




"""simple docstring"""




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     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				BlenderbotConfig
    UpperCAmelCase__							  =				{}
    UpperCAmelCase__							  =				'''gelu'''


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





           '''simple docstring'''

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

           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   eos_token_id
           A__							=   pad_token_id
           A__							=   bos_token_id


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Union[str, Any]:





           '''simple docstring'''

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

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

           A__							=   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__							=   prepare_blenderbot_inputs_dict(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)
           return config, inputs_dict





    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     int)						->Dict:





           '''simple docstring'''

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

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

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

           A__			,							A__							=   outputs.to_tuple()

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

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

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

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    lowercase_=None			,    )       ->	Union[str, Any]:



       """simple docstring"""

       if attention_mask is None:
              A__							=   tf.cast(tf.math.not_equal(UpperCAmelCase_			,    config.pad_token_id						)			,    tf.inta						)
       if decoder_attention_mask is None:
              A__							=   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__							=   tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
       if decoder_head_mask is None:
              A__							=   tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
       if cross_attn_head_mask is None:
              A__							=   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     UpperCamelCase_   (			_lowerCamelCase		, _lowerCamelCase		, unittest.TestCase					):

    '''simple docstring'''






    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 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   TFBlenderbotModelTester(self)
           A__							=   ConfigTester(self ,			config_class=UpperCAmelCase__)


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





           '''simple docstring'''

           self.config_tester.run_common_tests()





    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->int:





           '''simple docstring'''

           A__							=   self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__)


@require_tokenizers
@require_tf
class     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				['''My friends are cool but they eat too many carbs.''']
    UpperCAmelCase__							  =				'''facebook/blenderbot-400M-distill'''


    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           return BlenderbotTokenizer.from_pretrained(self.model_name)


    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->int:





           '''simple docstring'''

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





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   self.tokenizer(self.src_text ,			return_tensors='''tf''')
           A__							=   self.model.generate(
               model_inputs.input_ids ,			)
           A__							=   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 __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       # Display within reasonable bounds
       A__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       plt.ylim(-2 * pi			,    2 * pi						)
       plt.ylabel('''Phase shift (Radians)'''						)
       plt.plot(np.unwrap(lowercase_			,    -2 * pi						)						)
       plt.show()


