code: 
  
  
  
  import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  
  import datasets
  import librosa
  import numpy as np
  import torch
  from lang_trans import arabic
  from packaging import version
  from torch import nn
  
  from transformers import (
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaForCTC,
      WavaVecaProcessor,
      is_apex_available,
      trainer_utils,
  )
  
  
  if is_apex_available():
  	from apex import amp
  
  if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
  	__A       					=		True
  	from torch.cuda.amp import autocast
  
  
  __A       					=		logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      str	     =							field(
  	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
  	a__   :      Optional[str]	     =							field(
  	    default=snake_case__				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)
  	a__   :      Optional[bool]	     =							field(
  	    default=snake_case__				,					metadata={"help": "Whether to freeze the feature extractor layers of the model."})
  	a__   :      Optional[bool]	     =							field(
  	    default=snake_case__				,					metadata={"help": "Whether to log verbose messages or not."}				,					)
  
  
  
  def  __a  (					lowerCAmelCase_     :      ModelArguments    ,lowerCAmelCase_     :      TrainingArguments       )   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						logging.basicConfig(
  						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,datefmt="""%m/%d/%Y %H:%M:%S"""    ,handlers=[logging.StreamHandler(sys.stdout       )]    ,)
  						UpperCAmelCase_=     logging.WARNING
  						if model_args.verbose_logging:
  												UpperCAmelCase_=     logging.DEBUG
  						elif trainer_utils.is_main_process(training_args.local_rank       ):
  												UpperCAmelCase_=     logging.INFO
  						logger.setLevel(lowerCAmelCase_       )
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      str	     =							field(
  	    default=snake_case__				,					metadata={"help": "The name of the dataset to use (via the datasets library)."})
  	a__   :      Optional[str]	     =							field(
  	    default=snake_case__				,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
  	a__   :      Optional[str]	     =							field(
  	    default="train"				,					metadata={
  	        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
  	    }				,					)
  	a__   :      Optional[str]	     =							field(
  	    default="validation"				,					metadata={
  	        "help": (
  	            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
  	        )
  	    }				,					)
  	a__   :      Optional[str]	     =							field(
  	    default="text"				,					metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"}				,					)
  	a__   :      Optional[str]	     =							field(
  	    default="file"				,					metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}				,					)
  	a__   :      Optional[bool]	     =							field(
  	    default=snake_case__				,					metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."}				,					)
  	a__   :      Optional[float]	     =							field(
  	    default=snake_case__				,					metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."}				,					)
  	a__   :      Optional[str]	     =							field(
  	    default="librispeech"				,					metadata={
  	        "help": (
  	            "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
  	            " 'buckwalter'."
  	        )
  	    }				,					)
  	a__   :      bool	     =							field(
  	    default=snake_case__				,					metadata={"help": "Overwrite the cached preprocessed datasets or not."})
  	a__   :      Optional[int]	     =							field(
  	    default=snake_case__				,					metadata={"help": "The number of processes to use for the preprocessing."}				,					)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      bool	     =							False
  	a__   :      Optional[str]	     =							None
  	a__   :      Optional[str]	     =							"|"
  	a__   :      Optional[Dict[str, str]]	     =							field(default_factory=snake_case__)
  	a__   :      Optional[Set[str]]	     =							field(default_factory=snake_case__)
  	a__   :      Optional[Callable[[str], str]]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		str  )    ->				Dict:
  							if name == "librispeech":
  													return cls()
  							if name == "timit":
  													return cls(
  													    do_lower_case=__UpperCAmelCase		,						translation_table=str.maketrans({"""-""": """ """}  )		,						)
  							if name == "buckwalter":
  													UpperCAmelCase_=     {
  													    """-""": """ """,  # sometimes used to represent pauses
  													    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
  													}
  													return cls(
  													    vocab_file=pathlib.Path(__UpperCAmelCase  ).parent.joinpath("""vocab/buckwalter.json"""  )		,						word_delimiter_token="""/"""		,						translation_table=str.maketrans(__UpperCAmelCase  )		,						words_to_remove={"""sil"""}		,						untransliterator=arabic.buckwalter.untransliterate		,						)
  							raise ValueError(F"""Unsupported orthography: '{name}'."""  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		str  )    ->				str:
  							# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
  							if len(self.translation_table  ) > 0:
  													UpperCAmelCase_=     text.translate(self.translation_table  )
  							if len(self.words_to_remove  ) == 0:
  													UpperCAmelCase_=     """ """.join(text.split()  )  # clean up whitespaces
  							else:
  													UpperCAmelCase_=     """ """.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
  							return text
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		ModelArguments  )    ->				WavaVecaProcessor:
  							UpperCAmelCase_=     WavaVecaFeatureExtractor.from_pretrained(
  							    model_args.model_name_or_path		,						cache_dir=model_args.cache_dir  )
  							if self.vocab_file:
  													UpperCAmelCase_=     WavaVecaCTCTokenizer(
  													    self.vocab_file		,						cache_dir=model_args.cache_dir		,						do_lower_case=self.do_lower_case		,						word_delimiter_token=self.word_delimiter_token		,						)
  							else:
  													UpperCAmelCase_=     WavaVecaCTCTokenizer.from_pretrained(
  													    model_args.model_name_or_path		,						cache_dir=model_args.cache_dir		,						do_lower_case=self.do_lower_case		,						word_delimiter_token=self.word_delimiter_token		,						)
  							return WavaVecaProcessor(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      WavaVecaProcessor
  	a__   :      Union[bool, str]	     =							True
  	a__   :      Optional[int]	     =							None
  	a__   :      Optional[int]	     =							None
  	a__   :      Optional[int]	     =							None
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	def __call__(			self			:		List[str]		,						__UpperCAmelCase			:		List[Dict[str, Union[List[int], torch.Tensor]]]  )    ->				Dict[str, torch.Tensor]:
  							# split inputs and labels since they have to be of different lenghts and need
  							# different padding methods
  							UpperCAmelCase_=     [{"""input_values""": feature["""input_values"""]} for feature in features]
  							UpperCAmelCase_=     [{"""input_ids""": feature["""labels"""]} for feature in features]
  
  							UpperCAmelCase_=     self.processor.pad(
  							    __UpperCAmelCase		,						padding=self.padding		,						max_length=self.max_length		,						pad_to_multiple_of=self.pad_to_multiple_of		,						return_tensors="""pt"""		,						)
  							UpperCAmelCase_=     self.processor.pad(
  							    labels=__UpperCAmelCase		,						padding=self.padding		,						max_length=self.max_length_labels		,						pad_to_multiple_of=self.pad_to_multiple_of_labels		,						return_tensors="""pt"""		,						)
  
  							# replace padding with -100 to ignore loss correctly
  							UpperCAmelCase_=     labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1  )		,						-100  )
  
  							UpperCAmelCase_=     labels
  
  							return batch
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		nn.Module		,						__UpperCAmelCase			:		Dict[str, Union[torch.Tensor, Any]]  )    ->				torch.Tensor:
  							model.train()
  							UpperCAmelCase_=     self._prepare_inputs(__UpperCAmelCase  )
  
  							if self.use_amp:
  													with autocast():
  																			UpperCAmelCase_=     self.compute_loss(__UpperCAmelCase		,						__UpperCAmelCase  )
  							else:
  													UpperCAmelCase_=     self.compute_loss(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							if self.args.n_gpu > 1:
  													if model.module.config.ctc_loss_reduction == "mean":
  																			UpperCAmelCase_=     loss.mean()
  													elif model.module.config.ctc_loss_reduction == "sum":
  																			UpperCAmelCase_=     loss.sum() / (inputs["""labels"""] >= 0).sum()
  													else:
  																			raise ValueError(F"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""  )
  
  							if self.args.gradient_accumulation_steps > 1:
  													UpperCAmelCase_=     loss / self.args.gradient_accumulation_steps
  
  							if self.use_amp:
  													self.scaler.scale(__UpperCAmelCase  ).backward()
  							elif self.use_apex:
  													with amp.scale_loss(__UpperCAmelCase		,						self.optimizer  ) as scaled_loss:
  																			scaled_loss.backward()
  							elif self.deepspeed:
  													self.deepspeed.backward(__UpperCAmelCase  )
  							else:
  													loss.backward()
  
  							return loss.detach()
  
  
  
  def  __a  (					)   ->				str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
  
  						UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     parser.parse_args_into_dataclasses()
  						configure_logger(lowerCAmelCase_    ,lowerCAmelCase_       )
  
  						UpperCAmelCase_=     Orthography.from_name(data_args.orthography.lower()       )
  						UpperCAmelCase_=     orthography.create_processor(lowerCAmelCase_       )
  						UpperCAmelCase_=     WavaVecaForCTC.from_pretrained(
  						    model_args.model_name_or_path    ,cache_dir=model_args.cache_dir    ,gradient_checkpointing=training_args.gradient_checkpointing    ,vocab_size=len(processor.tokenizer       )    ,)
  
  						UpperCAmelCase_=     datasets.load_dataset(
  						    data_args.dataset_name    ,data_args.dataset_config_name    ,split=data_args.train_split_name       )
  						UpperCAmelCase_=     datasets.load_dataset(
  						    data_args.dataset_name    ,data_args.dataset_config_name    ,split=data_args.validation_split_name       )
  
  						UpperCAmelCase_=     datasets.load_metric("""wer"""       )
  						UpperCAmelCase_=     processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  						UpperCAmelCase_=     """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowerCAmelCase_       ) == 1       )
  						UpperCAmelCase_=     re.compile(  # remove characters not in vocabulary
  						    rF"""[^\s{re.escape(lowerCAmelCase_       )}]"""    ,flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0    ,)
  						UpperCAmelCase_=     []
  
  						def prepare_example(lowerCAmelCase_     :      Dict       ):  # TODO(elgeish) make use of multiprocessing?
  												UpperCAmelCase_,     UpperCAmelCase_=     librosa.load(example[data_args.speech_file_column]    ,sr=lowerCAmelCase_       )
  												if data_args.max_duration_in_seconds is not None:
  																		UpperCAmelCase_=     len(example["""speech"""]       ) / example["""sampling_rate"""]
  												# Normalize and clean up text; order matters!
  												UpperCAmelCase_=     orthography.preprocess_for_training(example[data_args.target_text_column]       )
  												UpperCAmelCase_=     vocabulary_text_cleaner.sub(""""""    ,lowerCAmelCase_       )
  												if updated_text != example[data_args.target_text_column]:
  																		text_updates.append((example[data_args.target_text_column], updated_text)       )
  																		UpperCAmelCase_=     updated_text
  												return example
  
  						UpperCAmelCase_=     train_dataset.map(lowerCAmelCase_    ,remove_columns=[data_args.speech_file_column]       )
  						UpperCAmelCase_=     val_dataset.map(lowerCAmelCase_    ,remove_columns=[data_args.speech_file_column]       )
  
  						if data_args.max_duration_in_seconds is not None:
  
  												def filter_by_max_duration(lowerCAmelCase_     :      str       ):
  																		return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
  												UpperCAmelCase_=     len(lowerCAmelCase_       )
  												UpperCAmelCase_=     len(lowerCAmelCase_       )
  												UpperCAmelCase_=     train_dataset.filter(lowerCAmelCase_    ,remove_columns=["""duration_in_seconds"""]       )
  												UpperCAmelCase_=     val_dataset.filter(lowerCAmelCase_    ,remove_columns=["""duration_in_seconds"""]       )
  												if len(lowerCAmelCase_       ) > old_train_size:
  																		logger.warning(
  																		    F"""Filtered out {len(lowerCAmelCase_       ) - old_train_size} train example(s) longer than"""
  																		    F""" {data_args.max_duration_in_seconds} second(s)."""       )
  												if len(lowerCAmelCase_       ) > old_val_size:
  																		logger.warning(
  																		    F"""Filtered out {len(lowerCAmelCase_       ) - old_val_size} validation example(s) longer than"""
  																		    F""" {data_args.max_duration_in_seconds} second(s)."""       )
  						logger.info(F"""Split sizes: {len(lowerCAmelCase_       )} train and {len(lowerCAmelCase_       )} validation."""       )
  
  						logger.warning(F"""Updated {len(lowerCAmelCase_       )} transcript(s) using '{data_args.orthography}' orthography rules."""       )
  						if logger.isEnabledFor(logging.DEBUG       ):
  												for original_text, updated_text in text_updates:
  																		logger.debug(F"""Updated text: \"{original_text}\" -> \"{updated_text}\""""       )
  						UpperCAmelCase_=     None
  
  						def prepare_dataset(lowerCAmelCase_     :      List[str]       ):
  												# check that all files have the correct sampling rate
  												assert (
  												    len(set(batch["""sampling_rate"""]       )       ) == 1
  												), F"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""
  
  												UpperCAmelCase_=     processor(
  												    audio=batch["""speech"""]    ,text=batch[data_args.target_text_column]    ,sampling_rate=batch["""sampling_rate"""][0]       )
  												batch.update(lowerCAmelCase_       )
  												return batch
  
  						UpperCAmelCase_=     train_dataset.map(
  						    lowerCAmelCase_    ,batch_size=training_args.per_device_train_batch_size    ,batched=lowerCAmelCase_    ,num_proc=data_args.preprocessing_num_workers    ,)
  						UpperCAmelCase_=     val_dataset.map(
  						    lowerCAmelCase_    ,batch_size=training_args.per_device_train_batch_size    ,batched=lowerCAmelCase_    ,num_proc=data_args.preprocessing_num_workers    ,)
  
  						UpperCAmelCase_=     DataCollatorCTCWithPadding(processor=lowerCAmelCase_    ,padding=lowerCAmelCase_       )
  
  						def compute_metrics(lowerCAmelCase_     :      Dict       ):
  												UpperCAmelCase_=     pred.predictions
  												UpperCAmelCase_=     np.argmax(lowerCAmelCase_    ,axis=-1       )
  
  												UpperCAmelCase_=     processor.tokenizer.pad_token_id
  
  												UpperCAmelCase_=     processor.batch_decode(lowerCAmelCase_       )
  												# we do not want to group tokens when computing the metrics
  												UpperCAmelCase_=     processor.batch_decode(pred.label_ids    ,group_tokens=lowerCAmelCase_       )
  												if logger.isEnabledFor(logging.DEBUG       ):
  																		for reference, predicted in zip(lowerCAmelCase_    ,lowerCAmelCase_       ):
  																								logger.debug(F"""reference: \"{reference}\""""       )
  																								logger.debug(F"""predicted: \"{predicted}\""""       )
  																								if orthography.untransliterator is not None:
  																														logger.debug(F"""reference (untransliterated): \"{orthography.untransliterator(lowerCAmelCase_       )}\""""       )
  																														logger.debug(F"""predicted (untransliterated): \"{orthography.untransliterator(lowerCAmelCase_       )}\""""       )
  
  												UpperCAmelCase_=     wer_metric.compute(predictions=lowerCAmelCase_    ,references=lowerCAmelCase_       )
  
  												return {"wer": wer}
  
  						if model_args.freeze_feature_extractor:
  												model.freeze_feature_extractor()
  
  						UpperCAmelCase_=     CTCTrainer(
  						    model=lowerCAmelCase_    ,data_collator=lowerCAmelCase_    ,args=lowerCAmelCase_    ,compute_metrics=lowerCAmelCase_    ,train_dataset=lowerCAmelCase_    ,eval_dataset=lowerCAmelCase_    ,tokenizer=processor.feature_extractor    ,)
  
  						trainer.train()
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
style_context: 
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
  	import jax
  	import jax.numpy as jnp
  
  	from transformers.modeling_flax_pytorch_utils import (
  	    convert_pytorch_state_dict_to_flax,
  	    load_flax_weights_in_pytorch_model,
  	)
  	from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]=14		,						__UpperCAmelCase			:		Tuple=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Any=True		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		str=32		,						__UpperCAmelCase			:		Any=4		,						__UpperCAmelCase			:		Optional[Any]=4		,						__UpperCAmelCase			:		Union[str, Any]=[[["global", "local"], 2]]		,						__UpperCAmelCase			:		int=37		,						__UpperCAmelCase			:		str="gelu"		,						__UpperCAmelCase			:		int=0.1		,						__UpperCAmelCase			:		Tuple=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		List[str]=0.02		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_input_mask
  							UpperCAmelCase_=     use_token_type_ids
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     attention_types
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     window_size
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  							UpperCAmelCase_=     vocab_size - 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Optional[Any]:
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )
  
  							UpperCAmelCase_=     None
  							if self.use_input_mask:
  													UpperCAmelCase_=     random_attention_mask([self.batch_size, self.seq_length]  )
  
  							UpperCAmelCase_=     GPTNeoConfig(
  							    vocab_size=self.vocab_size		,						hidden_size=self.hidden_size		,						num_layers=self.num_hidden_layers		,						num_heads=self.num_attention_heads		,						max_position_embeddings=self.max_position_embeddings		,						use_cache=__UpperCAmelCase		,						bos_token_id=self.bos_token_id		,						eos_token_id=self.eos_token_id		,						pad_token_id=self.pad_token_id		,						window_size=self.window_size		,						attention_types=self.attention_types		,						)
  
  							return (config, input_ids, input_mask)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Dict:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
  							return config, inputs_dict
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				Dict:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.ones((input_ids.shape[0], max_decoder_length)		,						dtype="""i4"""  )
  
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						attention_mask=__UpperCAmelCase		,						past_key_values=outputs_cache.past_key_values		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Dict  )    ->				List[Any]:
  							UpperCAmelCase_=     20
  							UpperCAmelCase_=     model_class_name(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     jnp.concatenate(
  							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]		,						axis=-1		,						)
  
  							UpperCAmelCase_=     model.init_cache(input_ids.shape[0]		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1  )[None, :]		,						(input_ids.shape[0], input_ids.shape[-1] - 1)  )
  
  							UpperCAmelCase_=     model(
  							    input_ids[:, :-1]		,						attention_mask=__UpperCAmelCase		,						past_key_values=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,						dtype="""i4"""  )
  							UpperCAmelCase_=     model(
  							    input_ids[:, -1:]		,						past_key_values=outputs_cache.past_key_values		,						attention_mask=__UpperCAmelCase		,						position_ids=__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     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 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[Any]	     =							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  	a__   :      List[Any]	     =							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Any:
  							UpperCAmelCase_=     FlaxGPTNeoModelTester(self  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				str:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Any:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  													self.model_tester.check_use_cache_forward_with_attn_mask(
  													    __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     GPTaTokenizer.from_pretrained("""gpt2"""		,						pad_token="""<|endoftext|>"""		,						padding_side="""left"""  )
  							UpperCAmelCase_=     tokenizer(["""Hello this is a long string""", """Hey"""]		,						return_tensors="""np"""		,						padding=__UpperCAmelCase		,						truncation=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  							UpperCAmelCase_=     False
  							UpperCAmelCase_=     model.config.eos_token_id
  
  							UpperCAmelCase_=     jax.jit(model.generate  )
  
  							UpperCAmelCase_=     jit_generate(
  							    inputs["""input_ids"""]		,						attention_mask=inputs["""attention_mask"""]		,						pad_token_id=tokenizer.pad_token_id  ).sequences
  
  							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase		,						skip_special_tokens=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     [
  							    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
  							    """Hey, I'm a little late to the party. I'm going to""",
  							]
  
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	@is_pt_flax_cross_test
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Union[str, Any]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  
  							for model_class in self.all_model_classes:
  													with self.subTest(model_class.__name__  ):
  																			# prepare inputs
  																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     fx_state
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									pt_model.save_pretrained(__UpperCAmelCase  )
  																									UpperCAmelCase_=     model_class.from_pretrained(__UpperCAmelCase		,						from_pt=__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     fx_model_loaded(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(
  																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output_loaded, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output_loaded[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  
  
  
  	@is_pt_flax_cross_test
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Dict:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
  							for model_class in self.all_model_classes:
  													with self.subTest(model_class.__name__  ):
  																			# prepare inputs
  																			UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			UpperCAmelCase_=     {k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}
  
  																			# load corresponding PyTorch class
  																			UpperCAmelCase_=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
  																			UpperCAmelCase_=     getattr(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     pt_model_class(__UpperCAmelCase  ).eval()
  																			UpperCAmelCase_=     model_class(__UpperCAmelCase		,						dtype=jnp.floataa  )
  
  																			UpperCAmelCase_=     load_flax_weights_in_pytorch_model(__UpperCAmelCase		,						fx_model.params  )
  																			UpperCAmelCase_,     UpperCAmelCase_=     pt_inputs["""input_ids"""].shape
  																			UpperCAmelCase_=     np.random.randint(0		,						seq_length - 1		,						size=(batch_size,)  )
  																			for batch_idx, start_index in enumerate(__UpperCAmelCase  ):
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  																									UpperCAmelCase_=     0
  																									UpperCAmelCase_=     1
  
  																			# make sure weights are tied in PyTorch
  																			pt_model.tie_weights()
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model(**__UpperCAmelCase  ).to_tuple()
  
  																			UpperCAmelCase_=     fx_model(**__UpperCAmelCase  ).to_tuple()
  																			self.assertEqual(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									fx_model.save_pretrained(__UpperCAmelCase  )
  																									UpperCAmelCase_=     pt_model_class.from_pretrained(__UpperCAmelCase		,						from_flax=__UpperCAmelCase  )
  
  																			with torch.no_grad():
  																									UpperCAmelCase_=     pt_model_loaded(**__UpperCAmelCase  ).to_tuple()
  
  																			self.assertEqual(
  																			    len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )		,						"""Output lengths differ between Flax and PyTorch"""  )
  																			for fx_output, pt_output in zip(__UpperCAmelCase		,						__UpperCAmelCase  ):
  																									self.assert_almost_equals(fx_output[:, -1]		,						pt_output[:, -1].numpy()		,						4E-2  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Dict:
  							for model_class_name in self.all_model_classes:
  													UpperCAmelCase_=     model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""  )
  													UpperCAmelCase_=     model(np.ones((1, 1)  )  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  