
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


SCREAMING_SNAKE_CASE							:Union[str, Any]										=		random.Random()


def 						UpperCAmelCase      (     a_		,       a_=1.0		,       a_=None		,       a_=None						)		->     Any:







				"""simple docstring"""



				if rng is None:
								__A             =			global_rng

				__A             =			[]
				for batch_idx in range(shape[0]						):
								values.append([]						)
								for _ in range(shape[1]						):
												values[-1].append(rng.random() * scale						)

				return values





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(	self :			Any							,A :			Union[str, Any]							,A :			str=7							,A :			Dict=4_00							,A :			Dict=20_00							,A :			str=1							,A :			Dict=0.0							,A :			str=1_60_00							,A :			List[str]=True							,A :			str=True							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			min_seq_length
					__A             =			max_seq_length
					__A             =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__A             =			feature_size
					__A             =			padding_value
					__A             =			sampling_rate
					__A             =			return_attention_mask
					__A             =			do_normalize






	def     UpperCamelCase_  (	self :			List[str]			):
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







	def     UpperCamelCase_  (	self :			List[Any]							,A :			int=False							,A :			Optional[Any]=False			):


					def _flatten(A :			Optional[Any]			):
									return list(itertools.chain(*_a			)			)

					if equal_length:
									__A             =			floats_list((self.batch_size, self.max_seq_length)			)
					else:
									# make sure that inputs increase in size
									__A             =			[
									    _flatten(floats_list((x, self.feature_size)			)			)
									    for x in range(self.min_seq_length							,self.max_seq_length							,self.seq_length_diff			)
									]

					if numpify:
									__A             =			[np.asarray(_a			) for x in speech_inputs]

					return speech_inputs





class 							UpperCAmelCase				(  UpperCamelCase_      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					WavaVecaFeatureExtractor






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			WavaVecaFeatureExtractionTester(self			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Union[str, Any]			):
					self.assertTrue(np.all(np.mean(_a							,axis=0			) < 1E-3			)			)
					self.assertTrue(np.all(np.abs(np.var(_a							,axis=0			) - 1			) < 1E-3			)			)






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					# create three inputs of length 800, 1000, and 1200
					__A             =			[floats_list((1, x)			)[0] for x in range(8_00							,14_00							,2_00			)]
					__A             =			[np.asarray(_a			) for speech_input in speech_inputs]

					# Test not batched input
					__A             =			feat_extract(speech_inputs[0]							,return_tensors="np"			).input_values
					__A             =			feat_extract(np_speech_inputs[0]							,return_tensors="np"			).input_values
					self.assertTrue(np.allclose(_a							,_a							,atol=1E-3			)			)

					# Test batched
					__A             =			feat_extract(_a							,return_tensors="np"			).input_values
					__A             =			feat_extract(_a							,return_tensors="np"			).input_values
					for enc_seq_a, enc_seq_a in zip(_a							,_a			):
									self.assertTrue(np.allclose(_a							,_a							,atol=1E-3			)			)

					# Test 2-D numpy arrays are batched.
					__A             =			[floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
					__A             =			np.asarray(_a			)
					__A             =			feat_extract(_a							,return_tensors="np"			).input_values
					__A             =			feat_extract(_a							,return_tensors="np"			).input_values
					for enc_seq_a, enc_seq_a in zip(_a							,_a			):
									self.assertTrue(np.allclose(_a							,_a							,atol=1E-3			)			)






	def     UpperCamelCase_  (	self :			str			):
					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					__A             =			[floats_list((1, x)			)[0] for x in range(8_00							,14_00							,2_00			)]

					__A             =			["""longest""", """max_length""", """do_not_pad"""]
					__A             =			[None, 16_00, None]
					for max_length, padding in zip(_a							,_a			):
									__A             =			feat_extract(_a							,padding=_a							,max_length=_a							,return_tensors="np"			)
									__A             =			processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]			)
									self.assertTrue(input_values[0][8_00:].sum() < 1E-6			)
									self._check_zero_mean_unit_variance(input_values[1][:10_00]			)
									self.assertTrue(input_values[0][10_00:].sum() < 1E-6			)
									self._check_zero_mean_unit_variance(input_values[2][:12_00]			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					__A             =			range(8_00							,14_00							,2_00			)
					__A             =			[floats_list((1, x)			)[0] for x in lengths]

					__A             =			["""longest""", """max_length""", """do_not_pad"""]
					__A             =			[None, 16_00, None]

					for max_length, padding in zip(_a							,_a			):
									__A             =			feat_extract(_a							,max_length=_a							,padding=_a			)
									__A             =			processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]			)
									self._check_zero_mean_unit_variance(input_values[1][:10_00]			)
									self._check_zero_mean_unit_variance(input_values[2][:12_00]			)






	def     UpperCamelCase_  (	self :			Any			):
					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					__A             =			[floats_list((1, x)			)[0] for x in range(8_00							,14_00							,2_00			)]
					__A             =			feat_extract(
					    _a							,truncation=_a							,max_length=10_00							,padding="max_length"							,return_tensors="np"			)
					__A             =			processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]			)
					self._check_zero_mean_unit_variance(input_values[1]			)
					self._check_zero_mean_unit_variance(input_values[2]			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					__A             =			[floats_list((1, x)			)[0] for x in range(8_00							,14_00							,2_00			)]
					__A             =			feat_extract(
					    _a							,truncation=_a							,max_length=10_00							,padding="longest"							,return_tensors="np"			)
					__A             =			processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]			)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]			)
					self._check_zero_mean_unit_variance(input_values[2]			)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertTrue(input_values.shape == (3, 10_00)			)

					__A             =			[floats_list((1, x)			)[0] for x in range(8_00							,14_00							,2_00			)]
					__A             =			feat_extract(
					    _a							,truncation=_a							,max_length=20_00							,padding="longest"							,return_tensors="np"			)
					__A             =			processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]			)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]			)
					self._check_zero_mean_unit_variance(input_values[2]			)

					# make sure that if max_length > longest -> then pad to longest
					self.assertTrue(input_values.shape == (3, 12_00)			)






	@require_torch
	def     UpperCamelCase_  (	self :			Dict			):
					import torch

					__A             =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
					__A             =			np.random.rand(1_00			).astype(np.floataa			)
					__A             =			np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
									__A             =			feature_extractor.pad([{"input_values": inputs}]							,return_tensors="np"			)
									self.assertTrue(np_processed.input_values.dtype == np.floataa			)
									__A             =			feature_extractor.pad([{"input_values": inputs}]							,return_tensors="pt"			)
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)







	@slow
	@require_torch
	def     UpperCamelCase_  (	self :			Any			):


					for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
									__A             =			WavaVecaConfig.from_pretrained(_a			)
									__A             =			WavaVecaFeatureExtractor.from_pretrained(_a			)

									# only "layer" feature extraction norm should make use of
									# attention_mask
									self.assertEqual(feat_extract.return_attention_mask							,config.feat_extract_norm == "layer"			)


import argparse
from collections import defaultdict

import yaml


SCREAMING_SNAKE_CASE							:str										=    'docs/source/en/_toctree.yml'


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			defaultdict(a_						)
				for doc in model_doc:
								counts[doc["local"]] += 1
				__A             =			[key for key, value in counts.items() if value > 1]

				__A             =			[]
				for duplicate_key in duplicates:
								__A             =			list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}						)
								if len(a_						) > 1:
												raise ValueError(
												    F'''{duplicate_key} is present several times in the documentation table of content at '''
												    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
												    "others."						)
								# Only add this once
								new_doc.append({"local": duplicate_key, "title": titles[0]}						)

				# Add none duplicate-keys
				new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]						)

				# Sort
				return sorted(a_		,       key=lambda a_						: s["title"].lower()						)


def 						UpperCAmelCase      (     a_=False						)		->     List[Any]:







				"""simple docstring"""



				with open(a_		,       encoding="utf-8"						) as f:
								__A             =			yaml.safe_load(f.read()						)

				# Get to the API doc
				__A             =			0
				while content[api_idx]["title"] != "API":
								api_idx += 1
				__A             =			content[api_idx]["sections"]

				# Then to the model doc
				__A             =			0
				while api_doc[model_idx]["title"] != "Models":
								model_idx += 1

				__A             =			api_doc[model_idx]["sections"]

				__A             =			[(idx, section) for idx, section in enumerate(a_						) if "sections" in section]
				__A             =			False
				for idx, modality_doc in modalities_docs:
								__A             =			modality_doc["sections"]
								__A             =			clean_model_doc_toc(a_						)

								if old_modality_doc != new_modality_doc:
												__A             =			True
												if overwrite:
																__A             =			new_modality_doc

				if diff:
								if overwrite:
												__A             =			model_doc
												__A             =			api_doc
												with open(a_		,       "w"		,       encoding="utf-8"						) as f:
																f.write(yaml.dump(a_		,       allow_unicode=a_						)						)
								else:
												raise ValueError(
												    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    argparse.ArgumentParser()
					parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()

					check_model_doc(args.fix_and_overwrite)
