




'''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       :						int								=		random.Random()


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   Optional[int]=1.0		,				_UpperCAmelCase		:   List[Any]=None		,				_UpperCAmelCase		:   Union[str, Any]=None						)      ->      List[Any]:
			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 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def __init__(      self						:    List[str]      ,		a_						:    List[str]      ,		a_						:    Union[str, Any]=7      ,		a_						:    List[str]=400      ,		a_						:    Dict=2_000      ,		a_						:    Optional[Any]=1      ,		a_						:    List[str]=0.0      ,		a_						:    List[str]=16_000      ,		a_						:    str=True      ,		a_						:    Optional[int]=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 							A (      self						:    int							):



						"""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 							A (      self						:    Tuple      ,		a_						:    Any=False      ,		a_						:    Union[str, Any]=False							):



						"""simple docstring"""




						def _flatten(a_						:    Optional[Any]							):
									return list(itertools.chain(*a_							)							)

						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(a_							) for x in speech_inputs]

						return speech_inputs







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase				,     unittest.TestCase	):
			__SCREAMING_SNAKE_CASE    =			WavaVecaFeatureExtractor





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		WavaVecaFeatureExtractionTester(self							)





			def 							A (      self						:    Tuple      ,		a_						:    Optional[int]							):



						"""simple docstring"""




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





			def 							A (      self						:    Optional[int]							):



						"""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(800      ,		1_400      ,		200							)]
						__snake_case			=		[np.asarray(a_							) 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(a_      ,		a_      ,		atol=1e-3							)							)

						# Test batched
						__snake_case			=		feat_extract(a_      ,		return_tensors="np"							).input_values
						__snake_case			=		feat_extract(a_      ,		return_tensors="np"							).input_values
						for enc_seq_a, enc_seq_a in zip(a_      ,		a_							):
									self.assertTrue(np.allclose(a_      ,		a_      ,		atol=1e-3							)							)

						# Test 2-D numpy arrays are batched.
						__snake_case			=		[floats_list((1, x)							)[0] for x in (800, 800, 800)]
						__snake_case			=		np.asarray(a_							)
						__snake_case			=		feat_extract(a_      ,		return_tensors="np"							).input_values
						__snake_case			=		feat_extract(a_      ,		return_tensors="np"							).input_values
						for enc_seq_a, enc_seq_a in zip(a_      ,		a_							):
									self.assertTrue(np.allclose(a_      ,		a_      ,		atol=1e-3							)							)





			def 							A (      self						:    Optional[Any]							):



						"""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(800      ,		1_400      ,		200							)]

						__snake_case			=		["longest", "max_length", "do_not_pad"]
						__snake_case			=		[None, 1_600, None]
						for max_length, padding in zip(a_      ,		a_							):
									__snake_case			=		feat_extract(a_      ,		padding=a_      ,		max_length=a_      ,		return_tensors="np"							)
									__snake_case			=		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][:1_000]							)
									self.assertTrue(input_values[0][1_000:].sum() < 1e-6							)
									self._check_zero_mean_unit_variance(input_values[2][:1_200]							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
						__snake_case			=		range(800      ,		1_400      ,		200							)
						__snake_case			=		[floats_list((1, x)							)[0] for x in lengths]

						__snake_case			=		["longest", "max_length", "do_not_pad"]
						__snake_case			=		[None, 1_600, None]

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

									self._check_zero_mean_unit_variance(input_values[0][:800]							)
									self._check_zero_mean_unit_variance(input_values[1][:1_000]							)
									self._check_zero_mean_unit_variance(input_values[2][:1_200]							)





			def 							A (      self						:    str							):



						"""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(800      ,		1_400      ,		200							)]
						__snake_case			=		feat_extract(
						    a_      ,		truncation=a_      ,		max_length=1_000      ,		padding="max_length"      ,		return_tensors="np"							)
						__snake_case			=		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 							A (      self						:    Optional[int]							):



						"""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(800      ,		1_400      ,		200							)]
						__snake_case			=		feat_extract(
						    a_      ,		truncation=a_      ,		max_length=1_000      ,		padding="longest"      ,		return_tensors="np"							)
						__snake_case			=		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]							)
						self._check_zero_mean_unit_variance(input_values[1, :1_000]							)
						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_000)							)

						__snake_case			=		[floats_list((1, x)							)[0] for x in range(800      ,		1_400      ,		200							)]
						__snake_case			=		feat_extract(
						    a_      ,		truncation=a_      ,		max_length=2_000      ,		padding="longest"      ,		return_tensors="np"							)
						__snake_case			=		processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]							)
						self._check_zero_mean_unit_variance(input_values[1, :1_000]							)
						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_200)							)





			@require_torch
			def 							A (      self						:    Tuple							):



						"""simple docstring"""




						import torch

						__snake_case			=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
						__snake_case			=		np.random.rand(100							).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 							A (      self						:    str							):



						"""simple docstring"""




						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
									__snake_case			=		WavaVecaConfig.from_pretrained(a_							)
									__snake_case			=		WavaVecaFeatureExtractor.from_pretrained(a_							)

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












import copy
import random

from transformers import CLIPTokenizer

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														super().__init__(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      {}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      super().add_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
														if num_added_tokens == 0:
																					raise ValueError(
																					    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
																					    """ `placeholder_token` that is not already in the tokenizer."""	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=1	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					output.append(__lowerCAmelCase	)
														else:
																					UpperCamelCase__					  =      []
																					for i in range(__lowerCAmelCase	):
																												UpperCamelCase__					  =      placeholder_token + f"""_{i}"""
																												self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												output.append(__lowerCAmelCase	)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
														for token in self.token_map:
																					if token in placeholder_token:
																												raise ValueError(
																												    f"""The tokenizer already has placeholder token {token} that can get confused with"""
																												    f""" {placeholder_token}keep placeholder tokens independent"""	)
														UpperCamelCase__					  =      output






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	):
														if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      []
																					for i in range(len(__lowerCAmelCase	)	):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]	,			vector_shuffle=__lowerCAmelCase	)	)
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												UpperCamelCase__					  =      self.token_map[placeholder_token]
																												UpperCamelCase__					  =      tokens[: 1 + int(len(__lowerCAmelCase	) * prop_tokens_to_load	)]
																												if vector_shuffle:
																																			UpperCamelCase__					  =      copy.copy(__lowerCAmelCase	)
																																			random.shuffle(__lowerCAmelCase	)
																												UpperCamelCase__					  =      text.replace(__lowerCAmelCase	,			""" """.join(__lowerCAmelCase	)	)
														return text






							def __call__(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)




