
'''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 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=1.0  ,					_SCREAMING_SNAKE_CASE=None  ,					_SCREAMING_SNAKE_CASE=None  ):
		if rng is None:
				lowerCAmelCase_   :       Union[str, 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				: int					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Dict=7					,						UpperCamelCase_				: int=400					,						UpperCamelCase_				: int=2000					,						UpperCamelCase_				: Union[str, Any]=1					,						UpperCamelCase_				: Any=0.0					,						UpperCamelCase_				: Tuple=24000					,						UpperCamelCase_				: List[str]=True					,						):
			lowerCAmelCase_   :       str    							=parent
			lowerCAmelCase_   :       Dict    							=batch_size
			lowerCAmelCase_   :       Union[str, Any]    							=min_seq_length
			lowerCAmelCase_   :       Optional[Any]    							=max_seq_length
			lowerCAmelCase_   :       Dict    							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCAmelCase_   :       List[Any]    							=feature_size
			lowerCAmelCase_   :       Optional[Any]    							=padding_value
			lowerCAmelCase_   :       List[Any]    							=sampling_rate
			lowerCAmelCase_   :       List[Any]    							=return_attention_mask




	def 	__A      (						self				: Optional[Any]      ):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			}





	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: int=False					,						UpperCamelCase_				: int=False      ):
			def _flatten(UpperCamelCase_				: Union[str, Any]      ):
					return list(itertools.chain(*UpperCamelCase_      )      )

			if equal_length:
					lowerCAmelCase_   :       Optional[int]    							=floats_list((self.batch_size, self.max_seq_length)      )
			else:
					# make sure that inputs increase in size
					lowerCAmelCase_   :       List[str]    							=[
					    _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_   :       List[Any]    							=[np.asarray(UpperCamelCase_      ) for x in audio_inputs]

			return audio_inputs







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





	_UpperCamelCase							:  Optional[Any]								=		EncodecFeatureExtractor




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       Optional[Any]    							=EnCodecFeatureExtractionTester(self      )




	def 	__A      (						self				: Optional[Any]      ):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCAmelCase_   :       List[str]    							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
			# create three inputs of length 800, 1000, and 1200
			lowerCAmelCase_   :       int    							=[floats_list((1, x)      )[0] for x in range(800					,						1400					,						200      )]
			lowerCAmelCase_   :       Any    							=[np.asarray(UpperCamelCase_      ) for audio_input in audio_inputs]

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

			# Test batched
			lowerCAmelCase_   :       Tuple    							=feat_extract(UpperCamelCase_					,						padding=UpperCamelCase_					,						return_tensors='''np'''      ).input_values
			lowerCAmelCase_   :       List[Any]    							=feat_extract(UpperCamelCase_					,						padding=UpperCamelCase_					,						return_tensors='''np'''      ).input_values
			for enc_seq_a, enc_seq_a in zip(UpperCamelCase_					,						UpperCamelCase_      ):
					self.assertTrue(np.allclose(UpperCamelCase_					,						UpperCamelCase_					,						atol=1E-3      )      )




	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       List[Any]    							=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
			lowerCAmelCase_   :       Optional[Any]    							=np.random.rand(100      ).astype(np.floataa      )
			lowerCAmelCase_   :       Dict    							=np_audio_inputs.tolist()

			for inputs in [py_audio_inputs, np_audio_inputs]:
					lowerCAmelCase_   :       Dict    							=feature_extractor.pad([{'''input_values''': inputs}]					,						return_tensors='''np'''      )
					self.assertTrue(np_processed.input_values.dtype == np.floataa      )
					lowerCAmelCase_   :       Union[str, Any]    							=feature_extractor.pad([{'''input_values''': inputs}]					,						return_tensors='''pt'''      )
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa      )




	def 	__A      (						self				: List[Any]					,						UpperCamelCase_				: int      ):
			from datasets import load_dataset

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

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




	def 	__A      (						self				: Optional[int]      ):
			# fmt: off
			lowerCAmelCase_   :       Union[str, Any]    							=torch.tensor(
			    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
			     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
			     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
			     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
			     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
			     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]      )
			# fmt: on
			lowerCAmelCase_   :       Any    							=self._load_datasamples(1      )
			lowerCAmelCase_   :       List[str]    							=EncodecFeatureExtractor()
			lowerCAmelCase_   :       Any    							=feature_extractor(UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(input_values.shape					,						(1, 1, 93680)      )
			self.assertTrue(torch.allclose(input_values[0, 0, :30]					,						UpperCamelCase_					,						atol=1E-6      )      )




	def 	__A      (						self				: List[str]      ):
			# fmt: off
			lowerCAmelCase_   :       Optional[int]    							=torch.tensor(
			    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
			     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
			     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
			     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
			     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
			     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]      )
			# fmt: on
			lowerCAmelCase_   :       str    							=self._load_datasamples(1      )
			lowerCAmelCase_   :       Any    							=[np.tile(input_audio[0][None]					,						reps=(2, 1)      )]
			input_audio[0][1] *= 0.5
			lowerCAmelCase_   :       Any    							=EncodecFeatureExtractor(feature_size=2      )
			lowerCAmelCase_   :       int    							=feature_extractor(UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(input_values.shape					,						(1, 2, 93680)      )
			self.assertTrue(torch.allclose(input_values[0, 0, :30]					,						UpperCamelCase_					,						atol=1E-6      )      )
			self.assertTrue(torch.allclose(input_values[0, 1, :30]					,						EXPECTED_INPUT_VALUES * 0.5					,						atol=1E-6      )      )





	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       Tuple    							=self._load_datasamples(2      )
			# would be easier if the stride was like
			lowerCAmelCase_   :       List[str]    							=EncodecFeatureExtractor(feature_size=1					,						chunk_length_s=1					,						overlap=0.0_1      )

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

			# truncate to chunk
			lowerCAmelCase_   :       Any    							=feature_extractor(UpperCamelCase_					,						truncation=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(2, 1, 71520)      )  # 2 chunks

			# force truncate to max_length
			lowerCAmelCase_   :       Union[str, Any]    							=feature_extractor(
			    UpperCamelCase_					,						truncation=UpperCamelCase_					,						max_length=48000					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(2, 1, 48000)      )

			# pad to chunk
			lowerCAmelCase_   :       Dict    							=feature_extractor(UpperCamelCase_					,						padding=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(padded_outputs.shape					,						(2, 1, 95280)      )

			# pad to chunk
			lowerCAmelCase_   :       str    							=feature_extractor(UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(2, 1, 95280)      )

			# force pad to max length
			lowerCAmelCase_   :       str    							=feature_extractor(
			    UpperCamelCase_					,						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(
			    UpperCamelCase_					,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''					,						):
					lowerCAmelCase_   :       List[Any]    							=feature_extractor(UpperCamelCase_					,						padding=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values

			lowerCAmelCase_   :       List[str]    							=feature_extractor(input_audio[0]					,						padding=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(1, 1, 93680)      )

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

			lowerCAmelCase_   :       Dict    							=feature_extractor(input_audio[0]					,						padding=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(1, 1, 93680)      )

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

			lowerCAmelCase_   :       Optional[int]    							=feature_extractor(input_audio[0]					,						padding=UpperCamelCase_					,						return_tensors='''pt'''      ).input_values
			self.assertEquals(truncated_outputs.shape					,						(1, 1, 93680)      )








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






