code: 
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  __A       					=		{
      '''source_spm''': '''source.spm''',
      '''target_spm''': '''target.spm''',
      '''vocab''': '''vocab.json''',
      '''target_vocab_file''': '''target_vocab.json''',
      '''tokenizer_config_file''': '''tokenizer_config.json''',
  }
  
  __A       					=		{
      '''source_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
      },
      '''target_spm''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
      },
      '''vocab''': {
          '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
      },
      '''tokenizer_config_file''': {
          '''Helsinki-NLP/opus-mt-en-de''': (
              '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
          )
      },
  }
  
  __A       					=		{'''Helsinki-NLP/opus-mt-en-de''': 512}
  __A       					=		{}
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      List[str]	     =							VOCAB_FILES_NAMES
  	a__   :      Optional[int]	     =							PRETRAINED_VOCAB_FILES_MAP
  	a__   :      Dict	     =							PRETRAINED_INIT_CONFIGURATION
  	a__   :      Optional[Any]	     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	a__   :      List[str]	     =							["input_ids", "attention_mask"]
  	a__   :      Dict	     =							re.compile(">>.+<<")  # type: re.Pattern
  
  
  
  
  	def __init__(			self			:		Optional[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Tuple=None		,						__UpperCAmelCase			:		str=None		,						__UpperCAmelCase			:		Optional[Any]=None		,						__UpperCAmelCase			:		List[Any]="<unk>"		,						__UpperCAmelCase			:		Any="</s>"		,						__UpperCAmelCase			:		Optional[Any]="<pad>"		,						__UpperCAmelCase			:		List[Any]=512		,						__UpperCAmelCase			:		Optional[Dict[str, Any]] = None		,						__UpperCAmelCase			:		int=False		,						**__UpperCAmelCase			:		int		,						)    ->				None:
  							UpperCAmelCase_=     {} if sp_model_kwargs is None else sp_model_kwargs
  
  							super().__init__(
  							    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
  							    source_lang=__UpperCAmelCase		,						target_lang=__UpperCAmelCase		,						unk_token=__UpperCAmelCase		,						eos_token=__UpperCAmelCase		,						pad_token=__UpperCAmelCase		,						model_max_length=__UpperCAmelCase		,						sp_model_kwargs=self.sp_model_kwargs		,						target_vocab_file=__UpperCAmelCase		,						separate_vocabs=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  							assert Path(__UpperCAmelCase  ).exists(), F"""cannot find spm source {source_spm}"""
  
  							UpperCAmelCase_=     separate_vocabs
  							UpperCAmelCase_=     load_json(__UpperCAmelCase  )
  							if self.unk_token not in self.encoder:
  													raise KeyError("""<unk> token must be in vocab"""  )
  							assert self.pad_token in self.encoder
  
  							if separate_vocabs:
  													UpperCAmelCase_=     load_json(__UpperCAmelCase  )
  													UpperCAmelCase_=     {v: k for k, v in self.target_encoder.items()}
  													UpperCAmelCase_=     []
  							else:
  													UpperCAmelCase_=     {v: k for k, v in self.encoder.items()}
  													UpperCAmelCase_=     [k for k in self.encoder if k.startswith(""">>"""  ) and k.endswith("""<<"""  )]
  
  							UpperCAmelCase_=     source_lang
  							UpperCAmelCase_=     target_lang
  							UpperCAmelCase_=     [source_spm, target_spm]
  
  							# load SentencePiece model for pre-processing
  							UpperCAmelCase_=     load_spm(__UpperCAmelCase		,						self.sp_model_kwargs  )
  							UpperCAmelCase_=     load_spm(__UpperCAmelCase		,						self.sp_model_kwargs  )
  							UpperCAmelCase_=     self.spm_source
  							UpperCAmelCase_=     self.encoder
  
  							# Multilingual target side: default to using first supported language code.
  
  							self._setup_normalizer()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				int:
  							try:
  													from sacremoses import MosesPunctNormalizer
  
  													UpperCAmelCase_=     MosesPunctNormalizer(self.source_lang  ).normalize
  							except (ImportError, FileNotFoundError):
  													warnings.warn("""Recommended: pip install sacremoses."""  )
  													UpperCAmelCase_=     lambda __UpperCAmelCase  : x
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		str  )    ->				str:
  							return self.punc_normalizer(__UpperCAmelCase  ) if x else ""
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		List[Any]  )    ->				Any:
  							return self.current_encoder.get(__UpperCAmelCase		,						self.current_encoder[self.unk_token]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		str  )    ->				int:
  							UpperCAmelCase_=     self.language_code_re.match(__UpperCAmelCase  )
  							UpperCAmelCase_=     [match.group(0  )] if match else []
  							return code, self.language_code_re.sub(""""""		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		str  )    ->				List[str]:
  							UpperCAmelCase_,     UpperCAmelCase_=     self.remove_language_code(__UpperCAmelCase  )
  							UpperCAmelCase_=     self.current_spm.encode(__UpperCAmelCase		,						out_type=__UpperCAmelCase  )
  							return code + pieces
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		int  )    ->				str:
  							return self.decoder.get(__UpperCAmelCase		,						self.unk_token  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Union[str, Any]		,						**__UpperCAmelCase			:		Optional[int]  )    ->				str:
  							return super().batch_decode(__UpperCAmelCase		,						**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		List[Any]		,						**__UpperCAmelCase			:		Tuple  )    ->				List[Any]:
  							return super().decode(__UpperCAmelCase		,						**__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		List[str]  )    ->				str:
  							UpperCAmelCase_=     self.spm_source if self._decode_use_source_tokenizer else self.spm_target
  							UpperCAmelCase_=     []
  							UpperCAmelCase_=     """"""
  							for token in tokens:
  													# make sure that special tokens are not decoded using sentencepiece model
  													if token in self.all_special_tokens:
  																			out_string += sp_model.decode_pieces(__UpperCAmelCase  ) + token + " "
  																			UpperCAmelCase_=     []
  													else:
  																			current_sub_tokens.append(__UpperCAmelCase  )
  							out_string += sp_model.decode_pieces(__UpperCAmelCase  )
  							return out_string.strip()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[int]=None  )    ->				List[int]:
  							if token_ids_a is None:
  													return token_ids_a + [self.eos_token_id]
  							# We don't expect to process pairs, but leave the pair logic for API consistency
  							return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				List[str]:
  							UpperCAmelCase_=     self.spm_source
  							UpperCAmelCase_=     self.encoder
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.spm_target
  							if self.separate_vocabs:
  													UpperCAmelCase_=     self.target_encoder
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				int:
  							return len(self.encoder  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Optional[str] = None  )    ->				Tuple[str]:
  							if not os.path.isdir(__UpperCAmelCase  ):
  													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
  													return
  							UpperCAmelCase_=     []
  
  							if self.separate_vocabs:
  													UpperCAmelCase_=     os.path.join(
  													    __UpperCAmelCase		,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]		,						)
  													UpperCAmelCase_=     os.path.join(
  													    __UpperCAmelCase		,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]		,						)
  													save_json(self.encoder		,						__UpperCAmelCase  )
  													save_json(self.target_encoder		,						__UpperCAmelCase  )
  													saved_files.append(__UpperCAmelCase  )
  													saved_files.append(__UpperCAmelCase  )
  							else:
  													UpperCAmelCase_=     os.path.join(
  													    __UpperCAmelCase		,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]  )
  													save_json(self.encoder		,						__UpperCAmelCase  )
  													saved_files.append(__UpperCAmelCase  )
  
  							for spm_save_filename, spm_orig_path, spm_model in zip(
  							    [VOCAB_FILES_NAMES["""source_spm"""], VOCAB_FILES_NAMES["""target_spm"""]]		,						self.spm_files		,						[self.spm_source, self.spm_target]		,						):
  													UpperCAmelCase_=     os.path.join(
  													    __UpperCAmelCase		,						(filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename  )
  													if os.path.abspath(__UpperCAmelCase  ) != os.path.abspath(__UpperCAmelCase  ) and os.path.isfile(__UpperCAmelCase  ):
  																			copyfile(__UpperCAmelCase		,						__UpperCAmelCase  )
  																			saved_files.append(__UpperCAmelCase  )
  													elif not os.path.isfile(__UpperCAmelCase  ):
  																			with open(__UpperCAmelCase		,						"""wb"""  ) as fi:
  																									UpperCAmelCase_=     spm_model.serialized_model_proto()
  																									fi.write(__UpperCAmelCase  )
  																			saved_files.append(__UpperCAmelCase  )
  
  							return tuple(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Dict:
  							return self.get_src_vocab()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Any:
  							return dict(self.encoder		,						**self.added_tokens_encoder  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				int:
  							return dict(self.target_encoder		,						**self.added_tokens_decoder  )
  
  
  
  
  	def __getstate__(			self			:		List[Any]  )    ->				Dict:
  							UpperCAmelCase_=     self.__dict__.copy()
  							state.update(
  							    {k: None for k in ["""spm_source""", """spm_target""", """current_spm""", """punc_normalizer""", """target_vocab_file"""]}  )
  							return state
  
  
  
  
  	def __setstate__(			self			:		int		,						__UpperCAmelCase			:		Dict  )    ->				None:
  							UpperCAmelCase_=     d
  
  							# for backward compatibility
  							if not hasattr(self		,						"""sp_model_kwargs"""  ):
  													UpperCAmelCase_=     {}
  
  							UpperCAmelCase_,     UpperCAmelCase_=     (load_spm(__UpperCAmelCase		,						self.sp_model_kwargs  ) for f in self.spm_files)
  							UpperCAmelCase_=     self.spm_source
  							self._setup_normalizer()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						*__UpperCAmelCase			:		Union[str, Any]		,						**__UpperCAmelCase			:		Dict  )    ->				Optional[Any]:
  							return 1
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		str  )    ->				Tuple:
  							UpperCAmelCase_=     set(self.all_special_ids  )  # call it once instead of inside list comp
  							all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special
  							return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		List		,						__UpperCAmelCase			:		Optional[List] = None		,						__UpperCAmelCase			:		bool = False  )    ->				List[int]:
  							if already_has_special_tokens:
  													return self._special_token_mask(__UpperCAmelCase  )
  							elif token_ids_a is None:
  													return self._special_token_mask(__UpperCAmelCase  ) + [1]
  							else:
  													return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]
  
  
  
  def  __a  (					lowerCAmelCase_     :      str    ,lowerCAmelCase_     :      Dict[str, Any]       )   ->				sentencepiece.SentencePieceProcessor:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     sentencepiece.SentencePieceProcessor(**lowerCAmelCase_       )
  						spm.Load(lowerCAmelCase_       )
  						return spm
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      str       )   ->				None:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						with open(lowerCAmelCase_    ,"""w"""       ) as f:
  												json.dump(lowerCAmelCase_    ,lowerCAmelCase_    ,indent=2       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      str       )   ->				Union[Dict, List]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						with open(lowerCAmelCase_    ,"""r"""       ) as f:
  												return json.load(lowerCAmelCase_       )
  
  
  
  
  
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  )
  
  
  
  
  