'''simple docstring'''




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow





class 					__magic_name__		:







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Any       ,    lowercase_       :						int					):
										return None





class 					__magic_name__		:







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Dict       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						List[Any]					):
										return None





class 					__magic_name__		(					unittest.TestCase):
			UpperCamelCase__       = [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]







			@require_tf
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(lowercase_       ,    """tf"""       ,    12       ,    **lowercase_					)







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(lowercase_       ,    """pt"""       ,    12       ,    **lowercase_					)







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										from transformers import BertModel

										lowercase_      :    str       		= ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
										with NamedTemporaryFile(mode="""w+t"""					) as vocab_file:
																	vocab_file.write("""\n""".join(lowercase_					)					)
																	vocab_file.flush()
																	lowercase_      :    str       		= BertTokenizerFast(vocab_file.name					)

										with TemporaryDirectory() as bert_save_dir:
																	lowercase_      :    Any       		= BertModel(BertConfig(vocab_size=len(lowercase_					)					)					)
																	model.save_pretrained(lowercase_					)
																	self._test_export(lowercase_       ,    """pt"""       ,    12       ,    lowercase_					)







			@require_tf
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	lowercase_      :    Optional[int]       		= self._test_export(lowercase_       ,    """tf"""       ,    12       ,    **lowercase_					)
																	lowercase_      :    Dict       		= quantize(Path(lowercase_					)					)

																	# Ensure the actual quantized model is not bigger than the original one
																	if quantized_path.stat().st_size >= Path(lowercase_					).stat().st_size:
																								self.fail("""Quantized model is bigger than initial ONNX model"""					)







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	lowercase_      :    Union[str, Any]       		= self._test_export(lowercase_       ,    """pt"""       ,    12       ,    **lowercase_					)
																	lowercase_      :    List[Any]       		= quantize(lowercase_					)

																	# Ensure the actual quantized model is not bigger than the original one
																	if quantized_path.stat().st_size >= Path(lowercase_					).stat().st_size:
																								self.fail("""Quantized model is bigger than initial ONNX model"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						List[str]       ,    lowercase_       :						int       ,    lowercase_       :						List[str]       ,    lowercase_       :						List[str]=None       ,    **lowercase_       :						Tuple					):
										try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																								lowercase_      :    List[str]       		= Path(lowercase_					).joinpath("""model.onnx"""					)

																	# Remove folder if exists
																	if path.parent.exists():
																								path.parent.rmdir()

																	# Export
																	convert(lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    lowercase_       ,    **lowercase_					)

																	return path
										except Exception as e:
																	self.fail(lowercase_					)







			@require_torch
			@require_tokenizers
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										from transformers import BertModel

										lowercase_      :    List[str]       		= BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
										lowercase_      :    Any       		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
										self._test_infer_dynamic_axis(lowercase_       ,    lowercase_       ,    """pt"""					)







			@require_tf
			@require_tokenizers
			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										from transformers import TFBertModel

										lowercase_      :    Any       		= TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
										lowercase_      :    str       		= BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
										self._test_infer_dynamic_axis(lowercase_       ,    lowercase_       ,    """tf"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Tuple       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= FeatureExtractionPipeline(lowercase_       ,    lowercase_					)

										lowercase_      :    List[Any]       		= ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
										lowercase_		,		lowercase_		,		lowercase_		,		lowercase_      :    Any       		= infer_shapes(lowercase_       ,    lowercase_					)

										# Assert all variables are present
										self.assertEqual(len(lowercase_					)       ,    len(lowercase_					)					)
										self.assertTrue(all(var_name in shapes for var_name in variable_names					)					)
										self.assertSequenceEqual(variable_names[:3]       ,    lowercase_					)
										self.assertSequenceEqual(variable_names[3:]       ,    lowercase_					)

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																	self.assertDictEqual(shapes[var_name]       ,    {0: """batch""", 1: """sequence"""}					)

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["""output_0"""]       ,    {0: """batch""", 1: """sequence"""}					)
										self.assertDictEqual(shapes["""output_1"""]       ,    {0: """batch"""}					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    int       		= ["""input_ids""", """attention_mask""", """token_type_ids"""]
										lowercase_      :    List[Any]       		= {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
										lowercase_		,		lowercase_      :    Optional[Any]       		= ensure_valid_input(FuncContiguousArgs()       ,    lowercase_       ,    lowercase_					)

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(lowercase_					)       ,    3					)

										# Should have exactly the same input names
										self.assertEqual(set(lowercase_					)       ,    set(lowercase_					)					)

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(lowercase_       ,    (tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])					)

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										lowercase_		,		lowercase_      :    List[Any]       		= ensure_valid_input(FuncNonContiguousArgs()       ,    lowercase_       ,    lowercase_					)

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(lowercase_					)       ,    1					)
										self.assertEqual(len(lowercase_					)       ,    1					)

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]       ,    tokens["""input_ids"""]					)
										self.assertEqual(ordered_input_names[0]       ,    """input_ids"""					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    Tuple       		= generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""					)       ,    """-test"""					)
										self.assertEqual("""/home/something/my_fake_model-test.onnx"""       ,    generated.as_posix()					)






'''simple docstring'''




from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 - _cos) / 2
							lowercase_      :    Optional[int]       		= 1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    List[Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    Optional[int]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Dict       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= _sin / (2 * q_factor)

							lowercase_      :    Dict       		= (1 + _cos) / 2
							lowercase_      :    str       		= -1 - _cos

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    List[Any]       		= 1 - alpha

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    int       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    Union[str, Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    str       		= _sin / (2 * q_factor)

							lowercase_      :    str       		= _sin / 2
							lowercase_      :    Any       		= 0
							lowercase_      :    Optional[Any]       		= -ba

							lowercase_      :    Dict       		= 1 + alpha
							lowercase_      :    Union[str, Any]       		= -2 * _cos
							lowercase_      :    Union[str, Any]       		= 1 - alpha

							lowercase_      :    Tuple       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)				)    ->   IIRFilter:
							lowercase_      :    List[str]       		= tau * frequency / samplerate
							lowercase_      :    Any       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    Optional[Any]       		= _sin / (2 * q_factor)

							lowercase_      :    Any       		= 1 - alpha
							lowercase_      :    Optional[Any]       		= -2 * _cos
							lowercase_      :    Optional[int]       		= 1 + alpha

							lowercase_      :    Dict       		= IIRFilter(2				)
							filt.set_coefficients([ba, ba, ba]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Tuple       		= sin(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= cos(UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)

							lowercase_      :    List[str]       		= 1 + alpha * big_a
							lowercase_      :    List[Any]       		= -2 * _cos
							lowercase_      :    Dict       		= 1 - alpha * big_a
							lowercase_      :    str       		= 1 + alpha / big_a
							lowercase_      :    List[str]       		= -2 * _cos
							lowercase_      :    Tuple       		= 1 - alpha / big_a

							lowercase_      :    Any       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    Dict       		= tau * frequency / samplerate
							lowercase_      :    Union[str, Any]       		= sin(UpperCAmelCase__				)
							lowercase_      :    Any       		= cos(UpperCAmelCase__				)
							lowercase_      :    Any       		= _sin / (2 * q_factor)
							lowercase_      :    Any       		= 10 ** (gain_db / 40)
							lowercase_      :    Any       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    int       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Tuple       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    Optional[Any]       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    int       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (pmc + aaa)
							lowercase_      :    List[str]       		= 2 * big_a * mpc
							lowercase_      :    Union[str, Any]       		= big_a * (pmc - aaa)
							lowercase_      :    Optional[int]       		= ppmc + aaa
							lowercase_      :    Optional[int]       		= -2 * pmpc
							lowercase_      :    Any       		= ppmc - aaa

							lowercase_      :    Optional[int]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt




def 							lowerCamelCase			(   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float = 1 / sqrt(2				)   ,   )    ->   IIRFilter:
							lowercase_      :    str       		= tau * frequency / samplerate
							lowercase_      :    int       		= sin(UpperCAmelCase__				)
							lowercase_      :    int       		= cos(UpperCAmelCase__				)
							lowercase_      :    Dict       		= _sin / (2 * q_factor)
							lowercase_      :    Union[str, Any]       		= 10 ** (gain_db / 40)
							lowercase_      :    Union[str, Any]       		= (big_a + 1) - (big_a - 1) * _cos
							lowercase_      :    Optional[int]       		= (big_a + 1) + (big_a - 1) * _cos
							lowercase_      :    Any       		= (big_a - 1) - (big_a + 1) * _cos
							lowercase_      :    str       		= (big_a - 1) + (big_a + 1) * _cos
							lowercase_      :    Optional[int]       		= 2 * sqrt(UpperCAmelCase__				) * alpha

							lowercase_      :    Tuple       		= big_a * (ppmc + aaa)
							lowercase_      :    List[Any]       		= -2 * big_a * pmpc
							lowercase_      :    Optional[Any]       		= big_a * (ppmc - aaa)
							lowercase_      :    Optional[Any]       		= pmc + aaa
							lowercase_      :    int       		= 2 * mpc
							lowercase_      :    Tuple       		= pmc - aaa

							lowercase_      :    Union[str, Any]       		= IIRFilter(2				)
							filt.set_coefficients([aa, aa, aa]   ,   [ba, ba, ba]				)
							return filt





