

from typing import TYPE_CHECKING

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


A_ :				Union[str, Any]						 =    {
    '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:
	A_ :				Optional[int]						 =    ['BlenderbotSmallTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				List[Any]						 =    [
	    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotSmallForCausalLM',
	    'BlenderbotSmallForConditionalGeneration',
	    'BlenderbotSmallModel',
	    'BlenderbotSmallPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				str						 =    [
	    'TFBlenderbotSmallForConditionalGeneration',
	    'TFBlenderbotSmallModel',
	    'TFBlenderbotSmallPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_ :				Tuple						 =    [
	    '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

	A_ :				Union[str, Any]						 =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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


A_ :				Dict						 =    random.Random()

if is_torch_available():
	import torch

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				Tuple=1.0					,   lowercase_:				Dict=None					,   lowercase_:				int=None      )  ->   str:
	if rng is None:
		A__							:					Optional[Any]       =	global_rng

	A__							:					List[str]       =	[]
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=7					, A__=400					, A__=2000					, A__=1					, A__=0.0					, A__=1_6000					, A__=True					, A__=True					, ):
						A__							:					Any       =	parent
						A__							:					Optional[int]       =	batch_size
						A__							:					Union[str, Any]       =	min_seq_length
						A__							:					Dict       =	max_seq_length
						A__							:					Tuple       =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A__							:					str       =	feature_size
						A__							:					Optional[int]       =	padding_value
						A__							:					List[str]       =	sampling_rate
						A__							:					List[str]       =	return_attention_mask
						A__							:					int       =	do_normalize






					def 						__A    (				self    ):
						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 						__A    (				self					, A__=False					, A__=False    ):
						def _flatten(A__    ):
							return list(itertools.chain(*A__    )    )

						if equal_length:
							A__							:					Dict       =	floats_list((self.batch_size, self.max_seq_length)    )
						else:
							# make sure that inputs increase in size
							A__							:					Union[str, 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:
							A__							:					Optional[int]       =	[np.asarray(A__    ) for x in speech_inputs]

						return speech_inputs



@require_torch
@require_torchaudio
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= ASTFeatureExtractor






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	ASTFeatureExtractionTester(self    )






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

						# Test not batched input
						A__							:					Tuple       =	feat_extract(speech_inputs[0]					, return_tensors="""np"""    ).input_values
						A__							:					Tuple       =	feat_extract(np_speech_inputs[0]					, return_tensors="""np"""    ).input_values
						self.assertTrue(np.allclose(A__					, A__					, atol=1e-3    )    )

						# Test batched
						A__							:					Tuple       =	feat_extract(A__					, padding=A__					, return_tensors="""np"""    ).input_values
						A__							:					Tuple       =	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.
						A__							:					int       =	[floats_list((1, x)    )[0] for x in (800, 800, 800)]
						A__							:					List[str]       =	np.asarray(A__    )
						A__							:					Union[str, Any]       =	feat_extract(A__					, return_tensors="""np"""    ).input_values
						A__							:					Optional[Any]       =	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 						__A    (				self    ):
						import torch

						A__							:					Union[str, Any]       =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
						A__							:					Tuple       =	np.random.rand(100    ).astype(np.floataa    )
						A__							:					Tuple       =	np_speech_inputs.tolist()

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






					def 						__A    (				self					, A__    ):
						from datasets import load_dataset

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

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






					@require_torch
					def 						__A    (				self    ):
						# fmt: off
						A__							:					Optional[Any]       =	torch.tensor(
						    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
						     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
						     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
						     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]    )
						# fmt: on

						A__							:					Any       =	self._load_datasamples(1    )
						A__							:					Tuple       =	ASTFeatureExtractor()
						A__							:					Dict       =	feature_extractor(A__					, return_tensors="""pt"""    ).input_values
						self.assertEquals(input_values.shape					, (1, 1024, 128)    )
						self.assertTrue(torch.allclose(input_values[0, 0, :30]					, A__					, atol=1e-4    )    )



