






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

											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     max_position_embeddings
											snake_case__									=     eos_token_id
											snake_case__									=     pad_token_id
											snake_case__									=     bos_token_id
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											snake_case__									=     ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size						)
											snake_case__									=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       ,	1						)
											snake_case__									=     tf.concat([input_ids, eos_tensor]       ,	axis=1						)

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

											snake_case__									=     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__									=     prepare_blenderbot_inputs_dict(_A       ,	_A       ,	_A						)
											return config, inputs_dict


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

											snake_case__									=     input_ids[:1, :]
											snake_case__									=     inputs_dict['attention_mask'][:1, :]
											snake_case__									=     inputs_dict['head_mask']
											snake_case__									=     1

											# first forward pass
											snake_case__									=     model(_A       ,	attention_mask=_A       ,	head_mask=_A       ,	use_cache=_A						)

											snake_case__							,						snake_case__									=     outputs.to_tuple()

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

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

											snake_case__									=     model(_A       ,	attention_mask=_A						)[0]
											snake_case__									=     model(_A       ,	attention_mask=_A       ,	past_key_values=_A						)[0]

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

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

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


def      a_ (      _A      ,   _A      ,   _A      ,   _A=None      ,   _A=None      ,   _A=None      ,   _A=None      ,   _A=None      ,   )			->			List[str]:







				"""simple docstring"""
				if attention_mask is None:
								snake_case__									=     tf.cast(tf.math.not_equal(__snake_case      ,   config.pad_token_id  )      ,   tf.inta  )
				if decoder_attention_mask is None:
								snake_case__									=     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__									=     tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
				if decoder_head_mask is None:
								snake_case__									=     tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
				if cross_attn_head_mask is None:
								snake_case__									=     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   __SCREAMING_SNAKE_CASE(	__lowercase    ,  __lowercase    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							_UpperCAmelCase     =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							_UpperCAmelCase     =       (
							    {
							        "conversational": TFBlenderbotForConditionalGeneration,
							        "feature-extraction": TFBlenderbotModel,
							        "summarization": TFBlenderbotForConditionalGeneration,
							        "text2text-generation": TFBlenderbotForConditionalGeneration,
							        "translation": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											snake_case__									=     TFBlenderbotModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=_A						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		str:
											self.config_tester.run_common_tests()


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


@require_tokenizers
@require_tf
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							_UpperCAmelCase     =       ["My friends are cool but they eat too many carbs."]
							_UpperCAmelCase     =       "facebook/blenderbot-400M-distill"
							@cached_property
							def lowerCAmelCase_ (     self:      Any						)		->		List[Any]:
											return BlenderbotTokenizer.from_pretrained(self.model_name						)
							@cached_property
							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											return model


							@slow
							def lowerCAmelCase_ (     self:      Tuple						)		->		List[str]:
											snake_case__									=     self.tokenizer(self.src_text       ,	return_tensors='tf'						)
											snake_case__									=     self.model.generate(
											    model_inputs.input_ids       ,	)
											snake_case__									=     self.tokenizer.batch_decode(generated_ids.numpy()       ,	skip_special_tokens=_A						)[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					:		str	  =						{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""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					:		Any	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




