"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


__lowercase    =				random.Random()


def    lowerCAmelCase			(__UpperCamelCase							:    Tuple							,					__UpperCamelCase							:    Optional[Any]=1.0							,					__UpperCamelCase							:    List[Any]=None							,					__UpperCamelCase							:    List[Any]=None			):
							"""simple docstring"""


							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






@require_torch
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def __init__(				self							:    List[Any]     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    Dict=7     , UpperCamelCase__							:    Optional[int]=400     , UpperCamelCase__							:    Optional[int]=2000     , UpperCamelCase__							:    Tuple=1     , UpperCamelCase__							:    str=0.0     , UpperCamelCase__							:    Tuple=24000     , UpperCamelCase__							:    Optional[int]=True     , )				->				Optional[Any]:
													'''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





						def   UpperCAmelCase_ (				self							:    Any				)				->				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,
													}





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    Dict=False     , UpperCamelCase__							:    Optional[int]=False				)				->				Tuple:
													'''simple docstring'''



													def _flatten(UpperCamelCase__							:    Optional[int]				):
																				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 audio_inputs]

													return audio_inputs






@require_torch
class 		_lowercase (						_SCREAMING_SNAKE_CASE					,					unittest.TestCase      ):



						"""simple docstring"""




						lowercase__       =  EncodecFeatureExtractor





						def   UpperCAmelCase_ (				self							:    Any				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=EnCodecFeatureExtractionTester(self				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Union[str, Any]:
													'''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(__A				) for audio_input in audio_inputs]

													# Test not batched input
													__UpperCamelCase							=feat_extract(audio_inputs[0]     , return_tensors='''np'''				).input_values
													__UpperCamelCase							=feat_extract(np_audio_inputs[0]     , return_tensors='''np'''				).input_values
													self.assertTrue(np.allclose(__A     , __A     , atol=1E-3				)				)

													# Test batched
													__UpperCamelCase							=feat_extract(__A     , padding=__A     , return_tensors='''np'''				).input_values
													__UpperCamelCase							=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				)				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Union[str, Any]:
													'''simple docstring'''



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

													for inputs in [py_audio_inputs, np_audio_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				)





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    int				)				->				Optional[Any]:
													'''simple docstring'''



													from datasets import load_dataset

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

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





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=torch.tensor(
													    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
													     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
													     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
													     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
													     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
													     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]				)
													# fmt: on
													__UpperCamelCase							=self._load_datasamples(1				)
													__UpperCamelCase							=EncodecFeatureExtractor()
													__UpperCamelCase							=feature_extractor(__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(input_values.shape     , (1, 1, 93680)				)
													self.assertTrue(torch.allclose(input_values[0, 0, :30]     , __A     , atol=1E-6				)				)





						def   UpperCAmelCase_ (				self							:    int				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=torch.tensor(
													    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
													     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
													     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
													     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
													     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
													     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]				)
													# fmt: on
													__UpperCamelCase							=self._load_datasamples(1				)
													__UpperCamelCase							=[np.tile(input_audio[0][None]     , reps=(2, 1)				)]
													input_audio[0][1] *= 0.5
													__UpperCamelCase							=EncodecFeatureExtractor(feature_size=2				)
													__UpperCamelCase							=feature_extractor(__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(input_values.shape     , (1, 2, 93680)				)
													self.assertTrue(torch.allclose(input_values[0, 0, :30]     , __A     , atol=1E-6				)				)
													self.assertTrue(torch.allclose(input_values[0, 1, :30]     , EXPECTED_INPUT_VALUES * 0.5     , atol=1E-6				)				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=self._load_datasamples(2				)
													# would be easier if the stride was like
													__UpperCamelCase							=EncodecFeatureExtractor(feature_size=1     , chunk_length_s=1     , overlap=0.01				)

													# pad and trunc raise an error ?
													with self.assertRaisesRegex(
													    __A     , '''^Both padding and truncation were set. Make sure you only set one.$'''     , ):
																				__UpperCamelCase							=feature_extractor(
																				    __A     , padding='''max_length'''     , truncation=__A     , return_tensors='''pt'''				).input_values

													# truncate to chunk
													__UpperCamelCase							=feature_extractor(__A     , truncation=__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (2, 1, 71520)				)  # 2 chunks

													# force truncate to max_length
													__UpperCamelCase							=feature_extractor(
													    __A     , truncation=__A     , max_length=48000     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (2, 1, 48000)				)

													# pad to chunk
													__UpperCamelCase							=feature_extractor(__A     , padding=__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(padded_outputs.shape     , (2, 1, 95280)				)

													# pad to chunk
													__UpperCamelCase							=feature_extractor(__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (2, 1, 95280)				)

													# force pad to max length
													__UpperCamelCase							=feature_extractor(
													    __A     , padding='''max_length'''     , max_length=100000     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (2, 1, 100000)				)

													# force no pad
													with self.assertRaisesRegex(
													    __A     , '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''     , ):
																				__UpperCamelCase							=feature_extractor(__A     , padding=__A     , return_tensors='''pt'''				).input_values

													__UpperCamelCase							=feature_extractor(input_audio[0]     , padding=__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (1, 1, 93680)				)

													# no pad if no chunk_length_s
													__UpperCamelCase							=None
													with self.assertRaisesRegex(
													    __A     , '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''     , ):
																				__UpperCamelCase							=feature_extractor(__A     , padding=__A     , return_tensors='''pt'''				).input_values

													__UpperCamelCase							=feature_extractor(input_audio[0]     , padding=__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (1, 1, 93680)				)

													# no pad if no overlap
													__UpperCamelCase							=2
													__UpperCamelCase							=None
													with self.assertRaisesRegex(
													    __A     , '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''     , ):
																				__UpperCamelCase							=feature_extractor(__A     , padding=__A     , return_tensors='''pt'''				).input_values

													__UpperCamelCase							=feature_extractor(input_audio[0]     , padding=__A     , return_tensors='''pt'''				).input_values
													self.assertEquals(truncated_outputs.shape     , (1, 1, 93680)				)







"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class  __A							(			unittest.TestCase,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=load_tool('''document-question-answering'''		)
     self.tool.setup()
     a											=load_tool('''document-question-answering'''					,       remote=__A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     self.tool(document=__A					,       question='''When is the coffee break?'''		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(document=__A					,       question='''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)