tuple     			=						evaluate.load('''glue''' ,       '''mrpc'''			)
		snake_case			,snake_case  :       Union[str, Any]     			=						get_mrpc_setup(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		# First do baseline
		snake_case			,snake_case			,snake_case  :       str     			=						setup['''no''']
		model.to(SCREAMING_SNAKE_CASE__			)
		model.eval()
		for batch in dataloader:
				batch.to(SCREAMING_SNAKE_CASE__			)
				with torch.inference_mode():
						snake_case  :       List[str]     			=						model(**SCREAMING_SNAKE_CASE__			)
				snake_case  :       List[str]     			=						outputs.logits.argmax(dim=-1			)
				metric.add_batch(predictions=SCREAMING_SNAKE_CASE__ ,       references=batch['''labels''']			)
		snake_case  :       List[str]     			=						metric.compute()

		# Then do distributed
		snake_case			,snake_case			,snake_case  :       List[str]     			=						setup['''ddp''']
		model.eval()
		for batch in dataloader:
				with torch.inference_mode():
						snake_case  :       Dict     			=						model(**SCREAMING_SNAKE_CASE__			)
				snake_case  :       int     			=						outputs.logits.argmax(dim=-1			)
				snake_case  :       Any     			=						batch['''labels''']
				snake_case			,snake_case  :       Tuple     			=						accelerator.gather_for_metrics((preds, references)			)
				metric.add_batch(predictions=SCREAMING_SNAKE_CASE__ ,       references=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[int]     			=						metric.compute()

		for key in "accuracy f1".split():
				assert math.isclose(
				    baseline[key] ,       distributed[key]			), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'




def    _UpperCamelCase						(       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						Accelerator(split_batches=SCREAMING_SNAKE_CASE__ ,       dispatch_batches=SCREAMING_SNAKE_CASE__			)
		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_warning()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		# These are a bit slower so they should only be ran on the GPU or TPU
		if torch.cuda.is_available() or is_tpu_available():
				if accelerator.is_local_main_process:
						print('''**Testing gather_for_metrics**'''			)
				for split_batches in [True, False]:
						for dispatch_batches in [True, False]:
								if accelerator.is_local_main_process:
										print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'			)
								test_mrpc(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
								accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test torch metrics**'''			)
		for split_batches in [True, False]:
				for dispatch_batches in [True, False]:
						snake_case  :       Tuple     			=						Accelerator(split_batches=SCREAMING_SNAKE_CASE__ ,       dispatch_batches=SCREAMING_SNAKE_CASE__			)
						if accelerator.is_local_main_process:
								print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'			)
						test_torch_metrics(SCREAMING_SNAKE_CASE__ ,       99			)
						accelerator.state._reset_state()
		if accelerator.is_local_main_process:
				print('''**Test last batch is not dropped when perfectly divisible**'''			)
		snake_case  :       List[Any]     			=						Accelerator()
		test_torch_metrics(SCREAMING_SNAKE_CASE__ ,       512			)
		accelerator.state._reset_state()




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Optional[int]:


		'''simple docstring'''


		main()


if __name__ == "__main__":
						main()




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


