






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							float:
							return 0.0





def      lowercase__      (     __snake_case    :							np.ndarray     ,       __snake_case    :							int  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							FilterType     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						512
					UpperCAmelCase_    :      str            =						[1] + [0] * (size - 1)
					UpperCAmelCase_    :      Optional[Any]            =						[filter_type.process(__snake_case  ) for item in inputs]

					UpperCAmelCase_    :      Dict            =						[0] * (samplerate - size)  # zero-padding
					outputs += filler
					UpperCAmelCase_    :      Optional[int]            =						np.abs(np.fft.fft(__snake_case  )  )
					UpperCAmelCase_    :      List[str]            =						20 * np.logaa(__snake_case  )

					# 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
					UpperCAmelCase_    :      Union[str, Any]            =						get_bounds(__snake_case     ,       __snake_case  )
					plt.ylim(max([-80, bounds[0]]  )     ,       min([80, bounds[1]]  )  )
					plt.ylabel('Gain (dB)'  )

					plt.plot(__snake_case  )
					plt.show()






def      lowercase__      (     __snake_case    :							FilterType     ,       __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						512
					UpperCAmelCase_    :      Tuple            =						[1] + [0] * (size - 1)
					UpperCAmelCase_    :      Tuple            =						[filter_type.process(__snake_case  ) for item in inputs]

					UpperCAmelCase_    :      List[str]            =						[0] * (samplerate - size)  # zero-padding
					outputs += filler
					UpperCAmelCase_    :      Dict            =						np.angle(np.fft.fft(__snake_case  )  )

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












import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

