






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 					_snake_case						:
	_A          =   BlenderbotConfig
	_A          =   {}
	_A          =   'gelu'






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

					snake_case__      :List[Any]         =   hidden_dropout_prob
					snake_case__      :List[str]         =   attention_probs_dropout_prob
					snake_case__      :Optional[int]         =   max_position_embeddings
					snake_case__      :str         =   eos_token_id
					snake_case__      :Any         =   pad_token_id
					snake_case__      :Optional[Any]         =   bos_token_id






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[Any]         =   ids_tensor([self.batch_size, self.seq_length - 1]					,self.vocab_size       )
					snake_case__      :Optional[int]         =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )					,1       )
					snake_case__      :Optional[int]         =   tf.concat([input_ids, eos_tensor]					,axis=1       )

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

					snake_case__      :Dict         =   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					,)
					snake_case__      :str         =   prepare_blenderbot_inputs_dict(UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )
					return config, inputs_dict






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					snake_case__      :int         =   TFBlenderbotModel(config=UpperCamelCase       ).get_decoder()
					snake_case__      :Optional[int]         =   inputs_dict["input_ids"]

					snake_case__      :List[Any]         =   input_ids[:1, :]
					snake_case__      :Union[str, Any]         =   inputs_dict["attention_mask"][:1, :]
					snake_case__      :Any         =   inputs_dict["head_mask"]
					snake_case__      :List[str]         =   1

					# first forward pass
					snake_case__      :Optional[int]         =   model(UpperCamelCase					,attention_mask=UpperCamelCase					,head_mask=UpperCamelCase					,use_cache=UpperCamelCase       )

					snake_case__    ,						snake_case__      :Union[str, Any]         =   outputs.to_tuple()

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

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

					snake_case__      :Union[str, Any]         =   model(UpperCamelCase					,attention_mask=UpperCamelCase       )[0]
					snake_case__      :int         =   model(UpperCamelCase					,attention_mask=UpperCamelCase					,past_key_values=UpperCamelCase       )[0]

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

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

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





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Any	,	__snake_case     :							Optional[Any]	,	__snake_case     :							List[str]=None	,	__snake_case     :							List[str]=None	,	__snake_case     :							str=None	,	__snake_case     :							List[str]=None	,	__snake_case     :							Optional[int]=None	,	)     -> Tuple:





				'''simple docstring'''


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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Optional[int]         =   TFBlenderbotModelTester(self       )
					snake_case__      :int         =   ConfigTester(self					,config_class=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					self.config_tester.run_common_tests()






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Any         =   self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase       )




@require_tokenizers
@require_tf
class 					_snake_case						(			unittest.TestCase ):
	_A          =   ['My friends are cool but they eat too many carbs.']
	_A          =   'facebook/blenderbot-400M-distill'






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					return BlenderbotTokenizer.from_pretrained(self.model_name       )






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Union[str, Any]         =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
					return model






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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase							:   Tuple							=  {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   List[Any]							=  ["BartphoTokenizer"]

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	__UpperCAmelCase							:   Tuple							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)