"""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


_UpperCAmelCase            =		random.Random()




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[str]=1.0						,__lowercase						:			Optional[int]=None						,__lowercase						:			Optional[int]=None     ):
		'''simple docstring'''




		if rng is None:
				A_		:    Optional[Any]         =				global_rng

		A_		:    Tuple         =				[]
		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		,     lowercase		,     lowercase=7		,     lowercase=4_0_0		,     lowercase=2_0_0_0		,     lowercase=1		,     lowercase=0.0		,     lowercase=1_6_0_0_0		,     lowercase=True		,     lowercase=True		,     ):



						"""simple docstring"""





						A_		:    str         =				parent
						A_		:    int         =				batch_size
						A_		:    Tuple         =				min_seq_length
						A_		:    Dict         =				max_seq_length
						A_		:    List[str]         =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A_		:    Optional[Any]         =				feature_size
						A_		:    Optional[Any]         =				padding_value
						A_		:    int         =				sampling_rate
						A_		:    Optional[Any]         =				return_attention_mask
						A_		:    int         =				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		,     lowercase=False		,     lowercase=False ):



						"""simple docstring"""





						def _flatten(lowercase ):
								return list(itertools.chain(*lowercase ) )

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

						return speech_inputs





class UpperCAmelCase						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = WavaVecaFeatureExtractor
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				WavaVecaFeatureExtractionTester(self )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						self.assertTrue(np.all(np.mean(lowercase		,     axis=0 ) < 1E-3 ) )
						self.assertTrue(np.all(np.abs(np.var(lowercase		,     axis=0 ) - 1 ) < 1E-3 ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						# create three inputs of length 800, 1000, and 1200
						A_		:    Dict         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    Dict         =				[np.asarray(lowercase ) for speech_input in speech_inputs]

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

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

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



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    List[str]         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]

						A_		:    List[str]         =				['longest', 'max_length', 'do_not_pad']
						A_		:    Any         =				[None, 1_6_0_0, None]
						for max_length, padding in zip(lowercase		,     lowercase ):
								A_		:    Optional[Any]         =				feat_extract(lowercase		,     padding=lowercase		,     max_length=lowercase		,     return_tensors='np' )
								A_		:    int         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    Optional[Any]         =				range(8_0_0		,     1_4_0_0		,     2_0_0 )
						A_		:    str         =				[floats_list((1, x) )[0] for x in lengths]

						A_		:    Optional[Any]         =				['longest', 'max_length', 'do_not_pad']
						A_		:    List[Any]         =				[None, 1_6_0_0, None]

						for max_length, padding in zip(lowercase		,     lowercase ):
								A_		:    Union[str, Any]         =				feat_extract(lowercase		,     max_length=lowercase		,     padding=lowercase )
								A_		:    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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    List[str]         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    List[str]         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     max_length=1_0_0_0		,     padding='max_length'		,     return_tensors='np' )
						A_		:    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] )
						self._check_zero_mean_unit_variance(input_values[2] )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				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_		:    str         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     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_		:    Dict         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    Dict         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     max_length=2_0_0_0		,     padding='longest'		,     return_tensors='np' )
						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] )

						# make sure that if max_length > longest -> then pad to longest
						self.assertTrue(input_values.shape == (3, 1_2_0_0) )
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import torch

						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    Optional[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_		:    Dict         =				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:
								A_		:    Optional[int]         =				WavaVecaConfig.from_pretrained(lowercase )
								A_		:    int         =				WavaVecaFeatureExtractor.from_pretrained(lowercase )

								# 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
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase            =		{
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase            =		{
    """squeezebert/squeezebert-uncased""": 512,
    """squeezebert/squeezebert-mnli""": 512,
    """squeezebert/squeezebert-mnli-headless""": 512,
}


_UpperCAmelCase            =		{
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = SqueezeBertTokenizer
				def __init__(						self		,     lowercase=None		,     lowercase=None		,     lowercase=True		,     lowercase="[UNK]"		,     lowercase="[SEP]"		,     lowercase="[PAD]"		,     lowercase="[CLS]"		,     lowercase="[MASK]"		,     lowercase=True		,     lowercase=None		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(
						    lowercase		,     tokenizer_file=lowercase		,     do_lower_case=lowercase		,     unk_token=lowercase		,     sep_token=lowercase		,     pad_token=lowercase		,     cls_token=lowercase		,     mask_token=lowercase		,     tokenize_chinese_chars=lowercase		,     strip_accents=lowercase		,     **lowercase		,     )

						A_		:    int         =				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
						if (
						    normalizer_state.get('lowercase'		,     lowercase ) != do_lower_case
						    or normalizer_state.get('strip_accents'		,     lowercase ) != strip_accents
						    or normalizer_state.get('handle_chinese_chars'		,     lowercase ) != tokenize_chinese_chars
						):
								A_		:    Dict         =				getattr(lowercase		,     normalizer_state.pop('type' ) )
								A_		:    Optional[int]         =				do_lower_case
								A_		:    Optional[Any]         =				strip_accents
								A_		:    str         =				tokenize_chinese_chars
								A_		:    Any         =				normalizer_class(**lowercase )

						A_		:    Tuple         =				do_lower_case
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    str         =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
								output += token_ids_a + [self.sep_token_id]

						return output
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Dict         =				[self.sep_token_id]
						A_		:    Union[str, Any]         =				[self.cls_token_id]
						if token_ids_a is None:
								return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Dict         =				self._tokenizer.model.save(lowercase		,     name=lowercase )
						return tuple(lowercase )






