

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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_mbartaa import MBartaaTokenizer
else:
	__snake_case							:     Tuple 						=	None


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     Optional[int] 						=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__snake_case							:     Optional[Any] 						=	{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

__snake_case							:     Any 						=	{
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
__snake_case							:     Optional[Any] 						=	["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]



class    A__(a_    ):
						"""simple docstring"""






						_A  :							str   			=	VOCAB_FILES_NAMES
						_A  :							int   			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A  :							Union[str, Any]   			=	PRETRAINED_VOCAB_FILES_MAP
						_A  :							Optional[Any]   			=	['''input_ids''', '''attention_mask''']
						_A  :							Tuple   			=	MBartaaTokenizer

						_A  :							List[int]   			=	[]
						_A  :							List[int]   			=	[]





						def __init__(  self       ,     _lowercase=None       ,     _lowercase=None       ,     _lowercase=None       ,     _lowercase=None       ,     _lowercase="</s>"       ,     _lowercase="</s>"       ,     _lowercase="<s>"       ,     _lowercase="<unk>"       ,     _lowercase="<pad>"       ,     _lowercase="<mask>"       ,     **_lowercase       ,     )      ->				Optional[int]:
									# Mask token behave like a normal word, i.e. include the space before it
									a_		:		Any				    =				AddedToken(_lowercase       ,     lstrip=_lowercase       ,     rstrip=_lowercase  ) if isinstance(_lowercase       ,     _lowercase  ) else mask_token

									a_		:		Union[str, Any]				    =				kwargs.get("""additional_special_tokens"""       ,     []  )
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    _lowercase       ,     src_lang=_lowercase       ,     tgt_lang=_lowercase       ,     tokenizer_file=_lowercase       ,     eos_token=_lowercase       ,     sep_token=_lowercase       ,     cls_token=_lowercase       ,     unk_token=_lowercase       ,     pad_token=_lowercase       ,     mask_token=_lowercase       ,     **_lowercase       ,     )

									a_		:		Tuple				    =				vocab_file
									a_		:		Optional[Any]				    =				False if not self.vocab_file else True

									a_		:		List[str]				    =				{
									    lang_code: self.convert_tokens_to_ids(_lowercase  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									a_		:		Tuple				    =				src_lang if src_lang is not None else """en_XX"""
									a_		:		Tuple				    =				tgt_lang
									a_		:		Union[str, Any]				    =				self.lang_code_to_id[self._src_lang]
									self.set_src_lang_special_tokens(self._src_lang  )





						@property
						def 	UpperCamelCase__			(  self  )      ->				str:
									return self._src_lang





						@src_lang.setter
						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				None:
									a_		:		Any				    =				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang  )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				List[int]:
									if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = "en_XX"       ,     _lowercase = None       ,     _lowercase = "ro_RO"       ,     **_lowercase       ,     )      ->				BatchEncoding:
									a_		:		List[Any]				    =				src_lang
									a_		:		int				    =				tgt_lang
									return super().prepare_seqaseq_batch(_lowercase       ,     _lowercase       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				str:
									return self.set_src_lang_special_tokens(self.src_lang  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									return self.set_tgt_lang_special_tokens(self.tgt_lang  )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				None:
									a_		:		Tuple				    =				self.convert_tokens_to_ids(_lowercase  )
									a_		:		Optional[int]				    =				[self.cur_lang_code_id]
									a_		:		str				    =				[self.eos_token_id]

									a_		:		Any				    =				self.convert_ids_to_tokens(self.prefix_tokens  )
									a_		:		Dict				    =				self.convert_ids_to_tokens(self.suffix_tokens  )

									a_		:		int				    =				processors.TemplateProcessing(
									    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,     pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,     self.prefix_tokens + self.suffix_tokens  )  )       ,     )





						def 	UpperCamelCase__			(  self       ,     _lowercase  )      ->				None:
									a_		:		Tuple				    =				self.convert_tokens_to_ids(_lowercase  )
									a_		:		Optional[Any]				    =				[self.cur_lang_code_id]
									a_		:		Optional[Any]				    =				[self.eos_token_id]

									a_		:		Tuple				    =				self.convert_ids_to_tokens(self.prefix_tokens  )
									a_		:		Union[str, Any]				    =				self.convert_ids_to_tokens(self.suffix_tokens  )

									a_		:		List[str]				    =				processors.TemplateProcessing(
									    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,     pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,     self.prefix_tokens + self.suffix_tokens  )  )       ,     )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     **_lowercase  )      ->				List[str]:
									if src_lang is None or tgt_lang is None:
												raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""  )
									a_		:		str				    =				src_lang
									a_		:		int				    =				self(_lowercase       ,     add_special_tokens=_lowercase       ,     return_tensors=_lowercase       ,     **_lowercase  )
									a_		:		List[Any]				    =				self.convert_tokens_to_ids(_lowercase  )
									a_		:		Any				    =				tgt_lang_id
									return inputs





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase = None  )      ->				Tuple[str]:
									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""  )

									if not os.path.isdir(_lowercase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
									a_		:		Optional[Any]				    =				os.path.join(
									    _lowercase       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

									return (out_vocab_file,)





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch



class    A__:
						"""simple docstring"""






						def __init__(  self       ,     _lowercase       ,     _lowercase=14       ,     _lowercase=7       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=True       ,     _lowercase=99       ,     _lowercase=32       ,     _lowercase=5       ,     _lowercase=4       ,     _lowercase=37       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=512       ,     _lowercase=0.0_2       ,     )      ->				Dict:
									a_		:		List[str]				    =				parent
									a_		:		Tuple				    =				batch_size
									a_		:		int				    =				seq_length
									a_		:		Tuple				    =				is_training
									a_		:		List[Any]				    =				use_input_mask
									a_		:		Dict				    =				use_token_type_ids
									a_		:		List[Any]				    =				use_labels
									a_		:		Optional[int]				    =				vocab_size
									a_		:		Any				    =				hidden_size
									a_		:		List[Any]				    =				num_hidden_layers
									a_		:		List[str]				    =				num_attention_heads
									a_		:		List[str]				    =				intermediate_size
									a_		:		List[str]				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		Tuple				    =				attention_probs_dropout_prob
									a_		:		Union[str, Any]				    =				max_position_embeddings
									a_		:		Tuple				    =				initializer_range
									a_		:		Any				    =				None
									a_		:		str				    =				vocab_size - 1
									a_		:		Optional[Any]				    =				vocab_size - 1
									a_		:		List[Any]				    =				vocab_size - 1





						def 	UpperCamelCase__			(  self  )      ->				Optional[int]:
									a_		:		Dict				    =				ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size  )

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

									a_		:		Optional[int]				    =				GPTaConfig(
									    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       ,     )

									return (config, input_ids, input_mask)





						def 	UpperCamelCase__			(  self  )      ->				int:
									a_		:		Any				    =				self.prepare_config_and_inputs()
									a_    ,			a_    ,			a_		:		List[str]				    =				config_and_inputs
									a_		:		Optional[Any]				    =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_    ,			a_    ,			a_		:		Dict				    =				self.prepare_config_and_inputs()

									a_		:		List[str]				    =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									a_		:		Optional[int]				    =				ids_tensor([self.batch_size, self.seq_length]       ,     vocab_size=2  )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				Optional[Any]:
									a_		:		Dict				    =				20
									a_		:		Tuple				    =				model_class_name(_lowercase  )

									a_		:		Tuple				    =				model.init_cache(input_ids.shape[0]       ,     _lowercase  )
									a_		:		Any				    =				jnp.ones((input_ids.shape[0], max_decoder_length)       ,     dtype="""i4"""  )

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

									a_		:		Dict				    =				model(_lowercase  )

									a_		:		Tuple				    =				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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				int:
									a_		:		Any				    =				20
									a_		:		Optional[int]				    =				model_class_name(_lowercase  )

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

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

									a_		:		Optional[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_		:		Dict				    =				model(
									    input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=_lowercase       ,     position_ids=_lowercase       ,     )

									a_		:		str				    =				model(_lowercase       ,     attention_mask=_lowercase  )

									a_		:		List[str]				    =				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    A__(a_,       a_,       unittest.TestCase    ):
						"""simple docstring"""






						_A  :							List[str]   			=	(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
						_A  :							Dict   			=	(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		List[str]				    =				FlaxGPTaModelTester(self  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Any				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(_lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				int:
									for model_class_name in self.all_model_classes:
												a_    ,			a_    ,			a_		:		Optional[Any]				    =				self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    _lowercase       ,     _lowercase       ,     _lowercase       ,     _lowercase  )





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		str				    =				GPTaTokenizer.from_pretrained("""gpt2"""       ,     pad_token="""</s>"""       ,     padding_side="""left"""  )
									a_		:		Optional[int]				    =				tokenizer(["""Hello this is a long string""", """Hey"""]       ,     return_tensors="""np"""       ,     padding=_lowercase       ,     truncation=_lowercase  )

									a_		:		Any				    =				FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""  )
									a_		:		List[Any]				    =				False
									a_		:		List[Any]				    =				model.config.eos_token_id

									a_		:		Tuple				    =				jax.jit(model.generate  )

									a_		:		Union[str, Any]				    =				jit_generate(inputs["""input_ids"""]       ,     attention_mask=inputs["""attention_mask"""]  ).sequences

									a_		:		Dict				    =				tokenizer.batch_decode(_lowercase       ,     skip_special_tokens=_lowercase  )

									a_		:		Tuple				    =				[
									    """Hello this is a long string of words. I'm going to start with the first one.\n""",
									    """Hey, I'm not sure if I'm going to be able to do""",
									]

									self.assertListEqual(_lowercase       ,     _lowercase  )





						@is_pt_flax_cross_test
						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									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_		:		List[str]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

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

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

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		List[str]				    =				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_		:		Dict				    =				model_class.from_pretrained(_lowercase       ,     from_pt=_lowercase  )

															a_		:		int				    =				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 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_    ,			a_		:		str				    =				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_		:		int				    =				self._prepare_for_class(_lowercase       ,     _lowercase  )
															a_		:		Optional[Any]				    =				{k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

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

															a_		:		Tuple				    =				pt_model_class(_lowercase  ).eval()
															a_		:		Dict				    =				model_class(_lowercase       ,     dtype=jnp.floataa  )

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

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

															with torch.no_grad():
																		a_		:		str				    =				pt_model(**_lowercase  ).to_tuple()

															a_		:		str				    =				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_		:		Dict				    =				pt_model_class.from_pretrained(_lowercase       ,     from_flax=_lowercase  )

															with torch.no_grad():
																		a_		:		List[str]				    =				pt_model_loaded(**_lowercase  ).to_tuple()

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





						@slow
						def 	UpperCamelCase__			(  self  )      ->				Any:
									for model_class_name in self.all_model_classes:
												a_		:		List[str]				    =				model_class_name.from_pretrained("""gpt2"""       ,     from_pt=_lowercase  )
												a_		:		List[str]				    =				model(np.ones((1, 1)  )  )
												self.assertIsNotNone(_lowercase  )


