
'''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 					A							:
     __magic_name__          =						BlenderbotSmallConfig
     __magic_name__          =						{}
     __magic_name__          =						'''gelu'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE=13		,     SCREAMING_SNAKE_CASE=7		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE=99		,     SCREAMING_SNAKE_CASE=32		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=37		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=20		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=0		,     )					->							Dict:






         """simple docstring"""






         A			:  Union[str, Any]   =		parent
         A			:  Optional[int]   =		batch_size
         A			:  str   =		seq_length
         A			:  int   =		is_training
         A			:  Tuple   =		use_labels
         A			:  str   =		vocab_size
         A			:  Tuple   =		hidden_size
         A			:  List[Any]   =		num_hidden_layers
         A			:  Tuple   =		num_attention_heads
         A			:  List[str]   =		intermediate_size

         A			:  List[Any]   =		hidden_dropout_prob
         A			:  str   =		attention_probs_dropout_prob
         A			:  Tuple   =		max_position_embeddings
         A			:  int   =		eos_token_id
         A			:  int   =		pad_token_id
         A			:  List[Any]   =		bos_token_id





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Dict   =		ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size      )
         A			:  Optional[int]   =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		,     1      )
         A			:  Dict   =		tf.concat([input_ids, eos_tensor]		,     axis=1      )

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

         A			:  Optional[Any]   =		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		,     )
         A			:  Dict   =		prepare_blenderbot_small_inputs_dict(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
         return config, inputs_dict





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							Any:






         """simple docstring"""






         A			:  List[Any]   =		TFBlenderbotSmallModel(config=SCREAMING_SNAKE_CASE      ).get_decoder()
         A			:  Optional[int]   =		inputs_dict['''input_ids''']

         A			:  Optional[Any]   =		input_ids[:1, :]
         A			:  Dict   =		inputs_dict['''attention_mask'''][:1, :]
         A			:  List[Any]   =		inputs_dict['''head_mask''']
         A			:  Dict   =		1

         # first forward pass
         A			:  int   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE		,     head_mask=SCREAMING_SNAKE_CASE		,     use_cache=SCREAMING_SNAKE_CASE      )

         A,    A			:  int   =		outputs.to_tuple()

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

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

         A			:  str   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE      )[0]
         A			:  int   =		model(SCREAMING_SNAKE_CASE		,     attention_mask=SCREAMING_SNAKE_CASE		,     past_key_values=SCREAMING_SNAKE_CASE      )[0]

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

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

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """simple docstring"""






         A			:  List[str]   =		TFBlenderbotSmallModelTester(self      )
         A			:  Optional[int]   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE      )





@require_tokenizers
@require_tf
class 					A							(				unittest.TestCase		):
     __magic_name__          =						[
         '''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?'''
     ]
     __magic_name__          =						'''facebook/blenderbot_small-90M'''





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






         """simple docstring"""






         return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )





     @cached_property
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  Optional[int]   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
         return model





     @slow
     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  int   =		self.tokenizer(self.src_text		,     return_tensors='''tf'''      )
         A			:  Optional[int]   =		self.model.generate(
             model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     use_cache=SCREAMING_SNAKE_CASE		,     )
         A			:  Any   =		self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=SCREAMING_SNAKE_CASE      )[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
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__="train"		):




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    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 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    for i in range(len(snake_case__		)		):
        print(('''Actual output value:''', output(snake_case__						,							'''test'''		))		)
        print(('''Hypothesis output:''', calculate_hypothesis_value(snake_case__						,							'''test'''		))		)


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




