






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


_UpperCAmelCase			:		List[Any]   				=    random.Random()

if is_torch_available():
			import torch





def 	lowerCAmelCase_		(lowercase__						:       int     ,					lowercase__						:       List[str]=1.0     ,					lowercase__						:       str=None     ,					lowercase__						:       List[Any]=None       )		->    Dict:


				'''simple docstring'''




				if rng is None:
								lowerCAmelCase__      			=						global_rng

				lowerCAmelCase__      			=						[]
				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					):




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Dict=7 ,							SCREAMING_SNAKE_CASE_     :   str=400 ,							SCREAMING_SNAKE_CASE_     :   int=2_000 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=1 ,							SCREAMING_SNAKE_CASE_     :   Any=0.0 ,							SCREAMING_SNAKE_CASE_     :   Dict=16_000 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=True ,							SCREAMING_SNAKE_CASE_     :   List[Any]=True ,							):
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						min_seq_length
											lowerCAmelCase__      			=						max_seq_length
											lowerCAmelCase__      			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											lowerCAmelCase__      			=						feature_size
											lowerCAmelCase__      			=						padding_value
											lowerCAmelCase__      			=						sampling_rate
											lowerCAmelCase__      			=						return_attention_mask
											lowerCAmelCase__      			=						do_normalize




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

											if equal_length:
															lowerCAmelCase__      			=						floats_list((self.batch_size, self.max_seq_length)	)
											else:
															# make sure that inputs increase in size
															lowerCAmelCase__      			=						[
															    _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__      			=						[np.asarray(SCREAMING_SNAKE_CASE_	) for x in speech_inputs]

											return speech_inputs



@require_torch
@require_torchaudio
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Tuple		      =    ASTFeatureExtractor




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						ASTFeatureExtractionTester(self	)




							def 						__snake_case					(   self     :   str	):
											# Tests that all call wrap to encode_plus and batch_encode_plus
											lowerCAmelCase__      			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in range(800 ,							1_400 ,							200	)]
											lowerCAmelCase__      			=						[np.asarray(SCREAMING_SNAKE_CASE_	) for speech_input in speech_inputs]

											# Test not batched input
											lowerCAmelCase__      			=						feat_extract(speech_inputs[0] ,							return_tensors='''np'''	).input_values
											lowerCAmelCase__      			=						feat_extract(np_speech_inputs[0] ,							return_tensors='''np'''	).input_values
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)

											# Test batched
											lowerCAmelCase__      			=						feat_extract(SCREAMING_SNAKE_CASE_ ,							padding=SCREAMING_SNAKE_CASE_ ,							return_tensors='''np'''	).input_values
											lowerCAmelCase__      			=						feat_extract(SCREAMING_SNAKE_CASE_ ,							padding=SCREAMING_SNAKE_CASE_ ,							return_tensors='''np'''	).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)

											# Test 2-D numpy arrays are batched.
											lowerCAmelCase__      			=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											lowerCAmelCase__      			=						np.asarray(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						feat_extract(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np'''	).input_values
											lowerCAmelCase__      			=						feat_extract(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np'''	).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	):
															self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)




							@require_torch
							def 						__snake_case					(   self     :   List[Any]	):
											import torch

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

											for inputs in [py_speech_inputs, np_speech_inputs]:
															lowerCAmelCase__      			=						feature_extractor.pad([{'''input_values''': inputs}] ,							return_tensors='''np'''	)
															self.assertTrue(np_processed.input_values.dtype == np.floataa	)
															lowerCAmelCase__      			=						feature_extractor.pad([{'''input_values''': inputs}] ,							return_tensors='''pt'''	)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]	):
											from datasets import load_dataset

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

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




							@require_torch
							def 						__snake_case					(   self     :   int	):
											# fmt: off
											lowerCAmelCase__      			=						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

											lowerCAmelCase__      			=						self._load_datasamples(1	)
											lowerCAmelCase__      			=						ASTFeatureExtractor()
											lowerCAmelCase__      			=						feature_extractor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).input_values
											self.assertEquals(input_values.shape ,							(1, 1_024, 128)	)
											self.assertTrue(torch.allclose(input_values[0, 0, :30] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		str   				=    {
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    ["BlenderbotSmallTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "BlenderbotSmallForCausalLM",
			    "BlenderbotSmallForConditionalGeneration",
			    "BlenderbotSmallModel",
			    "BlenderbotSmallPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    [
			    "TFBlenderbotSmallForConditionalGeneration",
			    "TFBlenderbotSmallModel",
			    "TFBlenderbotSmallPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[str]   				=    [
			    "FlaxBlenderbotSmallForConditionalGeneration",
			    "FlaxBlenderbotSmallModel",
			    "FlaxBlenderbotSmallPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		int   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





