
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowercase_ 				=			{
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

lowercase_ 				=			{
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		set()
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)							)
    __SCREAMING_SNAKE_CASE      :	int					=		char

  __SCREAMING_SNAKE_CASE      :	str					=		set(snake_case							)
  return pairs





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			VOCAB_FILES_NAMES
     lowerCAmelCase_							   =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_							   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(				self							:							List[str]     ,						_A							:							Tuple     ,						_A							:							Optional[Any]     ,						_A							:							str="<s>"     ,						_A							:							Any="</s>"     ,						_A							:							List[str]="</s>"     ,						_A							:							str="<s>"     ,						_A							:							Optional[int]="<unk>"     ,						_A							:							Any="<pad>"     ,						_A							:							Dict="<mask>"     ,						**_A							:							Tuple     ,						):







       """simple docstring"""




       super().__init__(
           bos_token=_A     ,						eos_token=_A     ,						unk_token=_A     ,						sep_token=_A     ,						cls_token=_A     ,						pad_token=_A     ,						mask_token=_A     ,						**_A     ,						)

       __SCREAMING_SNAKE_CASE      :	str					=		vocab_file
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		merges_file

       __SCREAMING_SNAKE_CASE      :	List[str]					=		{}
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		0
       __SCREAMING_SNAKE_CASE      :	List[str]					=		1
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		2
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		3

       self.add_from_file(_A )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		{v: k for k, v in self.encoder.items()}

       with open(_A     ,						encoding='''utf-8''' ) as merges_handle:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		merges_handle.read().split('''\n''' )[:-1]
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[tuple(merge.split()[:-1] ) for merge in merges]
       __SCREAMING_SNAKE_CASE      :	Tuple					=		dict(zip(_A     ,						range(len(_A ) ) ) )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		{}



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None ):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE      :	Any					=		[self.cls_token_id]
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None     ,						_A							:							bool = False ):







       """simple docstring"""




       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_A     ,						token_ids_a=_A     ,						already_has_special_tokens=_A )

       if token_ids_a is None:
         return [1] + ([0] * len(_A )) + [1]
       return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							List[int]     ,						_A							:							Optional[List[int]] = None ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		[self.sep_token_id]
       __SCREAMING_SNAKE_CASE      :	Tuple					=		[self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return len(self.encoder )



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       return dict(self.encoder     ,						**self.added_tokens_encoder )



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Any] ):







       """simple docstring"""




       if token in self.cache:
         return self.cache[token]
       __SCREAMING_SNAKE_CASE      :	int					=		tuple(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		get_pairs(_A )

       if not pairs:
         return token

       while True:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		min(_A     ,						key=lambda _A : self.bpe_ranks.get(_A     ,						float('''inf''' ) ) )
         if bigram not in self.bpe_ranks:
           break
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		bigram
         __SCREAMING_SNAKE_CASE      :	List[str]					=		[]
         __SCREAMING_SNAKE_CASE      :	List[str]					=		0
         while i < len(_A ):
           try:
             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		word.index(_A     ,						_A )
           except ValueError:
             new_word.extend(word[i:] )
             break
           else:
             new_word.extend(word[i:j] )
             __SCREAMING_SNAKE_CASE      :	Dict					=		j

           if word[i] == first and i < len(_A ) - 1 and word[i + 1] == second:
             new_word.append(first + second )
             i += 2
           else:
             new_word.append(word[i] )
             i += 1
         __SCREAMING_SNAKE_CASE      :	List[str]					=		tuple(_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		new_word
         if len(_A ) == 1:
           break
         else:
           __SCREAMING_SNAKE_CASE      :	Tuple					=		get_pairs(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		'''@@ '''.join(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		word[:-4]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		word
       return word



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	int					=		[]

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		re.findall(r'''\S+\n?'''     ,						_A )

       for token in words:
         split_tokens.extend(list(self.bpe(_A ).split(''' ''' ) ) )
       return split_tokens



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							Optional[Any] ):







       """simple docstring"""




       return self.encoder.get(_A     ,						self.encoder.get(self.unk_token ) )



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							int ):







       """simple docstring"""




       return self.decoder.get(_A     ,						self.unk_token )



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							str ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		''' '''.join(_A ).replace('''@@ '''     ,						'''''' ).strip()
       return out_string



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							str     ,						_A							:							Optional[str] = None ):







       """simple docstring"""




       if not os.path.isdir(_A ):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
         return
       __SCREAMING_SNAKE_CASE      :	Tuple					=		os.path.join(
           _A     ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		os.path.join(
           _A     ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

       if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ):
         copyfile(self.vocab_file     ,						_A )

       if os.path.abspath(self.merges_file ) != os.path.abspath(_A ):
         copyfile(self.merges_file     ,						_A )

       return out_vocab_file, out_merge_file




     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							str ):







       """simple docstring"""




       if isinstance(_A     ,						_A ):
         try:
           with open(_A     ,						'''r'''     ,						encoding='''utf-8''' ) as fd:
             self.add_from_file(_A )
         except FileNotFoundError as fnfe:
           raise fnfe
         except UnicodeError:
           raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
         return

       __SCREAMING_SNAKE_CASE      :	Any					=		f.readlines()
       for lineTmp in lines:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		lineTmp.strip()
         __SCREAMING_SNAKE_CASE      :	Tuple					=		line.rfind(''' ''' )
         if idx == -1:
           raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''' )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		line[:idx]
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		len(self.encoder )







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   lowerCAmelCase				:
 def __init__( self    : List[str]     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : str=14     ,	UpperCAmelCase    : Any=7     ,	UpperCAmelCase    : Dict=True     ,	UpperCAmelCase    : List[str]=True     ,	UpperCAmelCase    : Any=False     ,	UpperCAmelCase    : Tuple=True     ,	UpperCAmelCase    : int=99     ,	UpperCAmelCase    : str=32     ,	UpperCAmelCase    : int=4     ,	UpperCAmelCase    : Optional[Any]=4     ,	UpperCAmelCase    : Any=4     ,	UpperCAmelCase    : Optional[int]=37     ,	UpperCAmelCase    : Union[str, Any]="gelu"     ,	UpperCAmelCase    : List[Any]=0.1     ,	UpperCAmelCase    : Dict=0.1     ,	UpperCAmelCase    : Optional[int]=512     ,	UpperCAmelCase    : List[Any]=0.0_2     ,	)     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						batch_size
     lowerCamelCase__       : Optional[Any]   					=						seq_length
     lowerCamelCase__       : str   					=						is_training
     lowerCamelCase__       : Any   					=						use_input_mask
     lowerCamelCase__       : Optional[Any]   					=						use_token_type_ids
     lowerCamelCase__       : Optional[Any]   					=						use_labels
     lowerCamelCase__       : Optional[Any]   					=						vocab_size
     lowerCamelCase__       : Optional[int]   					=						hidden_size
     lowerCamelCase__       : Optional[Any]   					=						rotary_dim
     lowerCamelCase__       : Optional[Any]   					=						num_hidden_layers
     lowerCamelCase__       : Optional[Any]   					=						num_attention_heads
     lowerCamelCase__       : Tuple   					=						intermediate_size
     lowerCamelCase__       : Union[str, Any]   					=						hidden_act
     lowerCamelCase__       : List[Any]   					=						hidden_dropout_prob
     lowerCamelCase__       : int   					=						attention_probs_dropout_prob
     lowerCamelCase__       : Dict   					=						max_position_embeddings
     lowerCamelCase__       : Tuple   					=						initializer_range
     lowerCamelCase__       : Optional[int]   					=						None
     lowerCamelCase__       : int   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
     lowerCamelCase__       : str   					=						vocab_size - 1
 def 	A_      ( self    : str      )     ->      int:
     lowerCamelCase__       : Optional[int]   					=						ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

     lowerCamelCase__       : Optional[Any]   					=						None
     if self.use_input_mask:
         lowerCamelCase__       : List[str]   					=						random_attention_mask([self.batch_size, self.seq_length]      )

     lowerCamelCase__       : Tuple   					=						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=UpperCAmelCase     ,	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 	A_      ( self    : Optional[Any]      )     ->      Union[str, Any]:
     lowerCamelCase__       : Tuple   					=						self.prepare_config_and_inputs()
     lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : List[Any]   					=						config_and_inputs
     lowerCamelCase__       : List[Any]   					=						{'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict
 def 	A_      ( self    : int     ,	UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[str]     ,	UpperCAmelCase    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Tuple   					=						20
     lowerCamelCase__       : Dict   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : int   					=						jnp.ones((input_ids.shape[0], max_decoder_length)     ,	dtype='i4'      )

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

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

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )

     lowerCamelCase__       : Optional[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 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : Any     ,	UpperCAmelCase    : List[Any]     ,	UpperCAmelCase    : List[str]      )     ->      Optional[Any]:
     lowerCamelCase__       : Any   					=						20
     lowerCamelCase__       : Any   					=						model_class_name(UpperCAmelCase      )

     lowerCamelCase__       : List[str]   					=						jnp.concatenate(
         [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]     ,	axis=-1     ,	)

     lowerCamelCase__       : str   					=						model.init_cache(input_ids.shape[0]     ,	UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1      )[None, :]     ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )

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

     lowerCamelCase__       : List[Any]   					=						model(UpperCAmelCase     ,	attention_mask=UpperCAmelCase      )

     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   lowerCAmelCase				(   __UpperCamelCase,    __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 UpperCAmelCase__						=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()
 def 	A_      ( self    : Optional[Any]      )     ->      Tuple:
     lowerCamelCase__       : Optional[Any]   					=						FlaxGPTJModelTester(self      )
 def 	A_      ( self    : Tuple      )     ->      Union[str, Any]:
     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(UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]      )     ->      Tuple:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : str   					=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )
 @tooslow
 def 	A_      ( self    : Dict      )     ->      Tuple:
     lowerCamelCase__       : str   					=						GPTaTokenizer.from_pretrained('gpt2'     ,	pad_token='<|endoftext|>'     ,	padding_side='left'      )
     lowerCamelCase__       : Dict   					=						tokenizer(['Hello this is a long string', 'Hey']     ,	return_tensors='np'     ,	padding=UpperCAmelCase     ,	truncation=UpperCAmelCase      )

     lowerCamelCase__       : str   					=						FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      )
     lowerCamelCase__       : Optional[int]   					=						False
     lowerCamelCase__       : Optional[int]   					=						model.config.eos_token_id

     lowerCamelCase__       : Union[str, Any]   					=						jax.jit(model.generate      )

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

     lowerCamelCase__       : Optional[int]   					=						tokenizer.batch_decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase      )

     lowerCamelCase__       : int   					=						[
         '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 	A_      ( self    : List[str]      )     ->      Union[str, Any]:
     lowerCamelCase__					,				lowerCamelCase__       : 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
             lowerCamelCase__       : Dict   					=						self._prepare_for_class(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Optional[int]   					=						{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__       : Union[str, Any]   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

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

             lowerCamelCase__       : str   					=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						fx_state

             with torch.no_grad():
                 lowerCamelCase__       : List[str]   					=						pt_model(**UpperCAmelCase      ).to_tuple()

             lowerCamelCase__       : Dict   					=						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      )
                 lowerCamelCase__       : Tuple   					=						model_class.from_pretrained(UpperCAmelCase     ,	from_pt=UpperCAmelCase      )

             lowerCamelCase__       : int   					=						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 	A_      ( self    : Union[str, Any]      )     ->      int:
     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(UpperCAmelCase     ,	UpperCAmelCase      )
             lowerCamelCase__       : Tuple   					=						{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCamelCase__       : Union[str, Any]   					=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCamelCase__       : int   					=						getattr(UpperCAmelCase     ,	UpperCAmelCase      )

             lowerCamelCase__       : List[Any]   					=						pt_model_class(UpperCAmelCase      ).eval()
             lowerCamelCase__       : str   					=						model_class(UpperCAmelCase     ,	dtype=jnp.floataa      )

             lowerCamelCase__       : List[str]   					=						load_flax_weights_in_pytorch_model(UpperCAmelCase     ,	fx_model.params      )
             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(UpperCAmelCase      ):
                 lowerCamelCase__       : List[Any]   					=						0
                 lowerCamelCase__       : Tuple   					=						1
                 lowerCamelCase__       : Optional[Any]   					=						0
                 lowerCamelCase__       : List[str]   					=						1

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

             with torch.no_grad():
                 lowerCamelCase__       : Any   					=						pt_model(**UpperCAmelCase      ).to_tuple()

             lowerCamelCase__       : List[Any]   					=						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      )
                 lowerCamelCase__       : Tuple   					=						pt_model_class.from_pretrained(UpperCAmelCase     ,	from_flax=UpperCAmelCase      )

             with torch.no_grad():
                 lowerCamelCase__       : Optional[int]   					=						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      )







 @tooslow
 def 	A_      ( self    : List[str]      )     ->      List[Any]:
     for model_class_name in self.all_model_classes:
         lowerCamelCase__       : Union[str, Any]   					=						model_class_name.from_pretrained('EleutherAI/gpt-j-6B'      )
         lowerCamelCase__       : Any   					=						model(np.ones((1, 1)      )      )
         self.assertIsNotNone(UpperCAmelCase      )





