

import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_      =     logging.getLogger(__name__)


a_      =     list(MODEL_WITH_LM_HEAD_MAPPING.keys())
a_      =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =field(
			    default=__A				,						metadata={
			        'help': (
			            'The model checkpoint for weights initialization. Leave None if you want to train a model from'
			            ' scratch.'
			        )
			    }				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__A )}				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,						)






@dataclass
class _UpperCamelCase				:
			'''simple docstring'''







			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'The input training data file (a text file).'} )
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={
			        'help': (
			            'The input training data files (multiple files in glob format). '
			            'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
			        )
			    }				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'}				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'}				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'}				,						)

			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
			lowerCamelCase__							     =field(default=__A				,						metadata={'help': 'Whether ot not to use whole word mask.'} )
			lowerCamelCase__							     =field(
			    default=0.15				,						metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
			lowerCamelCase__							     =field(
			    default=1 / 6				,						metadata={
			        'help': (
			            'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
			            ' modeling.'
			        )
			    }				,						)
			lowerCamelCase__							     =field(
			    default=5				,						metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )

			lowerCamelCase__							     =field(
			    default=-1				,						metadata={
			        'help': (
			            'Optional input sequence length after tokenization.'
			            'The training dataset will be truncated in block of this size for training.'
			            'Default to the model max input length for single sentence inputs (take into account special tokens).'
			        )
			    }				,						)
			lowerCamelCase__							     =field(
			    default=__A				,						metadata={'help': 'Overwrite the cached training and evaluation sets'} )

def 			lowerCamelCase__			(       _a				,      _a				,      _a = False				,      _a = None				,      ):
	def _dataset(_a				,      _a=None):
		if args.line_by_line:
			if ref_path is not None:
				if not args.whole_word_mask or not args.mlm:
					raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask")
				return LineByLineWithRefDataset(
				    tokenizer=_a				,      file_path=_a				,      block_size=args.block_size				,      ref_path=_a				,      )

			return LineByLineTextDataset(tokenizer=_a				,      file_path=_a				,      block_size=args.block_size)
		else:
			return TextDataset(
			    tokenizer=_a				,      file_path=_a				,      block_size=args.block_size				,      overwrite_cache=args.overwrite_cache				,      cache_dir=_a				,      )

	if evaluate:
		return _dataset(args.eval_data_file				,      args.eval_ref_file)
	elif args.train_data_files:
		return ConcatDataset([_dataset(_a) for f in glob(args.train_data_files)])
	else:
		return _dataset(args.train_data_file				,      args.train_ref_file)

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

	SCREAMING_SNAKE_CASE :   Dict					  =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							parser.parse_args_into_dataclasses()

	if data_args.eval_data_file is None and training_args.do_eval:
		raise ValueError(
		    "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
		    "or remove the --do_eval argument.")
	if (
	    os.path.exists(training_args.output_dir)
	    and os.listdir(training_args.output_dir)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
		    " --overwrite_output_dir to overcome.")

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,      datefmt="%m/%d/%Y %H:%M:%S"				,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,      )
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,      training_args.local_rank				,      training_args.device				,      training_args.n_gpu				,      bool(training_args.local_rank != -1)				,      training_args.fpaa				,      )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"				,      _a)

	# Set seed
	set_seed(training_args.seed)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	if model_args.config_name:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoConfig.from_pretrained(model_args.config_name				,      cache_dir=model_args.cache_dir)
	elif model_args.model_name_or_path:
		SCREAMING_SNAKE_CASE :   List[str]					  =							AutoConfig.from_pretrained(model_args.model_name_or_path				,      cache_dir=model_args.cache_dir)
	else:
		SCREAMING_SNAKE_CASE :   Dict					  =							CONFIG_MAPPING[model_args.model_type]()
		logger.warning("You are instantiating a new config instance from scratch.")

	if model_args.tokenizer_name:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							AutoTokenizer.from_pretrained(model_args.tokenizer_name				,      cache_dir=model_args.cache_dir)
	elif model_args.model_name_or_path:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							AutoTokenizer.from_pretrained(model_args.model_name_or_path				,      cache_dir=model_args.cache_dir)
	else:
		raise ValueError(
		    "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
		    " script, save it,and load it from here, using --tokenizer_name")

	if model_args.model_name_or_path:
		SCREAMING_SNAKE_CASE :   Tuple					  =							AutoModelWithLMHead.from_pretrained(
		    model_args.model_name_or_path				,      from_tf=bool(".ckpt" in model_args.model_name_or_path)				,      config=_a				,      cache_dir=model_args.cache_dir				,      )
	else:
		logger.info("Training new model from scratch")
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							AutoModelWithLMHead.from_config(_a)

	model.resize_token_embeddings(len(_a))

	if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
		raise ValueError(
		    "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
		    "--mlm flag (masked language modeling).")

	if data_args.block_size <= 0:
		SCREAMING_SNAKE_CASE :   str					  =							tokenizer.max_len
		# Our input block size will be the max possible for the model
	else:
		SCREAMING_SNAKE_CASE :   List[str]					  =							min(data_args.block_size				,      tokenizer.max_len)

	# Get datasets

	SCREAMING_SNAKE_CASE :   List[Any]					  =							(
	    get_dataset(_a				,      tokenizer=_a				,      cache_dir=model_args.cache_dir) if training_args.do_train else None
	)
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(
	    get_dataset(_a				,      tokenizer=_a				,      evaluate=_a				,      cache_dir=model_args.cache_dir)
	    if training_args.do_eval
	    else None
	)
	if config.model_type == "xlnet":
		SCREAMING_SNAKE_CASE :   Any					  =							DataCollatorForPermutationLanguageModeling(
		    tokenizer=_a				,      plm_probability=data_args.plm_probability				,      max_span_length=data_args.max_span_length				,      )
	else:
		if data_args.mlm and data_args.whole_word_mask:
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							DataCollatorForWholeWordMask(
			    tokenizer=_a				,      mlm_probability=data_args.mlm_probability)
		else:
			SCREAMING_SNAKE_CASE :   Any					  =							DataCollatorForLanguageModeling(
			    tokenizer=_a				,      mlm=data_args.mlm				,      mlm_probability=data_args.mlm_probability)

    # Initialize our Trainer
	SCREAMING_SNAKE_CASE :   str					  =							Trainer(
	    model=_a				,      args=_a				,      data_collator=_a				,      train_dataset=_a				,      eval_dataset=_a				,      prediction_loss_only=_a				,      )

	# Training
	if training_args.do_train:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							(
		    model_args.model_name_or_path
		    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path)
		    else None
		)
		trainer.train(model_path=_a)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
	SCREAMING_SNAKE_CASE :   int					  =							{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***")

		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							trainer.evaluate()

		SCREAMING_SNAKE_CASE :   Any					  =							math.exp(eval_output["eval_loss"])
		SCREAMING_SNAKE_CASE :   Any					  =							{"perplexity": perplexity}

		SCREAMING_SNAKE_CASE :   Dict					  =							os.path.join(training_args.output_dir				,      "eval_results_lm.txt")
		if trainer.is_world_master():
			with open(_a				,      "w") as writer:
				logger.info("***** Eval results *****")
				for key in sorted(result.keys()):
					logger.info("  %s = %s"				,      _a				,      str(result[key]))
					writer.write("%s = %s\n" % (key, str(result[key])))

		results.update(_a)

	return results

def 			lowerCamelCase__			(       _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()





'''simple docstring'''


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  Optional[Any]						,						lowercase__   :  Tuple						,						lowercase__   :  Any=14						,						lowercase__   :  int=7						,						lowercase__   :  int=True						,						lowercase__   :  List[str]=True						,						lowercase__   :  Tuple=False						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  Any=99						,						lowercase__   :  Dict=32						,						lowercase__   :  Tuple=4						,						lowercase__   :  List[str]=4						,						lowercase__   :  Optional[Any]=4						,						lowercase__   :  Union[str, Any]=37						,						lowercase__   :  Union[str, Any]="gelu"						,						lowercase__   :  List[Any]=0.1						,						lowercase__   :  Union[str, Any]=0.1						,						lowercase__   :  List[str]=512						,						lowercase__   :  int=0.02						,						):


           '''simple docstring'''

           a_     :							Optional[int]     						=  parent
           a_     :							str     						=  batch_size
           a_     :							Dict     						=  seq_length
           a_     :							Tuple     						=  is_training
           a_     :							Dict     						=  use_input_mask
           a_     :							str     						=  use_token_type_ids
           a_     :							Tuple     						=  use_labels
           a_     :							List[str]     						=  vocab_size
           a_     :							Union[str, Any]     						=  hidden_size
           a_     :							Optional[Any]     						=  rotary_dim
           a_     :							int     						=  num_hidden_layers
           a_     :							int     						=  num_attention_heads
           a_     :							Any     						=  intermediate_size
           a_     :							Any     						=  hidden_act
           a_     :							Tuple     						=  hidden_dropout_prob
           a_     :							Dict     						=  attention_probs_dropout_prob
           a_     :							str     						=  max_position_embeddings
           a_     :							str     						=  initializer_range
           a_     :							Optional[int]     						=  None
           a_     :							Any     						=  vocab_size - 1
           a_     :							Dict     						=  vocab_size - 1
           a_     :							Tuple     						=  vocab_size - 1






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Any     						=  ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size       )

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

           a_     :							Optional[Any]     						=  GPTJConfig(
               vocab_size=self.vocab_size						,						n_embd=self.hidden_size						,						n_layer=self.num_hidden_layers						,						n_head=self.num_attention_heads						,						n_positions=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						,						rotary_dim=self.rotary_dim						,						)

           return (config, input_ids, input_mask)






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.prepare_config_and_inputs()
           a_						,     a_						,     a_     :							Dict     						=  config_and_inputs
           a_     :							str     						=  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  20
           a_     :							Tuple     						=  model_class_name(lowercase__       )

           a_     :							Union[str, Any]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							int     						=  jnp.ones((input_ids.shape[0], max_decoder_length)						,						dtype="""i4"""       )

           a_     :							Any     						=  jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )
           a_     :							int     						=  model(
               input_ids[:, :-1]						,						attention_mask=lowercase__						,						past_key_values=lowercase__						,						position_ids=lowercase__						,						)

           a_     :							Dict     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							int     						=  model(
               input_ids[:, -1:]						,						attention_mask=lowercase__						,						past_key_values=outputs_cache.past_key_values						,						position_ids=lowercase__						,						)

           a_     :							Dict     						=  model(lowercase__       )

           a_     :							Union[str, Any]     						=  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   :  Any						,						lowercase__   :  int						,						lowercase__   :  List[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  20
           a_     :							str     						=  model_class_name(lowercase__       )

           a_     :							Union[str, Any]     						=  jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]						,						axis=-1						,						)

           a_     :							Optional[int]     						=  model.init_cache(input_ids.shape[0]						,						lowercase__       )
           a_     :							Tuple     						=  jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,						(input_ids.shape[0], input_ids.shape[-1] - 1)       )

           a_     :							Union[str, Any]     						=  model(
               input_ids[:, :-1]						,						attention_mask=lowercase__						,						past_key_values=lowercase__						,						position_ids=lowercase__						,						)
           a_     :							List[str]     						=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,						dtype="""i4"""       )
           a_     :							List[str]     						=  model(
               input_ids[:, -1:]						,						past_key_values=outputs_cache.past_key_values						,						attention_mask=lowercase__						,						position_ids=lowercase__						,						)

           a_     :							List[str]     						=  model(lowercase__						,						attention_mask=lowercase__       )

           a_     :							Any     						=  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      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       List[str]   							=					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
      __magic_name__  :       str   							=					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							str     						=  FlaxGPTJModelTester(self       )






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[Any]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_						,     a_						,     a_     :							List[str]     						=  self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )






      @tooslow
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  GPTaTokenizer.from_pretrained("""gpt2"""						,						pad_token="""<|endoftext|>"""						,						padding_side="""left"""       )
           a_     :							Optional[Any]     						=  tokenizer(["""Hello this is a long string""", """Hey"""]						,						return_tensors="""np"""						,						padding=lowercase__						,						truncation=lowercase__       )

           a_     :							Union[str, Any]     						=  FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       )
           a_     :							List[str]     						=  False
           a_     :							Optional[int]     						=  model.config.eos_token_id

           a_     :							List[Any]     						=  jax.jit(model.generate       )

           a_     :							Dict     						=  jit_generate(
               inputs["""input_ids"""]						,						attention_mask=inputs["""attention_mask"""]						,						pad_token_id=tokenizer.pad_token_id       ).sequences

           a_     :							Tuple     						=  tokenizer.batch_decode(lowercase__						,						skip_special_tokens=lowercase__       )

           a_     :							List[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   :  Dict       ):


           '''simple docstring'''

           a_						,     a_     :							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
                     a_     :							Optional[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							int     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

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

                     a_     :							Any     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,						lowercase__       )
                     a_     :							Tuple     						=  fx_state

                     with torch.no_grad():
                          a_     :							Dict     						=  pt_model(**lowercase__       ).to_tuple()

                     a_     :							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__       )
                          a_     :							Union[str, Any]     						=  model_class.from_pretrained(lowercase__						,						from_pt=lowercase__       )

                     a_     :							Union[str, Any]     						=  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   :  Union[str, Any]       ):


           '''simple docstring'''

           a_						,     a_     :							Optional[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
                     a_     :							List[Any]     						=  self._prepare_for_class(lowercase__						,						lowercase__       )
                     a_     :							str     						=  {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

                     a_     :							Optional[Any]     						=  pt_model_class(lowercase__       ).eval()
                     a_     :							Any     						=  model_class(lowercase__						,						dtype=jnp.floataa       )

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

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

                     with torch.no_grad():
                          a_     :							List[str]     						=  pt_model(**lowercase__       ).to_tuple()

                     a_     :							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:
                          fx_model.save_pretrained(lowercase__       )
                          a_     :							str     						=  pt_model_class.from_pretrained(lowercase__						,						from_flax=lowercase__       )

                     with torch.no_grad():
                          a_     :							Tuple     						=  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       )






      @tooslow
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                a_     :							Union[str, Any]     						=  model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""       )
                a_     :							str     						=  model(np.ones((1, 1)       )       )
                self.assertIsNotNone(lowercase__       )
