


"""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


__snake_case             =   random.Random()

if is_torch_available():
		import torch







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  Any=1.0						,							lowercase  :  str=None						,							lowercase  :  Union[str, Any]=None			)     ->						Tuple:


				"""simple docstring"""





				if rng is None:
								snake_case   :				List[str]				 =   global_rng

				snake_case   :				int				 =   []
				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						,     UpperCamelCase__						,     UpperCamelCase__=7						,     UpperCamelCase__=400						,     UpperCamelCase__=2000						,     UpperCamelCase__=1						,     UpperCamelCase__=0.0						,     UpperCamelCase__=1_6000						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     )     ->		Tuple:

							'''simple docstring'''




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







			def 			lowerCamelCase		(     self						)     ->		int:

							'''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 			lowerCamelCase		(     self						,     UpperCamelCase__=False						,     UpperCamelCase__=False						)     ->		Union[str, Any]:

							'''simple docstring'''




							def _flatten(UpperCamelCase__						):
											return list(itertools.chain(*lowercase__						)						)

							if equal_length:
											snake_case   :				List[Any]				 =   floats_list((self.batch_size, self.max_seq_length)						)
							else:
											# make sure that inputs increase in size
											snake_case   :				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:
											snake_case   :				Union[str, Any]				 =   [np.asarray(lowercase__						) for x in speech_inputs]

							return speech_inputs







@require_torch
@require_torchaudio
class _lowerCAmelCase    (				lowerCamelCase__   ,							unittest.TestCase     ):
			__UpperCAmelCase			:	Any							  =  ASTFeatureExtractor







			def 			lowerCamelCase		(     self						)     ->		List[Any]:

							'''simple docstring'''




							snake_case   :				List[Any]				 =   ASTFeatureExtractionTester(self						)







			def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

							'''simple docstring'''




							snake_case   :				List[str]				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							# create three inputs of length 800, 1000, and 1200
							snake_case   :				Tuple				 =   [floats_list((1, x)						)[0] for x in range(800						,     1400						,     200						)]
							snake_case   :				List[str]				 =   [np.asarray(lowercase__						) for speech_input in speech_inputs]

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

							# Test batched
							snake_case   :				Dict				 =   feat_extract(lowercase__						,     padding=lowercase__						,     return_tensors="np"						).input_values
							snake_case   :				Union[str, Any]				 =   feat_extract(lowercase__						,     padding=lowercase__						,     return_tensors="np"						).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__						,     lowercase__						):
											self.assertTrue(np.allclose(lowercase__						,     lowercase__						,     atol=1e-3						)						)

							# Test 2-D numpy arrays are batched.
							snake_case   :				Optional[Any]				 =   [floats_list((1, x)						)[0] for x in (800, 800, 800)]
							snake_case   :				List[str]				 =   np.asarray(lowercase__						)
							snake_case   :				Any				 =   feat_extract(lowercase__						,     return_tensors="np"						).input_values
							snake_case   :				List[Any]				 =   feat_extract(lowercase__						,     return_tensors="np"						).input_values
							for enc_seq_a, enc_seq_a in zip(lowercase__						,     lowercase__						):
											self.assertTrue(np.allclose(lowercase__						,     lowercase__						,     atol=1e-3						)						)







			@require_torch
			def 			lowerCamelCase		(     self						)     ->		Tuple:

							'''simple docstring'''




							import torch

							snake_case   :				int				 =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							snake_case   :				int				 =   np.random.rand(100						).astype(np.floataa						)
							snake_case   :				List[str]				 =   np_speech_inputs.tolist()

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







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Union[str, Any]:

							'''simple docstring'''




							from datasets import load_dataset

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

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







			@require_torch
			def 			lowerCamelCase		(     self						)     ->		Tuple:

							'''simple docstring'''




							snake_case   :				Tuple				 =   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

							snake_case   :				Optional[int]				 =   self._load_datasamples(1						)
							snake_case   :				Union[str, Any]				 =   ASTFeatureExtractor()
							snake_case   :				Optional[int]				 =   feature_extractor(lowercase__						,     return_tensors="pt"						).input_values
							self.assertEquals(input_values.shape						,     (1, 1024, 128)						)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]						,     lowercase__						,     atol=1e-4						)						)









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





