





"""simple docstring"""




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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    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
a						: int     	=			logging.getLogger(__name__)

# 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/image-classification/requirements.txt''')

a						: Union[str, Any]     	=			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a						: Tuple     	=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->int:






	'''simple docstring'''






	with open(_lowercase    ,       "rb"					) as f:
		a      :		Optional[int]	=				Image.open(_lowercase					)
		return im.convert("RGB"					)


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={
							        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
							    }		,       )
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
							lowerCamelCase   :					Optional[str]      					=field(default=a__		,       metadata={"""help""": """A folder containing the training data."""}						)
							lowerCamelCase   :					Optional[str]      					=field(default=a__		,       metadata={"""help""": """A folder containing the validation data."""}						)
							lowerCamelCase   :					Optional[float]      					=field(
							    default=0.15		,       metadata={"""help""": """Percent to split off of train for validation."""}						)
							lowerCamelCase   :					Optional[int]      					=field(
							    default=a__		,       metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }		,       )
							lowerCamelCase   :					Optional[int]      					=field(
							    default=a__		,       metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
							            """value if set."""
							        )
							    }		,       )






							def     __a   (   self      )	-> Tuple:
								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
									raise ValueError(
									    "You must specify either a dataset name from the hub or a train and/or validation directory."      )


@dataclass
class        __UpperCamelCase				:
							lowerCamelCase   :					str      					=field(
							    default="""google/vit-base-patch16-224-in21k"""		,       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,       )
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a__						)}		,       )
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
							lowerCamelCase   :					Optional[str]      					=field(
							    default=a__		,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}						)
							lowerCamelCase   :					str      					=field(
							    default="""main"""		,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,       )
							lowerCamelCase   :					str      					=field(default=a__		,       metadata={"""help""": """Name or path of preprocessor config."""}						)
							lowerCamelCase   :					bool      					=field(
							    default=a__		,       metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }		,       )
							lowerCamelCase   :					bool      					=field(
							    default=a__		,       metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,       )






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->Optional[Any]:






	'''simple docstring'''






	a      :		Union[str, Any]	=				torch.stack([example["pixel_values"] for example in examples]					)
	a      :		Dict	=				torch.tensor([example["labels"] for example in examples]					)
	return {"pixel_values": pixel_values, "labels": labels}






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		List[Any]	=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
	if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		a,       a,       a      :		Union[str, Any]	=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		a,       a,       a      :		Dict	=				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_image_classification"    ,       _lowercase    ,       _lowercase					)

	# 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()

	a      :		List[str]	=				training_args.get_process_log_level()
	logger.setLevel(_lowercase					)
	transformers.utils.logging.set_verbosity(_lowercase					)
	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.
	a      :		Tuple	=				None
	if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
		a      :		Optional[Any]	=				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 and training_args.resume_from_checkpoint is 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					)

	# Initialize our dataset and prepare it for the 'image-classification' task.
	if data_args.dataset_name is not None:
		a      :		Optional[int]	=				load_dataset(
		    data_args.dataset_name    ,       data_args.dataset_config_name    ,       cache_dir=model_args.cache_dir    ,       task="image-classification"    ,       use_auth_token=True if model_args.use_auth_token else None    ,       )
	else:
		a      :		int	=				{}
		if data_args.train_dir is not None:
			a      :		List[Any]	=				os.path.join(data_args.train_dir    ,       "**"					)
		if data_args.validation_dir is not None:
			a      :		Optional[Any]	=				os.path.join(data_args.validation_dir    ,       "**"					)
		a      :		List[Any]	=				load_dataset(
		    "imagefolder"    ,       data_files=_lowercase    ,       cache_dir=model_args.cache_dir    ,       task="image-classification"    ,       )

	# If we don't have a validation split, split off a percentage of train as validation.
	a      :		List[Any]	=				None if "validation" in dataset.keys() else data_args.train_val_split
	if isinstance(data_args.train_val_split    ,       _lowercase					) and data_args.train_val_split > 0.0:
		a      :		Tuple	=				dataset["train"].train_test_split(data_args.train_val_split					)
		a      :		List[str]	=				split["train"]
		a      :		Optional[int]	=				split["test"]

	# Prepare label mappings.
	# We'll include these in the model's config to get human readable labels in the Inference API.
	a      :		Tuple	=				dataset["train"].features["labels"].names
	a,       a      :		Optional[int]	=				{}, {}
	for i, label in enumerate(_lowercase					):
		a      :		Union[str, Any]	=				str(_lowercase					)
		a      :		Optional[Any]	=				label

	# Load the accuracy metric from the datasets package
	a      :		Optional[Any]	=				evaluate.load("accuracy"					)

	# Define our 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(_lowercase						:    Optional[Any]					):
		return metric.compute(predictions=np.argmax(p.predictions    ,       axis=1					)    ,       references=p.label_ids					)

	a      :		str	=				AutoConfig.from_pretrained(
	    model_args.config_name or model_args.model_name_or_path    ,       num_labels=len(_lowercase					)    ,       labelaid=_lowercase    ,       idalabel=_lowercase    ,       finetuning_task="image-classification"    ,       cache_dir=model_args.cache_dir    ,       revision=model_args.model_revision    ,       use_auth_token=True if model_args.use_auth_token else None    ,       )
	a      :		int	=				AutoModelForImageClassification.from_pretrained(
	    model_args.model_name_or_path    ,       from_tf=bool(".ckpt" in model_args.model_name_or_path					)    ,       config=_lowercase    ,       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    ,       )
	a      :		Dict	=				AutoImageProcessor.from_pretrained(
	    model_args.image_processor_name or model_args.model_name_or_path    ,       cache_dir=model_args.cache_dir    ,       revision=model_args.model_revision    ,       use_auth_token=True if model_args.use_auth_token else None    ,       )

	# Define torchvision transforms to be applied to each image.
	if "shortest_edge" in image_processor.size:
		a      :		str	=				image_processor.size["shortest_edge"]
	else:
		a      :		Any	=				(image_processor.size["height"], image_processor.size["width"])
	a      :		List[Any]	=				Normalize(mean=image_processor.image_mean    ,       std=image_processor.image_std					)
	a      :		List[Any]	=				Compose(
	    [
	        RandomResizedCrop(_lowercase					),
	        RandomHorizontalFlip(),
	        ToTensor(),
	        normalize,
	    ]					)
	a      :		Optional[Any]	=				Compose(
	    [
	        Resize(_lowercase					),
	        CenterCrop(_lowercase					),
	        ToTensor(),
	        normalize,
	    ]					)

	def train_transforms(_lowercase						:    Optional[int]					):
		a      :		Union[str, Any]	=				[
		    _train_transforms(pil_img.convert("RGB"					)					) for pil_img in example_batch["image"]
		]
		return example_batch

	def val_transforms(_lowercase						:    List[str]					):
		a      :		List[Any]	=				[_val_transforms(pil_img.convert("RGB"					)					) for pil_img in example_batch["image"]]
		return example_batch

	if training_args.do_train:
		if "train" not in dataset:
			raise ValueError("--do_train requires a train dataset"					)
		if data_args.max_train_samples is not None:
			a      :		Union[str, Any]	=				(
			    dataset["train"].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
			)
		# Set the training transforms
		dataset["train"].set_transform(_lowercase					)

	if training_args.do_eval:
		if "validation" not in dataset:
			raise ValueError("--do_eval requires a validation dataset"					)
		if data_args.max_eval_samples is not None:
			a      :		Any	=				(
			    dataset["validation"].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
			)
		# Set the validation transforms
		dataset["validation"].set_transform(_lowercase					)

	# Initalize our trainer
	a      :		Optional[Any]	=				Trainer(
	    model=_lowercase    ,       args=_lowercase    ,       train_dataset=dataset["train"] if training_args.do_train else None    ,       eval_dataset=dataset["validation"] if training_args.do_eval else None    ,       compute_metrics=_lowercase    ,       tokenizer=_lowercase    ,       data_collator=_lowercase    ,       )

	# Training
	if training_args.do_train:
		a      :		Any	=				None
		if training_args.resume_from_checkpoint is not None:
			a      :		Tuple	=				training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			a      :		Union[str, Any]	=				last_checkpoint
		a      :		Dict	=				trainer.train(resume_from_checkpoint=_lowercase					)
		trainer.save_model()
		trainer.log_metrics("train"    ,       train_result.metrics					)
		trainer.save_metrics("train"    ,       train_result.metrics					)
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		a      :		Optional[Any]	=				trainer.evaluate()
		trainer.log_metrics("eval"    ,       _lowercase					)
		trainer.save_metrics("eval"    ,       _lowercase					)

	# Write model card and (optionally) push to hub
	a      :		Optional[int]	=				{
	    "finetuned_from": model_args.model_name_or_path,
	    "tasks": "image-classification",
	    "dataset": data_args.dataset_name,
	    "tags": ["image-classification", "vision"],
	}
	if training_args.push_to_hub:
		trainer.push_to_hub(**_lowercase					)
	else:
		trainer.create_model_card(**_lowercase					)


if __name__ == "__main__":
		main()









import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
       import os

       # The slow tests are often failing with OOM error on GPU
       # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
       # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
       snake_case__					:							Tuple													=     'platform'

       import jax
       import jax.numpy as jnp

       from transformers import BlenderbotTokenizer
       from transformers.models.blenderbot.modeling_flax_blenderbot import (
           FlaxBlenderbotForConditionalGeneration,
           FlaxBlenderbotModel,
           shift_tokens_right,
       )



def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






      if attention_mask is None:
            __A          =				np.where(input_ids != config.pad_token_id					,				1					,				0 )
      if decoder_attention_mask is None:
            __A          =				np.where(decoder_input_ids != config.pad_token_id					,				1					,				0 )
      if head_mask is None:
            __A          =				np.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
            __A          =				np.ones((config.decoder_layers, config.decoder_attention_heads) )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }





class A_					:




   def __init__(self	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __A          =				parent
         __A          =				batch_size
         __A          =				seq_length
         __A          =				is_training
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				eos_token_id
         __A          =				pad_token_id
         __A          =				bos_token_id
         __A          =				initializer_range




   def _lowerCAmelCase      (self	:Optional[int] )->					Union[str, Any]:
         __A          =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,   self.vocab_size )    ,   3    ,   self.vocab_size )
         __A          =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,   dtype=np.intaa ))    ,   -1 )

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=self.hidden_size    ,   encoder_layers=self.num_hidden_layers    ,   decoder_layers=self.num_hidden_layers    ,   encoder_attention_heads=self.num_attention_heads    ,   decoder_attention_heads=self.num_attention_heads    ,   encoder_ffn_dim=self.intermediate_size    ,   decoder_ffn_dim=self.intermediate_size    ,   dropout=self.hidden_dropout_prob    ,   attention_dropout=self.attention_probs_dropout_prob    ,   max_position_embeddings=self.max_position_embeddings    ,   eos_token_id=self.eos_token_id    ,   bos_token_id=self.bos_token_id    ,   pad_token_id=self.pad_token_id    ,   initializer_range=self.initializer_range    ,   use_cache=_UpperCamelCase    ,   )
         __A          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,   dtype='''i4''' )

         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )
         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
         self.parent.assertTrue(diff < 1e-3    ,   msg=f"""Max diff is {diff}""" )






   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

         __A          =				model.encode(inputs_dict['''input_ids'''] )

         __A   ,						__A          =				(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         __A          =				jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
             ]    ,   axis=-1    ,   )

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )

         __A          =				model.decode(
             decoder_input_ids[:, :-1]    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   past_key_values=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )
         __A          =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,   dtype='''i4''' )
         __A          =				model.decode(
             decoder_input_ids[:, -1:]    ,   _UpperCamelCase    ,   past_key_values=outputs_cache.past_key_values    ,   decoder_attention_mask=_UpperCamelCase    ,   decoder_position_ids=_UpperCamelCase    ,   )

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
         self.parent.assertTrue(diff < 1e-3    ,   msg=f"""Max diff is {diff}""" )





@require_flax
class A_					(			unittest.TestCase   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]    ,   dtype=np.intaa    ,   )

         __A          =				input_ids.shape[0]
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=24    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=32    ,   decoder_ffn_dim=32    ,   max_position_embeddings=48    ,   eos_token_id=2    ,   pad_token_id=1    ,   bos_token_id=0    ,   )
         return config, input_ids, batch_size




   def _lowerCAmelCase      (self	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:int )->					Dict:
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=14    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=8    ,   decoder_ffn_dim=8    ,   max_position_embeddings=48    ,   )
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,   dtype=np.intaa )
         __A          =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,   dtype=np.intaa )
         __A          =				lm_model(input_ids=_UpperCamelCase    ,   decoder_input_ids=_UpperCamelCase )
         __A          =				(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         self.assertEqual(shifted.shape    ,   input_ids.shape )
         self.assertEqual(_UpperCamelCase    ,   n_pad_before - 1 )
         self.assertTrue(np.equal(shifted[:, 0]    ,   2 ).all() )





@require_flax
class A_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

                     @jax.jit
                     def encode_jitted(_UpperCamelCase	:int    ,   _UpperCamelCase	:int=None    ,   **_UpperCamelCase	:Dict ):
                           return model.encode(input_ids=_UpperCamelCase    ,   attention_mask=_UpperCamelCase )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     self.assertEqual(len(_UpperCamelCase )    ,   len(_UpperCamelCase ) )
                     for jitted_output, output in zip(_UpperCamelCase    ,   _UpperCamelCase ):
                           self.assertEqual(jitted_output.shape    ,   output.shape )




   def _lowerCAmelCase      (self	:List[str] )->					List[Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				model_class(_UpperCamelCase )
                     __A          =				model.encode(inputs_dict['''input_ids''']    ,   inputs_dict['''attention_mask'''] )

                     __A          =				{
                         '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                         '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                         '''encoder_outputs''': encoder_outputs,
                     }

                     @jax.jit
                     def decode_jitted(_UpperCamelCase	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     self.assertEqual(len(_UpperCamelCase )    ,   len(_UpperCamelCase ) )
                     for jitted_output, output in zip(_UpperCamelCase    ,   _UpperCamelCase ):
                           self.assertEqual(jitted_output.shape    ,   output.shape )




   @slow
   def _lowerCAmelCase      (self	:int )->					Optional[int]:
         for model_class_name in self.all_model_classes:
               __A          =				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __A          =				np.ones((1, 1) ) * model.config.eos_token_id
               __A          =				model(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )






   @unittest.skipUnless(jax_device != '''cpu'''    ,   '''3B test too slow on CPU.''' )
   @slow
   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A          =				{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __A          =				{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __A          =				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,   from_pt=_UpperCamelCase )
         __A          =				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

         __A          =				model.generate(**_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				'''Sam is a great name. It means "sun" in Gaelic.'''

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





