


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


UpperCAmelCase_				:			int   =	random.Random()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Dict  ,   __A			:      Optional[int]=1.0  ,   __A			:      Union[str, Any]=None  ,   __A			:      Optional[int]=None    )     ->		Dict:

   """simple docstring"""

   if rng is None:
      a_ :		Tuple							=  global_rng

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

   return values
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def __init__(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Any=7					,							SCREAMING_SNAKE_CASE__    :	str=4_0_0					,							SCREAMING_SNAKE_CASE__    :	List[str]=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1					,							SCREAMING_SNAKE_CASE__    :	int=0.0					,							SCREAMING_SNAKE_CASE__    :	int=1_6_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=True					,							SCREAMING_SNAKE_CASE__    :	Any=True					,							)		->      List[str]:
         a_ :		Optional[Any]							=  parent
         a_ :		Dict							=  batch_size
         a_ :		int							=  min_seq_length
         a_ :		Optional[Any]							=  max_seq_length
         a_ :		Any							=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         a_ :		int							=  feature_size
         a_ :		Tuple							=  padding_value
         a_ :		Dict							=  sampling_rate
         a_ :		List[str]							=  return_attention_mask
         a_ :		Optional[int]							=  do_normalize
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      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 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	int=False					,							SCREAMING_SNAKE_CASE__    :	List[str]=False					)		->      List[Any]:
         def _flatten(SCREAMING_SNAKE_CASE__    :	Optional[int]					):
            return list(itertools.chain(*SCREAMING_SNAKE_CASE__					)					)

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

         return speech_inputs
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Optional[int]					       =						WavaVecaFeatureExtractor
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      List[Any]:
         a_ :		Optional[int]							=  WavaVecaFeatureExtractionTester(self					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	int					)		->      Optional[Any]:
         self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE__					,							axis=0					) < 1E-3					)					)
         self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE__					,							axis=0					) - 1					) < 1E-3					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Tuple:
         # Tests that all call wrap to encode_plus and batch_encode_plus
         a_ :		Union[str, Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         # create three inputs of length 800, 1000, and 1200
         a_ :		Optional[int]							=  [floats_list((1, x)					)[0] for x in range(8_0_0					,							1_4_0_0					,							2_0_0					)]
         a_ :		List[Any]							=  [np.asarray(SCREAMING_SNAKE_CASE__					) for speech_input in speech_inputs]

         # Test not batched input
         a_ :		List[Any]							=  feat_extract(speech_inputs[0]					,							return_tensors='np'					).input_values
         a_ :		List[str]							=  feat_extract(np_speech_inputs[0]					,							return_tensors='np'					).input_values
         self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							atol=1E-3					)					)

         # Test batched
         a_ :		Optional[Any]							=  feat_extract(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					).input_values
         a_ :		Any							=  feat_extract(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					).input_values
         for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
            self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							atol=1E-3					)					)

         # Test 2-D numpy arrays are batched.
         a_ :		Any							=  [floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         a_ :		List[str]							=  np.asarray(SCREAMING_SNAKE_CASE__					)
         a_ :		Dict							=  feat_extract(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					).input_values
         a_ :		int							=  feat_extract(SCREAMING_SNAKE_CASE__					,							return_tensors='np'					).input_values
         for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
            self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							atol=1E-3					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[str]:
         a_ :		Union[str, Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         a_ :		Union[str, Any]							=  [floats_list((1, x)					)[0] for x in range(8_0_0					,							1_4_0_0					,							2_0_0					)]

         a_ :		List[Any]							=  ['longest', 'max_length', 'do_not_pad']
         a_ :		Dict							=  [None, 1_6_0_0, None]
         for max_length, padding in zip(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
            a_ :		Optional[Any]							=  feat_extract(SCREAMING_SNAKE_CASE__					,							padding=SCREAMING_SNAKE_CASE__					,							max_length=SCREAMING_SNAKE_CASE__					,							return_tensors='np'					)
            a_ :		Any							=  processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
            self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6					)
            self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
            self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6					)
            self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Dict:
         a_ :		Optional[Any]							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         a_ :		Optional[int]							=  range(8_0_0					,							1_4_0_0					,							2_0_0					)
         a_ :		Optional[int]							=  [floats_list((1, x)					)[0] for x in lengths]

         a_ :		int							=  ['longest', 'max_length', 'do_not_pad']
         a_ :		Any							=  [None, 1_6_0_0, None]

         for max_length, padding in zip(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					):
            a_ :		List[str]							=  feat_extract(SCREAMING_SNAKE_CASE__					,							max_length=SCREAMING_SNAKE_CASE__					,							padding=SCREAMING_SNAKE_CASE__					)
            a_ :		List[str]							=  processed.input_values

            self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
            self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
            self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
         a_ :		int							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         a_ :		Optional[int]							=  [floats_list((1, x)					)[0] for x in range(8_0_0					,							1_4_0_0					,							2_0_0					)]
         a_ :		str							=  feat_extract(
             SCREAMING_SNAKE_CASE__					,							truncation=SCREAMING_SNAKE_CASE__					,							max_length=1_0_0_0					,							padding='max_length'					,							return_tensors='np'					)
         a_ :		Any							=  processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
         self._check_zero_mean_unit_variance(input_values[1]					)
         self._check_zero_mean_unit_variance(input_values[2]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
         a_ :		str							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         a_ :		int							=  [floats_list((1, x)					)[0] for x in range(8_0_0					,							1_4_0_0					,							2_0_0					)]
         a_ :		Dict							=  feat_extract(
             SCREAMING_SNAKE_CASE__					,							truncation=SCREAMING_SNAKE_CASE__					,							max_length=1_0_0_0					,							padding='longest'					,							return_tensors='np'					)
         a_ :		Optional[int]							=  processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
         self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
         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, 1_0_0_0)					)

         a_ :		str							=  [floats_list((1, x)					)[0] for x in range(8_0_0					,							1_4_0_0					,							2_0_0					)]
         a_ :		Optional[Any]							=  feat_extract(
             SCREAMING_SNAKE_CASE__					,							truncation=SCREAMING_SNAKE_CASE__					,							max_length=2_0_0_0					,							padding='longest'					,							return_tensors='np'					)
         a_ :		List[Any]							=  processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
         self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
         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, 1_2_0_0)					)
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
         import torch

         a_ :		int							=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
         a_ :		Any							=  np.random.rand(1_0_0					).astype(np.floataa					)
         a_ :		Any							=  np_speech_inputs.tolist()

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

      @slow
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Optional[Any]:
         # this test makes sure that models that are using
         # group norm don't have their feature extractor return the
         # attention_mask
         for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
            a_ :		int							=  WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
            a_ :		Tuple							=  WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					)

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






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


