



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     __lowerCAmelCase					:
			UpperCamelCase           =  BlenderbotConfig
			UpperCamelCase           =  {}
			UpperCamelCase           =  '''gelu'''



			def __init__(		self     :						List[str]				,	A     :						Tuple				,	A     :						Dict=13				,	A     :						Union[str, Any]=7				,	A     :						Optional[int]=True				,	A     :						int=False				,	A     :						str=99				,	A     :						Optional[int]=32				,	A     :						Optional[Any]=2				,	A     :						Dict=4				,	A     :						Union[str, Any]=37				,	A     :						Tuple=0.1				,	A     :						Optional[Any]=0.1				,	A     :						List[str]=20				,	A     :						Any=2				,	A     :						Union[str, Any]=1				,	A     :						List[str]=0				,	)     ->					List[Any]:



						"""simple docstring"""






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

						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     attention_probs_dropout_prob
						_UpperCAmelCase	 =     max_position_embeddings
						_UpperCAmelCase	 =     eos_token_id
						_UpperCAmelCase	 =     pad_token_id
						_UpperCAmelCase	 =     bos_token_id



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[str]:



						"""simple docstring"""






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

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

						_UpperCAmelCase	 =     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				,	)
						_UpperCAmelCase	 =     prepare_blenderbot_inputs_dict(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase)
						return config, inputs_dict



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						Union[str, Any]				,	A     :						List[str])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     TFBlenderbotModel(config=_UpperCAmelCase).get_decoder()
						_UpperCAmelCase	 =     inputs_dict['''input_ids''']

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

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

						_UpperCAmelCase	 =     outputs.to_tuple()

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

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

						_UpperCAmelCase	 =     model(_UpperCAmelCase				,	attention_mask=_UpperCAmelCase)[0]
						_UpperCAmelCase	 =     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
						_UpperCAmelCase	 =     int(ids_tensor((1,)				,	output_from_past.shape[-1]))
						_UpperCAmelCase	 =     output_from_no_past[:, -3:, random_slice_idx]
						_UpperCAmelCase	 =     output_from_past[:, :, random_slice_idx]

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





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     List[Any]=None							,     _UpperCAmelCase       :     Union[str, Any]=None							,     _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     Any=None							,     )       ->				int:







			'''simple docstring'''
			if attention_mask is None:
						_UpperCAmelCase	 =     tf.cast(tf.math.not_equal(a_							,     config.pad_token_id					)							,     tf.inta					)
			if decoder_attention_mask is None:
						_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
			if decoder_head_mask is None:
						_UpperCAmelCase	 =     tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
			if cross_attn_head_mask is None:
						_UpperCAmelCase	 =     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     __lowerCAmelCase					(    _UpperCamelCase       ,		_UpperCamelCase       ,		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     :						int)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     TFBlenderbotModelTester(self)
						_UpperCAmelCase	 =     ConfigTester(self				,	config_class=_UpperCAmelCase)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def 					_lowerCamelCase (		self     :						Dict)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase)







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



			@cached_property
			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						return BlenderbotTokenizer.from_pretrained(self.model_name)



			@cached_property
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






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



			@slow
			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.tokenizer(self.src_text				,	return_tensors='tf')
						_UpperCAmelCase	 =     self.model.generate(
						    model_inputs.input_ids				,	)
						_UpperCAmelCase	 =     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_torch_available, is_vision_available


UpperCAmelCase__     = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = ["YolosFeatureExtractor"]
							UpperCAmelCase__     = ["YolosImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "YolosForObjectDetection",
							    "YolosModel",
							    "YolosPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_yolos import YolosFeatureExtractor
														from .image_processing_yolos import YolosImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yolos import (
														    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YolosForObjectDetection,
														    YolosModel,
														    YolosPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
