





"""simple docstring"""




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  __UpperCamelCase       :
							lowerCamelCase     :      Optional[int]         =BlenderbotConfig
							lowerCamelCase     :      Dict         ={}
							lowerCamelCase     :      Any         ="""gelu"""






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=99   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=37   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=20   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0   ,      )			->				Dict:
								a   :	List[str]			=       parent
								a   :	List[str]			=       batch_size
								a   :	Optional[Any]			=       seq_length
								a   :	Any			=       is_training
								a   :	Optional[int]			=       use_labels
								a   :	Any			=       vocab_size
								a   :	Tuple			=       hidden_size
								a   :	Tuple			=       num_hidden_layers
								a   :	str			=       num_attention_heads
								a   :	str			=       intermediate_size

								a   :	Union[str, Any]			=       hidden_dropout_prob
								a   :	int			=       attention_probs_dropout_prob
								a   :	Optional[Any]			=       max_position_embeddings
								a   :	Any			=       eos_token_id
								a   :	Optional[int]			=       pad_token_id
								a   :	Any			=       bos_token_id






							def 					__a     (     self   )			->				Any:
								a   :	str			=       ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size   )
								a   :	Tuple			=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,      1   )
								a   :	Any			=       tf.concat([input_ids, eos_tensor]   ,      axis=1   )

								a   :	List[str]			=       ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size   )

								a   :	str			=       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   :	Optional[Any]			=       prepare_blenderbot_inputs_dict(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								return config, inputs_dict






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Tuple			=       TFBlenderbotModel(config=lowerCAmelCase__   ).get_decoder()
								a   :	Any			=       inputs_dict["input_ids"]

								a   :	Tuple			=       input_ids[:1, :]
								a   :	Dict			=       inputs_dict["attention_mask"][:1, :]
								a   :	List[Any]			=       inputs_dict["head_mask"]
								a   :	Any			=       1

								# first forward pass
								a   :	Optional[Any]			=       model(lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   ,      head_mask=lowerCAmelCase__   ,      use_cache=lowerCAmelCase__   )

								a   :	Optional[int]			=       outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a   :	List[str]			=       ids_tensor((self.batch_size, 3)   ,      config.vocab_size   )
								a   :	Any			=       tf.cast(ids_tensor((self.batch_size, 3)   ,      2   )   ,      tf.inta   )

								# append to next input_ids and
								a   :	int			=       tf.concat([input_ids, next_tokens]   ,      axis=-1   )
								a   :	List[Any]			=       tf.concat([attention_mask, next_attn_mask]   ,      axis=-1   )

								a   :	Tuple			=       model(lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   )[0]
								a   :	List[Any]			=       model(lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   ,      past_key_values=lowerCAmelCase__   )[0]

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

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

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					Any					, _lowercase		:					Optional[Any]					, _lowercase		:					Any=None					, _lowercase		:					Union[str, Any]=None					, _lowercase		:					Union[str, Any]=None					, _lowercase		:					Tuple=None					, _lowercase		:					Dict=None					, )				->Dict:






	'''simple docstring'''






	if attention_mask is None:
		a   :	str			=       tf.cast(tf.math.not_equal(_lowercase					, config.pad_token_id      )					, tf.inta      )
	if decoder_attention_mask is None:
		a   :	Union[str, Any]			=       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   :	int			=       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		a   :	Optional[int]			=       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		a   :	List[Any]			=       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  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							lowerCamelCase     :      Optional[Any]         =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							lowerCamelCase     :      Any         =(
							    {
							        """conversational""": TFBlenderbotForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotModel,
							        """summarization""": TFBlenderbotForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotForConditionalGeneration,
							        """translation""": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase     :      Tuple         =True
							lowerCamelCase     :      Optional[int]         =False
							lowerCamelCase     :      List[Any]         =False






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Union[str, Any]			=       TFBlenderbotModelTester(self   )
								a   :	Union[str, Any]			=       ConfigTester(self   ,      config_class=lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								self.config_tester.run_common_tests()






							def 					__a     (     self   )			->				int:
								a   :	Tuple			=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__   )


@require_tokenizers
@require_tf
class  __UpperCamelCase       (		unittest.TestCase		):
							lowerCamelCase     :      int         =["""My friends are cool but they eat too many carbs."""]
							lowerCamelCase     :      List[Any]         ="""facebook/blenderbot-400M-distill"""






							@cached_property
							def 					__a     (     self   )			->				Any:
								return BlenderbotTokenizer.from_pretrained(self.model_name   )






							@cached_property
							def 					__a     (     self   )			->				List[Any]:
								a   :	List[Any]			=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
								return model






							@slow
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Optional[int]			=       self.tokenizer(self.src_text   ,      return_tensors="tf"   )
								a   :	Union[str, Any]			=       self.model.generate(
								    model_inputs.input_ids   ,      )
								a   :	Optional[int]			=       self.tokenizer.batch_decode(generated_ids.numpy()   ,      skip_special_tokens=lowerCAmelCase__   )[0]
								assert (
								    generated_words
								    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
								)









"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


