


'''simple docstring'''




import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class _snake_case   ( unittest.TestCase    ,   a__   ):
       """simple docstring"""





       def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



           """simple docstring"""







           __lowerCamelCase		:      Dict											=					load_tool("document-question-answering"	)
           self.tool.setup()
           __lowerCamelCase		:      Optional[Any]											=					load_tool("document-question-answering"			,				remote=__A	)







       def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



           """simple docstring"""







           __lowerCamelCase		:      Tuple											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
           __lowerCamelCase		:      int											=					dataset[0]["image"]

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







       def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:



           """simple docstring"""







           __lowerCamelCase		:      Tuple											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
           __lowerCamelCase		:      Tuple											=					dataset[0]["image"]

           __lowerCamelCase		:      Optional[int]											=					self.remote_tool(__A			,				"When is the coffee break?"	)
           self.assertEqual(__A			,				"11-14 to 11:39 a.m."	)







       def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



           """simple docstring"""







           __lowerCamelCase		:      Optional[int]											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
           __lowerCamelCase		:      Union[str, Any]											=					dataset[0]["image"]

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







       def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



           """simple docstring"""







           __lowerCamelCase		:      str											=					load_dataset("hf-internal-testing/example-documents"			,				split="test"	)
           __lowerCamelCase		:      Dict											=					dataset[0]["image"]

           __lowerCamelCase		:      Optional[int]											=					self.remote_tool(document=__A			,				question="When is the coffee break?"	)
           self.assertEqual(__A			,				"11-14 to 11:39 a.m."	)









'''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  :      Any      		=				random.Random()





def 							a_       (						lowercase							,						lowercase=1.0							,						lowercase=None							,						lowercase=None    )			->int:






				'''simple docstring'''

				if rng is None:
								__lowerCamelCase		:      Any											=					global_rng

				__lowerCamelCase		:      List[Any]											=					[]
				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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def __init__(   self			,				__A			,				__A=7			,				__A=400			,				__A=2000			,				__A=1			,				__A=0.0			,				__A=24000			,				__A=True			,				)     ->Optional[int]:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""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 		__SCREAMING_SNAKE_CASE(   self			,				__A=False			,				__A=False	)     ->int:



											"""simple docstring"""







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

											if equal_length:
															__lowerCamelCase		:      Optional[int]											=					floats_list((self.batch_size, self.max_seq_length)	)
											else:
															# make sure that inputs increase in size
															__lowerCamelCase		:      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:
															__lowerCamelCase		:      int											=					[np.asarray(__A	) for x in audio_inputs]

											return audio_inputs





@require_torch
class _snake_case   ( a__    ,   unittest.TestCase   ):
							"""simple docstring"""





							__a												=     EncodecFeatureExtractor







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      str											=					EnCodecFeatureExtractionTester(self	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											__lowerCamelCase		:      Optional[Any]											=					[floats_list((1, x)	)[0] for x in range(800			,				1400			,				200	)]
											__lowerCamelCase		:      Optional[Any]											=					[np.asarray(__A	) for audio_input in audio_inputs]

											# Test not batched input
											__lowerCamelCase		:      List[str]											=					feat_extract(audio_inputs[0]			,				return_tensors="np"	).input_values
											__lowerCamelCase		:      Union[str, Any]											=					feat_extract(np_audio_inputs[0]			,				return_tensors="np"	).input_values
											self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)

											# Test batched
											__lowerCamelCase		:      Optional[int]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
											__lowerCamelCase		:      List[str]											=					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 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[Any]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__lowerCamelCase		:      Tuple											=					np.random.rand(100	).astype(np.floataa	)
											__lowerCamelCase		:      Any											=					np_audio_inputs.tolist()

											for inputs in [py_audio_inputs, np_audio_inputs]:
															__lowerCamelCase		:      int											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="np"	)
															self.assertTrue(np_processed.input_values.dtype == np.floataa	)
															__lowerCamelCase		:      List[Any]											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="pt"	)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:



											"""simple docstring"""







											from datasets import load_dataset

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

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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Dict											=					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
											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
											__lowerCamelCase		:      str											=					EncodecFeatureExtractor()
											__lowerCamelCase		:      Optional[int]											=					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 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[Any]											=					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
											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
											__lowerCamelCase		:      str											=					[np.tile(input_audio[0][None]			,				reps=(2, 1)	)]
											input_audio[0][1] *= 0.5
											__lowerCamelCase		:      Optional[int]											=					EncodecFeatureExtractor(feature_size=2	)
											__lowerCamelCase		:      str											=					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 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:      str											=					self._load_datasamples(2	)
											# would be easier if the stride was like
											__lowerCamelCase		:      List[Any]											=					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.$"			,				):
															__lowerCamelCase		:      Dict											=					feature_extractor(
															    __A			,				padding="max_length"			,				truncation=__A			,				return_tensors="pt"	).input_values

											# truncate to chunk
											__lowerCamelCase		:      Optional[Any]											=					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
											__lowerCamelCase		:      List[str]											=					feature_extractor(
											    __A			,				truncation=__A			,				max_length=48000			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 48000)	)

											# pad to chunk
											__lowerCamelCase		:      List[str]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(padded_outputs.shape			,				(2, 1, 95280)	)

											# pad to chunk
											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 95280)	)

											# force pad to max length
											__lowerCamelCase		:      int											=					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.$"			,				):
															__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      int											=					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
											__lowerCamelCase		:      Any											=					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.$"			,				):
															__lowerCamelCase		:      int											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      Any											=					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
											__lowerCamelCase		:      List[Any]											=					2
											__lowerCamelCase		:      Any											=					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.$"			,				):
															__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      List[str]											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)





