
"""simple docstring"""




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__      :    Tuple    							= random.Random()

if is_torch_available():
					import torch

def lowercase_  (   _snake_case   ,_snake_case=1.0   ,_snake_case=None   ,_snake_case=None						):
							if rng is None:
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   global_rng

							SCREAMING_SNAKE_CASE__						:       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 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=20_00   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=1_60_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=True   ,       )					->		Tuple:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Dict:



											"""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,
											    "do_normalize": self.do_normalize,
											}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=False    )					->		List[Any]:



											"""simple docstring"""




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

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

											return speech_inputs






@require_torch
@require_torchaudio
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						List[str]					       =   ASTFeatureExtractor



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ASTFeatureExtractionTester(self    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											# create three inputs of length 800, 1000, and 1200
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [floats_list((1, x)    )[0] for x in range(8_00   ,       14_00   ,       2_00    )]
											SCREAMING_SNAKE_CASE__						:       Dict           =   [np.asarray(SCREAMING_SNAKE_CASE__    ) for speech_input in speech_inputs]

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       int           =   feat_extract(speech_inputs[0]   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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
											SCREAMING_SNAKE_CASE__						:       List[str]           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       str           =   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.
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   np.asarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											import torch

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											SCREAMING_SNAKE_CASE__						:       Any           =   np.random.rand(1_00    ).astype(np.floataa    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   np_speech_inputs.tolist()

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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											from datasets import load_dataset

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

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



				@require_torch
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.tensor(
											    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
											     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
											     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
											     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]    )
											# fmt: on

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self._load_datasamples(1    )
											SCREAMING_SNAKE_CASE__						:       int           =   ASTFeatureExtractor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   feature_extractor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    ).input_values
											self.assertEquals(input_values.shape   ,       (1, 10_24, 1_28)    )
											self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )    )








'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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



