"""simple docstring"""
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__					  =     random.Random()





def       lowerCAmelCase__			(	_UpperCamelCase				: Union[str, Any]       ,	_UpperCamelCase				: Optional[Any]=1.0       ,	_UpperCamelCase				: Optional[int]=None       ,	_UpperCamelCase				: Optional[Any]=None    )    -> Tuple:





						"""simple docstring"""






						if rng is None:
												snake_case	    =	global_rng

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

						return values



class      lowerCAmelCase_			(	unittest.TestCase    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase=7						,			lowerCAmelCase=4_00						,			lowerCAmelCase=20_00						,			lowerCAmelCase=1						,			lowerCAmelCase=0.0						,			lowerCAmelCase=1_60_00						,			lowerCAmelCase=True						,			lowerCAmelCase=True						,			):






										"""simple docstring"""

										snake_case	    =	parent
										snake_case	    =	batch_size
										snake_case	    =	min_seq_length
										snake_case	    =	max_seq_length
										snake_case	    =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case	    =	feature_size
										snake_case	    =	padding_value
										snake_case	    =	sampling_rate
										snake_case	    =	return_attention_mask
										snake_case	    =	do_normalize

				def 						snake_case       ( self			):






										"""simple docstring"""

										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 						snake_case       ( self						,			lowerCAmelCase=False						,			lowerCAmelCase=False			):






										"""simple docstring"""

										def _flatten(lowerCAmelCase			):
																return list(itertools.chain(*__UpperCamelCase			)			)

										if equal_length:
																snake_case	    =	floats_list((self.batch_size, self.max_seq_length)			)
										else:
																# make sure that inputs increase in size
																snake_case	    =	[
																    _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:
																snake_case	    =	[np.asarray(__UpperCamelCase			) for x in speech_inputs]

										return speech_inputs





class      lowerCAmelCase_			(	lowercase_   ,			unittest.TestCase    ):



				"""simple docstring"""
				_lowerCAmelCase   :			str	     =  WavaVecaFeatureExtractor

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	WavaVecaFeatureExtractionTester(self			)

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										self.assertTrue(np.all(np.mean(__UpperCamelCase						,			axis=0			) < 1E-3			)			)
										self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase						,			axis=0			) - 1			) < 1E-3			)			)

				def 						snake_case       ( self			):






										"""simple docstring"""

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

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

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

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

				def 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case	    =	[floats_list((1, x)			)[0] for x in range(8_00						,			14_00						,			2_00			)]

										snake_case	    =	['longest', 'max_length', 'do_not_pad']
										snake_case	    =	[None, 16_00, None]
										for max_length, padding in zip(__UpperCamelCase						,			__UpperCamelCase			):
																snake_case	    =	feat_extract(__UpperCamelCase						,			padding=__UpperCamelCase						,			max_length=__UpperCamelCase						,			return_tensors='np'			)
																snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case	    =	range(8_00						,			14_00						,			2_00			)
										snake_case	    =	[floats_list((1, x)			)[0] for x in lengths]

										snake_case	    =	['longest', 'max_length', 'do_not_pad']
										snake_case	    =	[None, 16_00, None]

										for max_length, padding in zip(__UpperCamelCase						,			__UpperCamelCase			):
																snake_case	    =	feat_extract(__UpperCamelCase						,			max_length=__UpperCamelCase						,			padding=__UpperCamelCase			)
																snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case	    =	[floats_list((1, x)			)[0] for x in range(8_00						,			14_00						,			2_00			)]
										snake_case	    =	feat_extract(
										    __UpperCamelCase						,			truncation=__UpperCamelCase						,			max_length=10_00						,			padding='max_length'						,			return_tensors='np'			)
										snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										snake_case	    =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										snake_case	    =	[floats_list((1, x)			)[0] for x in range(8_00						,			14_00						,			2_00			)]
										snake_case	    =	feat_extract(
										    __UpperCamelCase						,			truncation=__UpperCamelCase						,			max_length=10_00						,			padding='longest'						,			return_tensors='np'			)
										snake_case	    =	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)			)

										snake_case	    =	[floats_list((1, x)			)[0] for x in range(8_00						,			14_00						,			2_00			)]
										snake_case	    =	feat_extract(
										    __UpperCamelCase						,			truncation=__UpperCamelCase						,			max_length=20_00						,			padding='longest'						,			return_tensors='np'			)
										snake_case	    =	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 						snake_case       ( self			):






										"""simple docstring"""

										import torch

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

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

				@slow
				@require_torch
				def 						snake_case       ( self			):






										"""simple docstring"""

										for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																snake_case	    =	WavaVecaConfig.from_pretrained(__UpperCamelCase			)
																snake_case	    =	WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase			)

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



"""simple docstring"""







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				str													=	[key for key in counter if counter[key] > 1]
        if duplicate_keys:
             raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				Optional[int]													=	'\n'.join(full_content[1:sep_idx]      )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

     return None, "\n".join(__SCREAMING_SNAKE_CASE      )







class        UpperCamelCase		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

        # Convert the YAML keys to DatasetMetadata fields
        lowercase_	:				Dict													=	{
            (key.replace('-'     ,'_'			) if key.replace('-'     ,'_'			) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return yaml.safe_dump(
            {
                (key.replace('_'     ,'-'			) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }     ,sort_keys=__UpperCamelCase     ,allow_unicode=__UpperCamelCase     ,encoding='utf-8'     ,).decode('utf-8'			)






__SCREAMING_SNAKE_CASE    	={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       __SCREAMING_SNAKE_CASE    	=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
       ap.add_argument("readme_filepath")
       __SCREAMING_SNAKE_CASE    	=ap.parse_args()

       __SCREAMING_SNAKE_CASE    	=Path(args.readme_filepath)
       __SCREAMING_SNAKE_CASE    	=DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)

