
'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel


@require_tf
class 			lowerCAmelCase :
		a							:					Dict       =					BlenderbotSmallConfig
		a							:					Dict       =					{}
		a							:					Tuple       =					"""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					,						):
					_SCREAMING_SNAKE_CASE				= parent
					_SCREAMING_SNAKE_CASE				= batch_size
					_SCREAMING_SNAKE_CASE				= seq_length
					_SCREAMING_SNAKE_CASE				= is_training
					_SCREAMING_SNAKE_CASE				= use_labels
					_SCREAMING_SNAKE_CASE				= vocab_size
					_SCREAMING_SNAKE_CASE				= hidden_size
					_SCREAMING_SNAKE_CASE				= num_hidden_layers
					_SCREAMING_SNAKE_CASE				= num_attention_heads
					_SCREAMING_SNAKE_CASE				= intermediate_size

					_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
					_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
					_SCREAMING_SNAKE_CASE				= max_position_embeddings
					_SCREAMING_SNAKE_CASE				= eos_token_id
					_SCREAMING_SNAKE_CASE				= pad_token_id
					_SCREAMING_SNAKE_CASE				= bos_token_id




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

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

					_SCREAMING_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					,						)
					_SCREAMING_SNAKE_CASE				= prepare_blenderbot_small_inputs_dict(__A					,						__A					,						__A      )
					return config, inputs_dict




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
					_SCREAMING_SNAKE_CASE				= TFBlenderbotSmallModel(config=__A      ).get_decoder()
					_SCREAMING_SNAKE_CASE				= inputs_dict["input_ids"]

					_SCREAMING_SNAKE_CASE				= input_ids[:1, :]
					_SCREAMING_SNAKE_CASE				= inputs_dict["attention_mask"][:1, :]
					_SCREAMING_SNAKE_CASE				= inputs_dict["head_mask"]
					_SCREAMING_SNAKE_CASE				= 1

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

					_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= outputs.to_tuple()

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

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

					_SCREAMING_SNAKE_CASE				= model(__A					,						attention_mask=__A      )[0]
					_SCREAMING_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
					_SCREAMING_SNAKE_CASE				= int(ids_tensor((1,)					,						output_from_past.shape[-1]      )      )
					_SCREAMING_SNAKE_CASE				= output_from_no_past[:, -3:, random_slice_idx]
					_SCREAMING_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		(	_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=None	,			_SCREAMING_SNAKE_CASE		:   int=None	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=None	,			_SCREAMING_SNAKE_CASE		:   List[str]=None	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=None	,			):
			if attention_mask is None:
						_SCREAMING_SNAKE_CASE				= tf.cast(tf.math.not_equal(UpperCamelCase__	,			config.pad_token_id  )	,			tf.inta  )
			if decoder_attention_mask is None:
						_SCREAMING_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:
						_SCREAMING_SNAKE_CASE				= tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
			if decoder_head_mask is None:
						_SCREAMING_SNAKE_CASE				= tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
			if cross_attn_head_mask is None:
						_SCREAMING_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 			lowerCAmelCase (				UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase			):
		a							:					Dict       =					(
		    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
		)
		a							:					int       =					(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
		a							:					Tuple       =					(
		    {
		        """conversational""": TFBlenderbotSmallForConditionalGeneration,
		        """feature-extraction""": TFBlenderbotSmallModel,
		        """summarization""": TFBlenderbotSmallForConditionalGeneration,
		        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
		        """translation""": TFBlenderbotSmallForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		a							:					Optional[int]       =					True
		a							:					Tuple       =					False
		a							:					Optional[Any]       =					False




		def     lowercase       (				self      ):
					_SCREAMING_SNAKE_CASE				= TFBlenderbotSmallModelTester(self      )
					_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=__A      )




		def     lowercase       (				self      ):
					self.config_tester.run_common_tests()




		def     lowercase       (				self      ):
					_SCREAMING_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 			lowerCAmelCase (				unittest.TestCase			):
		a							:					str       =					[
		    """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
		    """  i'm going to throw up.\nand why is that?"""
		]
		a							:					Optional[int]       =					"""facebook/blenderbot_small-90M"""




		@cached_property
		def     lowercase       (				self      ):
					return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"      )




		@cached_property
		def     lowercase       (				self      ):
					_SCREAMING_SNAKE_CASE				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
					return model




		@slow
		def     lowercase       (				self      ):
					_SCREAMING_SNAKE_CASE				= self.tokenizer(self.src_text					,						return_tensors="tf"      )
					_SCREAMING_SNAKE_CASE				= self.model.generate(
					    model_inputs.input_ids					,						attention_mask=model_inputs.attention_mask					,						num_beams=2					,						use_cache=__A					,						)
					_SCREAMING_SNAKE_CASE				= self.tokenizer.batch_decode(generated_ids.numpy()					,						skip_special_tokens=__A      )[0]
					assert generated_words in (
					    "i don't know. i just feel like i'm going to throw up. it's not fun.",
					    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
					    "i'm not sure. i just feel like i've been in a bad situation.",
					)

'''simple docstring'''







import numpy

# List of input, output pairs
_snake_case				:   Optional[Any]     =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case				:   Any     =				(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case				:   Any     =				[2, 4, 1, 5]
_snake_case				:   str     =				len(train_data)
_snake_case				:   int     =				0.009



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]  )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]  )
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=m  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(_SCREAMING_SNAKE_CASE  ):
												if index == -1:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  )
												else:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  ) * train_data[i][0][index]
						return summation_value



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



def 			_a		(	):
						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												print(("Actual output value:", output(_SCREAMING_SNAKE_CASE	,			"test"  ))  )
												print(("Hypothesis output:", calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			"test"  ))  )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()