



'''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_       )









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  )




