import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
    )




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Any    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				str=13    ,  _UpperCamelCase     :				Any=7    ,  _UpperCamelCase     :				Optional[Any]=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Any=False    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				List[str]=99    ,  _UpperCamelCase     :				str=32    ,  _UpperCamelCase     :				Optional[Any]=5    ,  _UpperCamelCase     :				str=4    ,  _UpperCamelCase     :				Optional[Any]=64    ,  _UpperCamelCase     :				Dict="gelu"    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				str=512    ,  _UpperCamelCase     :				Tuple=16    ,  _UpperCamelCase     :				Any=2    ,  _UpperCamelCase     :				Tuple=0.0_2    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				List[Any]=4    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				Any=2    ,  _UpperCamelCase     :				Optional[Any]=2    ,  _UpperCamelCase     :				int=2    ,  _UpperCamelCase     :				Tuple=2    ,  _UpperCamelCase     :				str=4    ,  _UpperCamelCase     :				List[str]=1    ,  )				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						parent
          _lowerCamelCase     :		Tuple 						=						batch_size
          _lowerCamelCase     :		Optional[int] 						=						seq_length
          _lowerCamelCase     :		List[str] 						=						is_training
          _lowerCamelCase     :		Union[str, Any] 						=						use_input_mask
          _lowerCamelCase     :		Tuple 						=						use_token_type_ids
          _lowerCamelCase     :		Optional[Any] 						=						use_labels
          _lowerCamelCase     :		str 						=						vocab_size
          _lowerCamelCase     :		Optional[Any] 						=						hidden_size
          _lowerCamelCase     :		Union[str, Any] 						=						num_hidden_layers
          _lowerCamelCase     :		List[str] 						=						num_attention_heads
          _lowerCamelCase     :		Dict 						=						intermediate_size
          _lowerCamelCase     :		Optional[int] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						hidden_dropout_prob
          _lowerCamelCase     :		List[Any] 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		int 						=						max_position_embeddings
          _lowerCamelCase     :		Union[str, Any] 						=						type_vocab_size
          _lowerCamelCase     :		Optional[Any] 						=						type_sequence_label_size
          _lowerCamelCase     :		Dict 						=						initializer_range
          _lowerCamelCase     :		Optional[Any] 						=						num_labels
          _lowerCamelCase     :		List[str] 						=						num_choices
          _lowerCamelCase     :		Optional[Any] 						=						scope
          _lowerCamelCase     :		int 						=						q_groups
          _lowerCamelCase     :		Union[str, Any] 						=						k_groups
          _lowerCamelCase     :		List[str] 						=						v_groups
          _lowerCamelCase     :		Union[str, Any] 						=						post_attention_groups
          _lowerCamelCase     :		List[Any] 						=						intermediate_groups
          _lowerCamelCase     :		int 						=						output_groups



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

          _lowerCamelCase     :		int 						=						None
          if self.use_input_mask:
              _lowerCamelCase     :		List[Any] 						=						random_attention_mask([self.batch_size, self.seq_length])

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Dict 						=						None
          _lowerCamelCase     :		List[Any] 						=						None
          if self.use_labels:
              _lowerCamelCase     :		Tuple 						=						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size)
              _lowerCamelCase     :		str 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels)
              _lowerCamelCase     :		Any 						=						ids_tensor([self.batch_size]    ,  self.num_choices)

          _lowerCamelCase     :		Tuple 						=						self.get_config()

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          return SqueezeBertConfig(
              embedding_size=self.hidden_size    ,  vocab_size=self.vocab_size    ,  hidden_size=self.hidden_size    ,  num_hidden_layers=self.num_hidden_layers    ,  num_attention_heads=self.num_attention_heads    ,  intermediate_size=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  attention_probs_dropout_prob=self.hidden_dropout_prob    ,  attention_dropout=self.attention_probs_dropout_prob    ,  max_position_embeddings=self.max_position_embeddings    ,  initializer_range=self.initializer_range    ,  q_groups=self.q_groups    ,  k_groups=self.k_groups    ,  v_groups=self.v_groups    ,  post_attention_groups=self.post_attention_groups    ,  intermediate_groups=self.intermediate_groups    ,  output_groups=self.output_groups    ,  )



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						SqueezeBertModel(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Tuple 						=						model(_UpperCamelCase    ,  _UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						model(_UpperCamelCase)
          self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Tuple)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						SqueezeBertForMaskedLM(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Any)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						SqueezeBertForQuestionAnswering(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Optional[int] 						=						model(
              _UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  start_positions=_UpperCamelCase    ,  end_positions=_UpperCamelCase)
          self.parent.assertEqual(result.start_logits.shape    ,  (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape    ,  (self.batch_size, self.seq_length))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.num_labels
          _lowerCamelCase     :		Optional[int] 						=						SqueezeBertForSequenceClassification(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Optional[int] 						=						model(_UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.num_labels
          _lowerCamelCase     :		str 						=						SqueezeBertForTokenClassification(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()

          _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  labels=_UpperCamelCase)
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.num_labels))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.num_choices
          _lowerCamelCase     :		Tuple 						=						SqueezeBertForMultipleChoice(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Optional[int] 						=						input_ids.unsqueeze(1).expand(-1    ,  self.num_choices    ,  -1).contiguous()
          _lowerCamelCase     :		Dict 						=						input_mask.unsqueeze(1).expand(-1    ,  self.num_choices    ,  -1).contiguous()
          _lowerCamelCase     :		Optional[int] 						=						model(
              _UpperCamelCase    ,  attention_mask=_UpperCamelCase    ,  labels=_UpperCamelCase    ,  )
          self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_choices))




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.prepare_config_and_inputs()
          (_lowerCamelCase)     :		List[str] 						=						config_and_inputs
          _lowerCamelCase     :		Dict 						=						{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict





@require_torch
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (
          (
              SqueezeBertModel,
              SqueezeBertForMaskedLM,
              SqueezeBertForMultipleChoice,
              SqueezeBertForQuestionAnswering,
              SqueezeBertForSequenceClassification,
              SqueezeBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      _snake_case    		=       (
          {
              'feature-extraction': SqueezeBertModel,
              'fill-mask': SqueezeBertForMaskedLM,
              'question-answering': SqueezeBertForQuestionAnswering,
              'text-classification': SqueezeBertForSequenceClassification,
              'token-classification': SqueezeBertForTokenClassification,
              'zero-shot': SqueezeBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _snake_case    		=       False
      _snake_case    		=       True
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						SqueezeBertModelTester(self)
          _lowerCamelCase     :		List[str] 						=						ConfigTester(self    ,  config_class=_UpperCamelCase    ,  dim=37)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          self.config_tester.run_common_tests()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_model(*_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCamelCase)



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


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCamelCase)




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[str]:


          """simple docstring"""


          for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowerCamelCase     :		Tuple 						=						SqueezeBertModel.from_pretrained(_UpperCamelCase)
              self.assertIsNotNone(_UpperCamelCase)





@require_sentencepiece
@require_tokenizers
@require_torch
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""")

          _lowerCamelCase     :		Any 						=						torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]])
          _lowerCamelCase     :		int 						=						model(_UpperCamelCase)[0]
          _lowerCamelCase     :		List[str] 						=						torch.Size((1, 3))
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
          self.assertTrue(torch.allclose(_UpperCamelCase    ,  _UpperCamelCase    ,  atol=1E-4))





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # 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
    _lowerCamelCase     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Any 						=						np.angle(np.fft.fft(__A	)	)

    # 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(__A   , -2 * pi	)	)
    plt.show()




