




"""simple docstring"""



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"):
      SCREAMING_SNAKE_CASE							=			True
      from torch.cuda.amp import autocast


SCREAMING_SNAKE_CASE							=			logging.getLogger(__name__)




@dataclass
class       UpperCAmelCase_    :
    lowercase__		   = field(
        metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},  )
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}				)
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Whether to log verbose messages or not.'''},  )







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						str:
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,					datefmt="%m/%d/%Y %H:%M:%S"     ,					handlers=[logging.StreamHandler(sys.stdout		)]     ,					)
 A__			=							logging.WARNING
 if model_args.verbose_logging:
  A__			=							logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank		):
  A__			=							logging.INFO
 logger.setLevel(SCREAMING_SNAKE_CASE_		)




@dataclass
class       UpperCAmelCase_    :
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}				)
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
    lowercase__		   = field(
        default='''train''',  metadata={
            '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
        },  )
    lowercase__		   = field(
        default='''validation''',  metadata={
            '''help''': (
                '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
            )
        },  )
    lowercase__		   = field(
        default='''text''',  metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''},  )
    lowercase__		   = field(
        default='''file''',  metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''},  )
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''},  )
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''},  )
    lowercase__		   = field(
        default='''librispeech''',  metadata={
            '''help''': (
                '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
                ''' \'buckwalter\'.'''
            )
        },  )
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}				)
    lowercase__		   = field(
        default=_A,  metadata={'''help''': '''The number of processes to use for the preprocessing.'''},  )






@dataclass
class       UpperCAmelCase_    :
    lowercase__		   = False
    lowercase__		   = None
    lowercase__		   = "|"
    lowercase__		   = field(default_factory=_A				)
    lowercase__		   = field(default_factory=_A				)
    lowercase__		   = None




    @classmethod
    def 				__magic_name__				(       cls		:			Dict      ,					snake_case_		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     if name == "librispeech":
      return cls()
     if name == "timit":
      return cls(
          do_lower_case=__lowercase      ,					translation_table=str.maketrans({"-": " "} )      ,					)
     if name == "buckwalter":
      A__			=							{
          '''-''': ''' ''',  # sometimes used to represent pauses
          '''^''': '''v''',  # fixing "tha" in arabic_speech_corpus dataset
      }
      return cls(
          vocab_file=pathlib.Path(__lowercase ).parent.joinpath("vocab/buckwalter.json" )      ,					word_delimiter_token="/"      ,					translation_table=str.maketrans(__lowercase )      ,					words_to_remove={"sil"}      ,					untransliterator=arabic.buckwalter.untransliterate      ,					)
     raise ValueError(F"""Unsupported orthography: '{name}'.""" )




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			List[Any] ) ->				str:




     '''simple docstring'''

     if len(self.translation_table ) > 0:
      A__			=							text.translate(self.translation_table )
     if len(self.words_to_remove ) == 0:
      A__			=							''' '''.join(text.split() )  # clean up whitespaces
     else:
      A__			=							''' '''.join(w for w in text.split() if w not in self.words_to_remove )  # and clean up whilespaces
     return text




    def 				__magic_name__				(       self		:			Tuple      ,					snake_case_		:			Tuple ) ->				WavaVecaProcessor:




     '''simple docstring'''

     A__			=							WavaVecaFeatureExtractor.from_pretrained(
         model_args.model_name_or_path      ,					cache_dir=model_args.cache_dir )
     if self.vocab_file:
      A__			=							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:
      A__			=							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(__lowercase      ,					__lowercase )






@dataclass
class       UpperCAmelCase_    :
    lowercase__		   = 42
    lowercase__		   = True
    lowercase__		   = None
    lowercase__		   = None
    lowercase__		   = None
    lowercase__		   = None




    def __call__(       self		:			List[str]      ,					snake_case_		:			Tuple ) ->				Dict[str, torch.Tensor]:




     '''simple docstring'''

     A__			=							[{'''input_values''': feature['''input_values''']} for feature in features]
     A__			=							[{'''input_ids''': feature['''labels''']} for feature in features]

     A__			=							self.processor.pad(
         __lowercase      ,					padding=self.padding      ,					max_length=self.max_length      ,					pad_to_multiple_of=self.pad_to_multiple_of      ,					return_tensors="pt"      ,					)
     A__			=							self.processor.pad(
         labels=__lowercase      ,					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
     A__			=							labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 )      ,					-100 )

     A__			=							labels

     return batch






class       UpperCAmelCase_    (		_A				):




    def 				__magic_name__				(       self		:			str      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			int ) ->				torch.Tensor:




     '''simple docstring'''

     model.train()
     A__			=							self._prepare_inputs(__lowercase )

     if self.use_amp:
      with autocast():
       A__			=							self.compute_loss(__lowercase      ,					__lowercase )
     else:
      A__			=							self.compute_loss(__lowercase      ,					__lowercase )

     if self.args.n_gpu > 1:
      if model.module.config.ctc_loss_reduction == "mean":
       A__			=							loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       A__			=							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:
      A__			=							loss / self.args.gradient_accumulation_steps

     if self.use_amp:
      self.scaler.scale(__lowercase ).backward()
     elif self.use_apex:
      with amp.scale_loss(__lowercase      ,					self.optimizer ) as scaled_loss:
       scaled_loss.backward()
     elif self.deepspeed:
      self.deepspeed.backward(__lowercase )
     else:
      loss.backward()

     return loss.detach()







def     _SCREAMING_SNAKE_CASE  (				)		->						Union[str, Any]:
 # 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.

 A__			=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

 A__			=							parser.parse_args_into_dataclasses()
 configure_logger(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_		)

 A__			=							Orthography.from_name(data_args.orthography.lower()		)
 A__			=							orthography.create_processor(SCREAMING_SNAKE_CASE_		)
 A__			=							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		)     ,					)

 A__			=							datasets.load_dataset(
     data_args.dataset_name     ,					data_args.dataset_config_name     ,					split=data_args.train_split_name		)
 A__			=							datasets.load_dataset(
     data_args.dataset_name     ,					data_args.dataset_config_name     ,					split=data_args.validation_split_name		)

 A__			=							datasets.load_metric("wer"		)
 A__			=							processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
 A__			=							''''''.join(t for t in processor.tokenizer.get_vocab().keys() if len(SCREAMING_SNAKE_CASE_		) == 1		)
 A__			=							re.compile(  # remove characters not in vocabulary
     Rf"""[^\s{re.escape(SCREAMING_SNAKE_CASE_		)}]"""     ,					flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0     ,					)
 A__			=							[]

 def prepare_example(lowercase_		):  # TODO(elgeish) make use of multiprocessing?
  A__			=							librosa.load(example[data_args.speech_file_column]     ,					sr=SCREAMING_SNAKE_CASE_		)
  if data_args.max_duration_in_seconds is not None:
   A__			=							len(example["speech"]		) / example['''sampling_rate''']
  # Normalize and clean up text; order matters!
  A__			=							orthography.preprocess_for_training(example[data_args.target_text_column]		)
  A__			=							vocabulary_text_cleaner.sub(""     ,					SCREAMING_SNAKE_CASE_		)
  if updated_text != example[data_args.target_text_column]:
   text_updates.append((example[data_args.target_text_column], updated_text)		)
   A__			=							updated_text
  return example

 A__			=							train_dataset.map(SCREAMING_SNAKE_CASE_     ,					remove_columns=[data_args.speech_file_column]		)
 A__			=							val_dataset.map(SCREAMING_SNAKE_CASE_     ,					remove_columns=[data_args.speech_file_column]		)

 if data_args.max_duration_in_seconds is not None:

  def filter_by_max_duration(lowercase_		):
   return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

  A__			=							len(SCREAMING_SNAKE_CASE_		)
  A__			=							len(SCREAMING_SNAKE_CASE_		)
  A__			=							train_dataset.filter(SCREAMING_SNAKE_CASE_     ,					remove_columns=["duration_in_seconds"]		)
  A__			=							val_dataset.filter(SCREAMING_SNAKE_CASE_     ,					remove_columns=["duration_in_seconds"]		)
  if len(SCREAMING_SNAKE_CASE_		) > old_train_size:
   logger.warning(
       f"""Filtered out {len(SCREAMING_SNAKE_CASE_		) - old_train_size} train example(s) longer than"""
       f""" {data_args.max_duration_in_seconds} second(s)."""		)
  if len(SCREAMING_SNAKE_CASE_		) > old_val_size:
   logger.warning(
       f"""Filtered out {len(SCREAMING_SNAKE_CASE_		) - old_val_size} validation example(s) longer than"""
       f""" {data_args.max_duration_in_seconds} second(s)."""		)
 logger.info(f"""Split sizes: {len(SCREAMING_SNAKE_CASE_		)} train and {len(SCREAMING_SNAKE_CASE_		)} validation."""		)

 logger.warning(f"""Updated {len(SCREAMING_SNAKE_CASE_		)} 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}\""""		)
 A__			=							None

 def prepare_dataset(lowercase_		):
  # 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}."""

  A__			=							processor(
      audio=batch["speech"]     ,					text=batch[data_args.target_text_column]     ,					sampling_rate=batch["sampling_rate"][0]		)
  batch.update(SCREAMING_SNAKE_CASE_		)
  return batch

 A__			=							train_dataset.map(
     SCREAMING_SNAKE_CASE_     ,					batch_size=training_args.per_device_train_batch_size     ,					batched=SCREAMING_SNAKE_CASE_     ,					num_proc=data_args.preprocessing_num_workers     ,					)
 A__			=							val_dataset.map(
     SCREAMING_SNAKE_CASE_     ,					batch_size=training_args.per_device_train_batch_size     ,					batched=SCREAMING_SNAKE_CASE_     ,					num_proc=data_args.preprocessing_num_workers     ,					)

 A__			=							DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE_     ,					padding=SCREAMING_SNAKE_CASE_		)

 def compute_metrics(lowercase_		):
  A__			=							pred.predictions
  A__			=							np.argmax(SCREAMING_SNAKE_CASE_     ,					axis=-1		)

  A__			=							processor.tokenizer.pad_token_id

  A__			=							processor.batch_decode(SCREAMING_SNAKE_CASE_		)
  # we do not want to group tokens when computing the metrics
  A__			=							processor.batch_decode(pred.label_ids     ,					group_tokens=SCREAMING_SNAKE_CASE_		)
  if logger.isEnabledFor(logging.DEBUG		):
   for reference, predicted in zip(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_		):
    logger.debug(f"""reference: \"{reference}\""""		)
    logger.debug(f"""predicted: \"{predicted}\""""		)
    if orthography.untransliterator is not None:
     logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(SCREAMING_SNAKE_CASE_		)}\""""		)
     logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(SCREAMING_SNAKE_CASE_		)}\""""		)

  A__			=							wer_metric.compute(predictions=SCREAMING_SNAKE_CASE_     ,					references=SCREAMING_SNAKE_CASE_		)

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

 A__			=							CTCTrainer(
     model=SCREAMING_SNAKE_CASE_     ,					data_collator=SCREAMING_SNAKE_CASE_     ,					args=SCREAMING_SNAKE_CASE_     ,					compute_metrics=SCREAMING_SNAKE_CASE_     ,					train_dataset=SCREAMING_SNAKE_CASE_     ,					eval_dataset=SCREAMING_SNAKE_CASE_     ,					tokenizer=processor.feature_extractor     ,					)

 trainer.train()


if __name__ == "__main__":
      main()












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 							snake_case_		:







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_4      ,  __lowercase=7      ,  __lowercase=True      ,  __lowercase=True      ,  __lowercase=False      ,  __lowercase=True      ,  __lowercase=9_9      ,  __lowercase=3_2      ,  __lowercase=4      ,  __lowercase=4      ,  __lowercase=[[["global", "local"], 2]]      ,  __lowercase=3_7      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=7      ,  __lowercase=0.0_2      ,  )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=parent
				lowerCamelCase :			Optional[int]    		=batch_size
				lowerCamelCase :			Any    		=seq_length
				lowerCamelCase :			Optional[Any]    		=is_training
				lowerCamelCase :			Any    		=use_input_mask
				lowerCamelCase :			int    		=use_token_type_ids
				lowerCamelCase :			int    		=use_labels
				lowerCamelCase :			str    		=vocab_size
				lowerCamelCase :			Optional[int]    		=hidden_size
				lowerCamelCase :			int    		=num_hidden_layers
				lowerCamelCase :			List[Any]    		=num_attention_heads
				lowerCamelCase :			Any    		=attention_types
				lowerCamelCase :			List[str]    		=intermediate_size
				lowerCamelCase :			Dict    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			Optional[int]    		=attention_probs_dropout_prob
				lowerCamelCase :			Any    		=max_position_embeddings
				lowerCamelCase :			int    		=window_size
				lowerCamelCase :			List[Any]    		=initializer_range
				lowerCamelCase :			Any    		=None
				lowerCamelCase :			List[Any]    		=vocab_size - 1
				lowerCamelCase :			str    		=vocab_size - 1
				lowerCamelCase :			int    		=vocab_size - 1







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			List[Any]    		=ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

				lowerCamelCase :			Tuple    		=None
				if self.use_input_mask:
						lowerCamelCase :			Union[str, Any]    		=random_attention_mask([self.batch_size, self.seq_length] )

				lowerCamelCase :			List[Any]    		=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=__lowercase      ,  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       __lowercase (		self )					-> Any:
				lowerCamelCase :			List[Any]    		=self.prepare_config_and_inputs()
				lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[Any]    		=config_and_inputs
				lowerCamelCase :			List[str]    		={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				lowerCamelCase :			Tuple    		=2_0
				lowerCamelCase :			Union[str, Any]    		=model_class_name(__lowercase )

				lowerCamelCase :			List[Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			str    		=jnp.ones((input_ids.shape[0], max_decoder_length)      ,  dtype='''i4''' )

				lowerCamelCase :			Optional[int]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
				lowerCamelCase :			str    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			List[str]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			List[Any]    		=model(
				    input_ids[:, -1:]      ,  attention_mask=__lowercase      ,  past_key_values=outputs_cache.past_key_values      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Any    		=model(__lowercase )

				lowerCamelCase :			Dict    		=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       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Dict:
				lowerCamelCase :			str    		=2_0
				lowerCamelCase :			Optional[int]    		=model_class_name(__lowercase )

				lowerCamelCase :			int    		=jnp.concatenate(
				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]      ,  axis=-1      ,  )

				lowerCamelCase :			Union[str, Any]    		=model.init_cache(input_ids.shape[0]      ,  __lowercase )
				lowerCamelCase :			List[Any]    		=jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

				lowerCamelCase :			Optional[Any]    		=model(
				    input_ids[:, :-1]      ,  attention_mask=__lowercase      ,  past_key_values=__lowercase      ,  position_ids=__lowercase      ,  )
				lowerCamelCase :			List[Any]    		=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='''i4''' )
				lowerCamelCase :			Union[str, Any]    		=model(
				    input_ids[:, -1:]      ,  past_key_values=outputs_cache.past_key_values      ,  attention_mask=__lowercase      ,  position_ids=__lowercase      ,  )

				lowerCamelCase :			Union[str, Any]    		=model(__lowercase      ,  attention_mask=__lowercase )

				lowerCamelCase :			Dict    		=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 							snake_case_		(      _A    ,       _A    ,       unittest.TestCase):
		lowerCamelCase     :List[Any]							=  (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
		lowerCamelCase     :Union[str, Any]							=  (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







		def       __lowercase (		self )					-> Union[str, Any]:
				lowerCamelCase :			str    		=FlaxGPTNeoModelTester(self )







		def       __lowercase (		self )					-> Dict:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Dict    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(__lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Tuple:
				for model_class_name in self.all_model_classes:
						lowerCamelCase		, lowerCamelCase		, lowerCamelCase :			Optional[int]    		=self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase )







		@slow
		def       __lowercase (		self )					-> Optional[int]:
				lowerCamelCase :			Tuple    		=GPTaTokenizer.from_pretrained('''gpt2'''      ,  pad_token='''<|endoftext|>'''      ,  padding_side='''left''' )
				lowerCamelCase :			Any    		=tokenizer(['''Hello this is a long string''', '''Hey''']      ,  return_tensors='''np'''      ,  padding=__lowercase      ,  truncation=__lowercase )

				lowerCamelCase :			List[Any]    		=FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
				lowerCamelCase :			Union[str, Any]    		=False
				lowerCamelCase :			Optional[int]    		=model.config.eos_token_id

				lowerCamelCase :			str    		=jax.jit(model.generate )

				lowerCamelCase :			Optional[int]    		=jit_generate(
				    inputs['''input_ids''']      ,  attention_mask=inputs['''attention_mask''']      ,  pad_token_id=tokenizer.pad_token_id ).sequences

				lowerCamelCase :			Any    		=tokenizer.batch_decode(__lowercase      ,  skip_special_tokens=__lowercase )

				lowerCamelCase :			str    		=[
				    '''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(__lowercase      ,  __lowercase )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> int:
				lowerCamelCase		, lowerCamelCase :			int    		=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
								lowerCamelCase :			Optional[Any]    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Optional[Any]    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase :			Any    		=model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase :			Optional[int]    		=getattr(__lowercase      ,  __lowercase )

								lowerCamelCase		, lowerCamelCase :			Union[str, Any]    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			Dict    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Optional[Any]    		=1
										lowerCamelCase :			Tuple    		=0
										lowerCamelCase :			Union[str, Any]    		=1
								lowerCamelCase :			Optional[int]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Any    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Union[str, Any]    		=convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  __lowercase )
								lowerCamelCase :			Optional[int]    		=fx_state

								with torch.no_grad():
										lowerCamelCase :			List[Any]    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			List[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										pt_model.save_pretrained(__lowercase )
										lowerCamelCase :			List[str]    		=model_class.from_pretrained(__lowercase      ,  from_pt=__lowercase )

								lowerCamelCase :			List[str]    		=fx_model_loaded(**__lowercase ).to_tuple()
								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output_loaded, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output_loaded[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@is_pt_flax_cross_test
		def       __lowercase (		self )					-> str:
				lowerCamelCase		, lowerCamelCase :			Dict    		=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
								lowerCamelCase :			Tuple    		=self._prepare_for_class(__lowercase      ,  __lowercase )
								lowerCamelCase :			Any    		={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

								# load corresponding PyTorch class
								lowerCamelCase :			Optional[Any]    		=model_class.__name__[4:]  # Skip the "Flax" at the beginning
								lowerCamelCase :			List[str]    		=getattr(__lowercase      ,  __lowercase )

								lowerCamelCase :			List[str]    		=pt_model_class(__lowercase ).eval()
								lowerCamelCase :			Union[str, Any]    		=model_class(__lowercase      ,  dtype=jnp.floataa )

								lowerCamelCase :			Dict    		=load_flax_weights_in_pytorch_model(__lowercase      ,  fx_model.params )
								lowerCamelCase		, lowerCamelCase :			Any    		=pt_inputs['''input_ids'''].shape
								lowerCamelCase :			str    		=np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
								for batch_idx, start_index in enumerate(__lowercase ):
										lowerCamelCase :			List[str]    		=0
										lowerCamelCase :			Union[str, Any]    		=1
										lowerCamelCase :			Optional[int]    		=0
										lowerCamelCase :			Dict    		=1

								# make sure weights are tied in PyTorch
								pt_model.tie_weights()

								with torch.no_grad():
										lowerCamelCase :			Tuple    		=pt_model(**__lowercase ).to_tuple()

								lowerCamelCase :			Optional[Any]    		=fx_model(**__lowercase ).to_tuple()
								self.assertEqual(len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )

								with tempfile.TemporaryDirectory() as tmpdirname:
										fx_model.save_pretrained(__lowercase )
										lowerCamelCase :			Union[str, Any]    		=pt_model_class.from_pretrained(__lowercase      ,  from_flax=__lowercase )

								with torch.no_grad():
										lowerCamelCase :			str    		=pt_model_loaded(**__lowercase ).to_tuple()

								self.assertEqual(
								    len(__lowercase )      ,  len(__lowercase )      ,  '''Output lengths differ between Flax and PyTorch''' )
								for fx_output, pt_output in zip(__lowercase      ,  __lowercase ):
										self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4e-2 )







		@slow
		def       __lowercase (		self )					-> Any:
				for model_class_name in self.all_model_classes:
						lowerCamelCase :			Optional[Any]    		=model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
						lowerCamelCase :			Union[str, Any]    		=model(np.ones((1, 1) ) )
						self.assertIsNotNone(__lowercase )

