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      :     List[str] ,			_A      :     Union[str, Any]=13 ,			_A      :     List[Any]=7 ,			_A      :     Any=True ,			_A      :     Tuple=False ,			_A      :     str=99 ,			_A      :     int=32 ,			_A      :     Optional[Any]=2 ,			_A      :     Optional[int]=4 ,			_A      :     Optional[Any]=37 ,			_A      :     Any=0.1 ,			_A      :     int=0.1 ,			_A      :     Union[str, Any]=20 ,			_A      :     List[str]=2 ,			_A      :     Tuple=1 ,			_A      :     Any=0 ,			):
												_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  UpperCamelCase_						(   self      :     str		):
												_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(_A ,			_A ,			_A		)
												return config, inputs_dict







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Union[str, Any] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFBlenderbotModel(config=_A		).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(_A ,			attention_mask=_A ,			head_mask=_A ,			use_cache=_A		)

												_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(_A ,			attention_mask=_A		)[0]
												_UpperCamelCase			     =     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
												_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(_A ,			_A ,			rtol=1e-3		)




def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     __snake_case=None  ,     ):
					if attention_mask is None:
										_UpperCamelCase			     =     tf.cast(tf.math.not_equal(__snake_case  ,     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_     (       __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  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFBlenderbotModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_A		)




@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  UpperCamelCase_						(   self      :     List[str]		):
												return BlenderbotTokenizer.from_pretrained(self.model_name		)







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
												return model







							@slow
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_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=_A		)[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					if self.task == "image-classification":
								return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
								return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )





		@property
		def   _UpperCamelCase( self   )      ->	float:

					"""simple docstring"""



					return 1E-4

