



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


SCREAMING_SNAKE_CASE  					=    random.Random()







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE=1.0      ,  __SCREAMING_SNAKE_CASE=None      ,  __SCREAMING_SNAKE_CASE=None			)		->				int:
		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 						lowerCamelCase   (    unittest.TestCase	):







					'''simple docstring'''







					def __init__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase=7     ,						lowerCAmelCase=400     ,						lowerCAmelCase=2000     ,						lowerCAmelCase=1     ,						lowerCAmelCase=0.0     ,						lowerCAmelCase=1_6000     ,						lowerCAmelCase=True     ,						lowerCAmelCase=True     ,						):
							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  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(*A__  )  )

							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(A__  ) for x in speech_inputs]

							return speech_inputs





class 						lowerCamelCase   (    __snake_case,  unittest.TestCase	):







					'''simple docstring'''







					lowerCAmelCase_					:     Optional[int]					       =	WavaVecaFeatureExtractor



					def  A__		(      self  ):
							UpperCAmelCase_						       =					WavaVecaFeatureExtractionTester(self  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							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  ):
							# Tests that all call wrap to encode_plus and batch_encode_plus
							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(A__  ) 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(A__     ,						A__     ,						atol=1e-3  )  )

							# Test batched
							UpperCAmelCase_						       =					feat_extract(A__     ,						return_tensors="np"  ).input_values
							UpperCAmelCase_						       =					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.
							UpperCAmelCase_						       =					[floats_list((1, x)  )[0] for x in (800, 800, 800)]
							UpperCAmelCase_						       =					np.asarray(A__  )
							UpperCAmelCase_						       =					feat_extract(A__     ,						return_tensors="np"  ).input_values
							UpperCAmelCase_						       =					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  ):
							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(A__     ,						A__  ):
									UpperCAmelCase_						       =					feat_extract(A__     ,						padding=A__     ,						max_length=A__     ,						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  A__		(      self  ):
							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(A__     ,						A__  ):
									UpperCAmelCase_						       =					feat_extract(A__     ,						max_length=A__     ,						padding=A__  )
									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  A__		(      self  ):
							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(
							    A__     ,						truncation=A__     ,						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  A__		(      self  ):
							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(
							    A__     ,						truncation=A__     ,						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(
							    A__     ,						truncation=A__     ,						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  A__		(      self  ):
							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  A__		(      self  ):
							# 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:
									UpperCAmelCase_						       =					WavaVecaConfig.from_pretrained(A__  )
									UpperCAmelCase_						       =					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"  )









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



