






import os
import sys
import unittest


lowercase    				=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase    				=   os.path.join(git_repo_path, "src", "transformers")

lowercase    				=   "\n{0} = None\n"

lowercase    				=   "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowercase    				=   "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'   )
								self.assertIsNone(a   )

								snake_case_           =				find_backend('    if not is_tokenizers_available():'   )
								self.assertEqual(a			,     'tokenizers'   )

								snake_case_           =				find_backend('    if not is_tensorflow_text_available():'   )
								self.assertEqual(a			,     'tensorflow_text'   )

								snake_case_           =				find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'   )
								self.assertEqual(a			,     'sentencepiece_and_tokenizers'   )

								snake_case_           =				find_backend(
								    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'   )
								self.assertEqual(a			,     'sentencepiece_and_tensorflow_text'   )

								snake_case_           =				find_backend(
								    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'   )
								self.assertEqual(a			,     'sentencepiece_and_tokenizers_and_vision'   )




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn('torch'			,     a   )
								self.assertIn('tensorflow_text'			,     a   )
								self.assertIn('sentencepiece_and_tokenizers'			,     a   )

								# Likewise, we can't assert on the exact content of a key
								self.assertIn('BertModel'			,     objects['torch']   )
								self.assertIn('TFBertModel'			,     objects['tf']   )
								self.assertIn('FlaxBertModel'			,     objects['flax']   )
								self.assertIn('BertModel'			,     objects['torch']   )
								self.assertIn('TFBertTokenizer'			,     objects['tensorflow_text']   )
								self.assertIn('convert_slow_tokenizer'			,     objects['sentencepiece_and_tokenizers']   )




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				create_dummy_object('CONSTANT'			,     '\'torch\''   )
								self.assertEqual(a			,     '\nCONSTANT = None\n'   )

								snake_case_           =				create_dummy_object('function'			,     '\'torch\''   )
								self.assertEqual(
								    a			,     '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'   )

								snake_case_           =				'\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
								snake_case_           =				create_dummy_object('FakeClass'			,     '\'torch\''   )
								self.assertEqual(a			,     a   )






	def 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				'# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
								snake_case_           =				create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}   )
								self.assertEqual(dummy_files['torch']			,     a   )













import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase    				=   random.Random()

if is_torch_available():
	import torch





def 		__UpperCAmelCase     (	a_				,	a_=1.0				,	a_=None				,	a_=None):
							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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a=7			,     a=4_00			,     a=20_00			,     a=1			,     a=0.0			,     a=1_60_00			,     a=True			,     a=True			,     )			->       Tuple:
								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 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								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 				_UpperCamelCase     (			self			,     a=False			,     a=False   )			->       Optional[int]:
								def _flatten(a   ):
															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






@require_torch
@require_torchaudio
class 					UpperCamelCase_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					ASTFeatureExtractor




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				ASTFeatureExtractionTester(self   )




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								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(8_00			,     14_00			,     2_00   )]
								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			,     padding=a			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(a			,     padding=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 (8_00, 8_00, 8_00)]
								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   )   )




	@require_torch
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								import torch

								snake_case_           =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								snake_case_           =				np.random.rand(1_00   ).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   )




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								from datasets import load_dataset

								snake_case_           =				load_dataset('hf-internal-testing/librispeech_asr_dummy'			,     'clean'			,     split='validation'   )
								# automatic decoding with librispeech
								snake_case_           =				ds.sort('id'   ).select(range(a   )   )[:num_samples]['audio']

								return [x["array"] for x in speech_samples]






	@require_torch
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								# fmt: off
								snake_case_           =				torch.tensor(
								    [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
								     -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
								     -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
								     -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]   )
								# fmt: on

								snake_case_           =				self._load_datasamples(1   )
								snake_case_           =				ASTFeatureExtractor()
								snake_case_           =				feature_extractor(a			,     return_tensors='pt'   ).input_values
								self.assertEquals(input_values.shape			,     (1, 10_24, 1_28)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]			,     a			,     atol=1E-4   )   )





