



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      :							Optional[int]							       =random.Random()



def       _lowerCAmelCase			(_lowerCAmelCase      ,		_lowerCAmelCase=1.0      ,		_lowerCAmelCase=None      ,		_lowerCAmelCase=None):
					if rng is None:
										UpperCamelCase_						     =      global_rng

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

					return values


class 					_lowercase      (unittest.TestCase					):
				'''simple docstring'''







				def __init__(       self   ,       snake_case__   ,       snake_case__=7   ,       snake_case__=400   ,       snake_case__=2000   ,       snake_case__=1   ,       snake_case__=0.0   ,       snake_case__=1_6000   ,       snake_case__=True   ,       snake_case__=True   ,       ):

									'''simple docstring'''


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

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

									'''simple docstring'''


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

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

									return speech_inputs




class 					_lowercase      (lowerCamelCase_					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  WavaVecaFeatureExtractor

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      WavaVecaFeatureExtractionTester(self			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									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 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									# create three inputs of length 800, 1000, and 1200
									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in range(800   ,       1400   ,       200			)]
									UpperCamelCase_						     =      [np.asarray(__snake_case			) for speech_input in speech_inputs]

									# Test not batched input
									UpperCamelCase_						     =      feat_extract(speech_inputs[0]   ,       return_tensors="np"			).input_values
									UpperCamelCase_						     =      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_						     =      feat_extract(__snake_case   ,       return_tensors="np"			).input_values
									UpperCamelCase_						     =      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_						     =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
									UpperCamelCase_						     =      np.asarray(__snake_case			)
									UpperCamelCase_						     =      feat_extract(__snake_case   ,       return_tensors="np"			).input_values
									UpperCamelCase_						     =      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 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in range(800   ,       1400   ,       200			)]

									UpperCamelCase_						     =      ["longest", "max_length", "do_not_pad"]
									UpperCamelCase_						     =      [None, 1600, None]
									for max_length, padding in zip(__snake_case   ,       __snake_case			):
														UpperCamelCase_						     =      feat_extract(__snake_case   ,       padding=__snake_case   ,       max_length=__snake_case   ,       return_tensors="np"			)
														UpperCamelCase_						     =      processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]			)
														self.assertTrue(input_values[0][800:].sum() < 1e-6			)
														self._check_zero_mean_unit_variance(input_values[1][:1000]			)
														self.assertTrue(input_values[0][1000:].sum() < 1e-6			)
														self._check_zero_mean_unit_variance(input_values[2][:1200]			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCamelCase_						     =      range(800   ,       1400   ,       200			)
									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in lengths]

									UpperCamelCase_						     =      ["longest", "max_length", "do_not_pad"]
									UpperCamelCase_						     =      [None, 1600, None]

									for max_length, padding in zip(__snake_case   ,       __snake_case			):
														UpperCamelCase_						     =      feat_extract(__snake_case   ,       max_length=__snake_case   ,       padding=__snake_case			)
														UpperCamelCase_						     =      processed.input_values

														self._check_zero_mean_unit_variance(input_values[0][:800]			)
														self._check_zero_mean_unit_variance(input_values[1][:1000]			)
														self._check_zero_mean_unit_variance(input_values[2][:1200]			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in range(800   ,       1400   ,       200			)]
									UpperCamelCase_						     =      feat_extract(
									    __snake_case   ,       truncation=__snake_case   ,       max_length=1000   ,       padding="max_length"   ,       return_tensors="np"			)
									UpperCamelCase_						     =      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]			)
									self._check_zero_mean_unit_variance(input_values[1]			)
									self._check_zero_mean_unit_variance(input_values[2]			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in range(800   ,       1400   ,       200			)]
									UpperCamelCase_						     =      feat_extract(
									    __snake_case   ,       truncation=__snake_case   ,       max_length=1000   ,       padding="longest"   ,       return_tensors="np"			)
									UpperCamelCase_						     =      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]			)
									self._check_zero_mean_unit_variance(input_values[1, :1000]			)
									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, 1000)			)

									UpperCamelCase_						     =      [floats_list((1, x)			)[0] for x in range(800   ,       1400   ,       200			)]
									UpperCamelCase_						     =      feat_extract(
									    __snake_case   ,       truncation=__snake_case   ,       max_length=2000   ,       padding="longest"   ,       return_tensors="np"			)
									UpperCamelCase_						     =      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]			)
									self._check_zero_mean_unit_variance(input_values[1, :1000]			)
									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, 1200)			)

				@require_torch
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									import torch

									UpperCamelCase_						     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCamelCase_						     =      np.random.rand(100			).astype(np.floataa			)
									UpperCamelCase_						     =      np_speech_inputs.tolist()

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

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
														UpperCamelCase_						     =      WavaVecaConfig.from_pretrained(__snake_case			)
														UpperCamelCase_						     =      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 json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			XLMTokenizer
					UpperCAmelCase__					:    List[str]          =			False
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__             =     [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    'w</w>',
												    'r</w>',
												    't</w>',
												    'lo',
												    'low',
												    'er</w>',
												    'low</w>',
												    'lowest</w>',
												    'newer</w>',
												    'wider</w>',
												    '<unk>',
												]
												a__             =     dict(zip(__snake_case			,range(len(__snake_case      )      )      )      )
												a__             =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['merges_file']      )
												with open(self.vocab_file			,'w'      ) as fp:
																			fp.write(json.dumps(__snake_case      )      )
												with open(self.merges_file			,'w'      ) as fp:
																			fp.write('\n'.join(__snake_case      )      )
					def  lowerCamelCase__(      self   :Any			,__snake_case   :int      )					->    Optional[int]:
												a__             =     'lower newer'
												a__             =     'lower newer'
												return input_text, output_text
					def  lowerCamelCase__(      self   :Tuple      )					->    Tuple:
												a__             =     XLMTokenizer(self.vocab_file			,self.merges_file      )

												a__             =     'lower'
												a__             =     ['low', 'er</w>']
												a__             =     tokenizer.tokenize(__snake_case      )
												self.assertListEqual(__snake_case			,__snake_case      )

												a__             =     tokens + ['<unk>']
												a__             =     [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )			,__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[Any]:
												a__             =     XLMTokenizer.from_pretrained('xlm-mlm-en-2048'      )

												a__             =     tokenizer.encode('sequence builders'			,add_special_tokens=__snake_case      )
												a__             =     tokenizer.encode('multi-sequence build'			,add_special_tokens=__snake_case      )

												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case      )
												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case			,__snake_case      )

												assert encoded_sentence == [0] + text + [1]
												assert encoded_pair == [0] + text + [1] + text_a + [1]




