

from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel

@require_tf
class     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				BlenderbotConfig
			__lowerCamelCase       				=				{}
			__lowerCamelCase       				=				'gelu'


			def __init__(			self					,					lowercase					,					lowercase=13					,					lowercase=7					,					lowercase=True					,					lowercase=False					,					lowercase=99					,					lowercase=32					,					lowercase=2					,					lowercase=4					,					lowercase=37					,					lowercase=0.1					,					lowercase=0.1					,					lowercase=20					,					lowercase=2					,					lowercase=1					,					lowercase=0					,					)     ->  Any:
							'''simple docstring'''






							A__						=	parent
							A__						=	batch_size
							A__						=	seq_length
							A__						=	is_training
							A__						=	use_labels
							A__						=	vocab_size
							A__						=	hidden_size
							A__						=	num_hidden_layers
							A__						=	num_attention_heads
							A__						=	intermediate_size

							A__						=	hidden_dropout_prob
							A__						=	attention_probs_dropout_prob
							A__						=	max_position_embeddings
							A__						=	eos_token_id
							A__						=	pad_token_id
							A__						=	bos_token_id


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	ids_tensor([self.batch_size, self.seq_length - 1]					,					self.vocab_size				)
							A__						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)					,					1				)
							A__						=	tf.concat([input_ids, eos_tensor]					,					axis=1				)

							A__						=	ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size				)

							A__						=	self.config_cls(
							    vocab_size=self.vocab_size					,					d_model=self.hidden_size					,					encoder_layers=self.num_hidden_layers					,					decoder_layers=self.num_hidden_layers					,					encoder_attention_heads=self.num_attention_heads					,					decoder_attention_heads=self.num_attention_heads					,					encoder_ffn_dim=self.intermediate_size					,					decoder_ffn_dim=self.intermediate_size					,					dropout=self.hidden_dropout_prob					,					attention_dropout=self.attention_probs_dropout_prob					,					max_position_embeddings=self.max_position_embeddings					,					eos_token_ids=[2]					,					bos_token_id=self.bos_token_id					,					pad_token_id=self.pad_token_id					,					decoder_start_token_id=self.pad_token_id					,					**self.config_updates					,					)
							A__						=	prepare_blenderbot_inputs_dict(lowercase					,					lowercase					,					lowercase				)
							return config, inputs_dict


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  Tuple:
							'''simple docstring'''






							A__						=	TFBlenderbotModel(config=lowercase				).get_decoder()
							A__						=	inputs_dict["input_ids"]

							A__						=	input_ids[:1, :]
							A__						=	inputs_dict["attention_mask"][:1, :]
							A__						=	inputs_dict["head_mask"]
							A__						=	1

							# first forward pass
							A__						=	model(lowercase					,					attention_mask=lowercase					,					head_mask=lowercase					,					use_cache=lowercase				)

							A__			, A__						=	outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							A__						=	ids_tensor((self.batch_size, 3)					,					config.vocab_size				)
							A__						=	tf.cast(ids_tensor((self.batch_size, 3)					,					2				)					,					tf.inta				)

							# append to next input_ids and
							A__						=	tf.concat([input_ids, next_tokens]					,					axis=-1				)
							A__						=	tf.concat([attention_mask, next_attn_mask]					,					axis=-1				)

							A__						=	model(lowercase					,					attention_mask=lowercase				)[0]
							A__						=	model(lowercase					,					attention_mask=lowercase					,					past_key_values=lowercase				)[0]

							self.parent.assertEqual(next_tokens.shape[1]					,					output_from_past.shape[1]				)

							# select random slice
							A__						=	int(ids_tensor((1,)					,					output_from_past.shape[-1]				)				)
							A__						=	output_from_no_past[:, -3:, random_slice_idx]
							A__						=	output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(lowercase					,					lowercase					,					rtol=1e-3				)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Any=None		,				SCREAMING_SNAKE_CASE_:							int=None		,				SCREAMING_SNAKE_CASE_:							int=None		,				SCREAMING_SNAKE_CASE_:							int=None		,				SCREAMING_SNAKE_CASE_:							List[str]=None		,				)	->					Optional[int]:




				'''simple docstring'''





				if attention_mask is None:
								A__						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_		,				config.pad_token_id					)		,				tf.inta					)
				if decoder_attention_mask is None:
								A__						=	tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape		,				dtype=tf.inta					),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,				config.pad_token_id					)		,				tf.inta					),
								    ]		,				axis=-1		,				)
				if head_mask is None:
								A__						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
				if decoder_head_mask is None:
								A__						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
				if cross_attn_head_mask is None:
								A__						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}

@require_tf
class     a__  (    snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
			__lowerCamelCase       				=				(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
			__lowerCamelCase       				=				(
			    {
			        'conversational': TFBlenderbotForConditionalGeneration,
			        'feature-extraction': TFBlenderbotModel,
			        'summarization': TFBlenderbotForConditionalGeneration,
			        'text2text-generation': TFBlenderbotForConditionalGeneration,
			        'translation': TFBlenderbotForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__lowerCamelCase       				=				True
			__lowerCamelCase       				=				False
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	TFBlenderbotModelTester(self				)
							A__						=	ConfigTester(self					,					config_class=lowercase				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*lowercase				)

@require_tokenizers
@require_tf
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				['My friends are cool but they eat too many carbs.']
			__lowerCamelCase       				=				'facebook/blenderbot-400M-distill'


			@cached_property
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							return BlenderbotTokenizer.from_pretrained(self.model_name				)


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
							return model


			@slow
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.tokenizer(self.src_text					,					return_tensors="tf"				)
							A__						=	self.model.generate(
							    model_inputs.input_ids					,					)
							A__						=	self.tokenizer.batch_decode(generated_ids.numpy()					,					skip_special_tokens=lowercase				)[0]
							assert (
							    generated_words
							    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
							)





'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {'vocab_file': 'sentencepiece.bpe.model'}

lowerCamelCase__					=   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCamelCase__					=   {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Any				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Union[str, Any]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		int				      =   ["input_ids", "attention_mask"]




			def __init__(						self     :   int		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   List[Any]="<s>"		,     lowerCamelCase__     :   Dict="</s>"		,     lowerCamelCase__     :   Union[str, Any]="</s>"		,     lowerCamelCase__     :   List[Any]="<s>"		,     lowerCamelCase__     :   List[str]="<unk>"		,     lowerCamelCase__     :   str="<pad>"		,     lowerCamelCase__     :   Tuple="<mask>"		,     lowerCamelCase__     :   Optional[Dict[str, Any]] = None		,     **lowerCamelCase__     :   Dict		,     )      ->None:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__		,     lowerCamelCase__       ) else mask_token

					_UpperCAmelCase						:				Any	    =					{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=lowerCamelCase__		,     eos_token=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     sp_model_kwargs=self.sp_model_kwargs		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Union[str, Any]	    =					vocab_file
					_UpperCAmelCase						:				Dict	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(str(lowerCamelCase__       )       )

					_UpperCAmelCase						:				Optional[int]	    =					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					_UpperCAmelCase						:				List[str]	    =					len(self.sp_model       ) - 1
					_UpperCAmelCase						:				List[str]	    =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCAmelCase						:				Union[str, Any]	    =					[self.cls_token_id]
					_UpperCAmelCase						:				str	    =					[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None		,     lowerCamelCase__     :   bool = False       )      ->List[int]:



					'''simple docstring'''







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

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




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[int]	    =					[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 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					return len(self.sp_model       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					{self.convert_ids_to_tokens(lowerCamelCase__       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   str       )      ->List[str]:



					'''simple docstring'''







					return self.sp_model.encode(lowerCamelCase__		,     out_type=lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[Any]:



					'''simple docstring'''







					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					_UpperCAmelCase						:				Optional[Any]	    =					self.sp_model.PieceToId(lowerCamelCase__       )

					return spm_id if spm_id else self.unk_token_id




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Any       )      ->Optional[Any]:



					'''simple docstring'''







					if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				Optional[Any]	    =					""
					_UpperCAmelCase						:				Optional[int]	    =					False
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									if not prev_is_special:
											out_string += " "
									out_string += self.sp_model.decode(lowerCamelCase__       ) + token
									_UpperCAmelCase						:				Union[str, Any]	    =					True
									_UpperCAmelCase						:				Union[str, Any]	    =					[]
							else:
									current_sub_tokens.append(lowerCamelCase__       )
									_UpperCAmelCase						:				int	    =					False
					out_string += self.sp_model.decode(lowerCamelCase__       )
					return out_string.strip()




			def __getstate__(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.__dict__.copy()
					_UpperCAmelCase						:				Optional[int]	    =					None
					return state




			def __setstate__(						self     :   Any		,     lowerCamelCase__     :   Dict       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					d

					# for backward compatibility
					if not hasattr(self		,     "sp_model_kwargs"       ):
							_UpperCAmelCase						:				List[Any]	    =					{}

					_UpperCAmelCase						:				Optional[int]	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.vocab_file       )






			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







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

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ) and os.path.isfile(self.vocab_file       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )
					elif not os.path.isfile(self.vocab_file       ):
							with open(lowerCamelCase__		,     "wb"       ) as fi:
									_UpperCAmelCase						:				Dict	    =					self.sp_model.serialized_model_proto()
									fi.write(lowerCamelCase__       )

					return (out_vocab_file,)



