






"""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


A_		:		List[Any]		     =			random.Random()

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=1.0     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=None  )	->    Optional[int]:
  if rng is None:
    UpperCamelCase_: Optional[Any]   				=    global_rng

  UpperCamelCase_: Union[str, Any]   				=    []
  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_0_0  ,      _lowerCamelCase=2_0_0_0  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=1_6_0_0_0  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      ):
         UpperCamelCase_: str   				=    parent
         UpperCamelCase_: List[Any]   				=    batch_size
         UpperCamelCase_: int   				=    min_seq_length
         UpperCamelCase_: Dict   				=    max_seq_length
         UpperCamelCase_: Tuple   				=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         UpperCamelCase_: str   				=    feature_size
         UpperCamelCase_: List[Any]   				=    padding_value
         UpperCamelCase_: Union[str, Any]   				=    sampling_rate
         UpperCamelCase_: List[str]   				=    return_attention_mask
         UpperCamelCase_: int   				=    do_normalize





       def       _a   ( self							):
         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       _a   ( self  ,      _lowerCamelCase=False  ,      _lowerCamelCase=False							):


         def _flatten(_lowerCamelCase							):
           return list(itertools.chain(*snake_case__							)							)

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

         return speech_inputs







class     _lowerCAmelCase(	A_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		int									=WavaVecaFeatureExtractor





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    WavaVecaFeatureExtractionTester(self							)





       def       _a   ( self  ,      _lowerCamelCase							):
         self.assertTrue(np.all(np.mean(snake_case__  ,      axis=0							) < 1e-3							)							)
         self.assertTrue(np.all(np.abs(np.var(snake_case__  ,      axis=0							) - 1							) < 1e-3							)							)





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         # create three inputs of length 800, 1000, and 1200
         UpperCamelCase_: Tuple   				=    [floats_list((1, x)							)[0] for x in range(8_0_0  ,      1_4_0_0  ,      2_0_0							)]
         UpperCamelCase_: List[str]   				=    [np.asarray(snake_case__							) for speech_input in speech_inputs]

         # Test not batched input
         UpperCamelCase_: Dict   				=    feat_extract(speech_inputs[0]  ,      return_tensors='np'							).input_values
         UpperCamelCase_: Any   				=    feat_extract(np_speech_inputs[0]  ,      return_tensors='np'							).input_values
         self.assertTrue(np.allclose(snake_case__  ,      snake_case__  ,      atol=1e-3							)							)

         # Test batched
         UpperCamelCase_: Union[str, Any]   				=    feat_extract(snake_case__  ,      return_tensors='np'							).input_values
         UpperCamelCase_: Any   				=    feat_extract(snake_case__  ,      return_tensors='np'							).input_values
         for enc_seq_a, enc_seq_a in zip(snake_case__  ,      snake_case__							):
           self.assertTrue(np.allclose(snake_case__  ,      snake_case__  ,      atol=1e-3							)							)

         # Test 2-D numpy arrays are batched.
         UpperCamelCase_: Any   				=    [floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         UpperCamelCase_: Union[str, Any]   				=    np.asarray(snake_case__							)
         UpperCamelCase_: Any   				=    feat_extract(snake_case__  ,      return_tensors='np'							).input_values
         UpperCamelCase_: List[str]   				=    feat_extract(snake_case__  ,      return_tensors='np'							).input_values
         for enc_seq_a, enc_seq_a in zip(snake_case__  ,      snake_case__							):
           self.assertTrue(np.allclose(snake_case__  ,      snake_case__  ,      atol=1e-3							)							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         UpperCamelCase_: Optional[int]   				=    [floats_list((1, x)							)[0] for x in range(8_0_0  ,      1_4_0_0  ,      2_0_0							)]

         UpperCamelCase_: int   				=    ["longest", "max_length", "do_not_pad"]
         UpperCamelCase_: List[str]   				=    [None, 1_6_0_0, None]
         for max_length, padding in zip(snake_case__  ,      snake_case__							):
           UpperCamelCase_: Optional[Any]   				=    feat_extract(snake_case__  ,      padding=snake_case__  ,      max_length=snake_case__  ,      return_tensors='np'							)
           UpperCamelCase_: str   				=    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       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         UpperCamelCase_: Tuple   				=    range(8_0_0  ,      1_4_0_0  ,      2_0_0							)
         UpperCamelCase_: Optional[Any]   				=    [floats_list((1, x)							)[0] for x in lengths]

         UpperCamelCase_: Tuple   				=    ["longest", "max_length", "do_not_pad"]
         UpperCamelCase_: Optional[Any]   				=    [None, 1_6_0_0, None]

         for max_length, padding in zip(snake_case__  ,      snake_case__							):
           UpperCamelCase_: Any   				=    feat_extract(snake_case__  ,      max_length=snake_case__  ,      padding=snake_case__							)
           UpperCamelCase_: 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][:1_2_0_0]							)





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         UpperCamelCase_: Optional[int]   				=    [floats_list((1, x)							)[0] for x in range(8_0_0  ,      1_4_0_0  ,      2_0_0							)]
         UpperCamelCase_: Tuple   				=    feat_extract(
             snake_case__  ,      truncation=snake_case__  ,      max_length=1_0_0_0  ,      padding='max_length'  ,      return_tensors='np'							)
         UpperCamelCase_: 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]							)
         self._check_zero_mean_unit_variance(input_values[2]							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         UpperCamelCase_: Optional[int]   				=    [floats_list((1, x)							)[0] for x in range(8_0_0  ,      1_4_0_0  ,      2_0_0							)]
         UpperCamelCase_: Optional[Any]   				=    feat_extract(
             snake_case__  ,      truncation=snake_case__  ,      max_length=1_0_0_0  ,      padding='longest'  ,      return_tensors='np'							)
         UpperCamelCase_: 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)							)

         UpperCamelCase_: Optional[Any]   				=    [floats_list((1, x)							)[0] for x in range(8_0_0  ,      1_4_0_0  ,      2_0_0							)]
         UpperCamelCase_: Optional[Any]   				=    feat_extract(
             snake_case__  ,      truncation=snake_case__  ,      max_length=2_0_0_0  ,      padding='longest'  ,      return_tensors='np'							)
         UpperCamelCase_: 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       _a   ( self							):
         import torch

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

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





       @slow
       @require_torch
       def       _a   ( self							):


         for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
           UpperCamelCase_: Any   				=    WavaVecaConfig.from_pretrained(snake_case__							)
           UpperCamelCase_: List[Any]   				=    WavaVecaFeatureExtractor.from_pretrained(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 unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass