




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Any  ):
					__lowerCAmelCase            = YolosConfig()

					# size of the architecture
					if "yolos_ti" in yolos_name:
										__lowerCAmelCase            = 1_92
										__lowerCAmelCase            = 7_68
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 3
										__lowerCAmelCase            = [8_00, 13_33]
										__lowerCAmelCase            = False
					elif yolos_name == "yolos_s_dWr":
										__lowerCAmelCase            = 3_30
										__lowerCAmelCase            = 14
										__lowerCAmelCase            = 6
										__lowerCAmelCase            = 13_20
					elif "yolos_s" in yolos_name:
										__lowerCAmelCase            = 3_84
										__lowerCAmelCase            = 15_36
										__lowerCAmelCase            = 12
										__lowerCAmelCase            = 6
					elif "yolos_b" in yolos_name:
										__lowerCAmelCase            = [8_00, 13_44]

					__lowerCAmelCase            = 91
					__lowerCAmelCase            = "huggingface/label-files"
					__lowerCAmelCase            = "coco-detection-id2label.json"
					__lowerCAmelCase            = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,   _SCREAMING_SNAKE_CASE   ,   repo_type="dataset"  )   ,   "r"  )  )
					__lowerCAmelCase            = {int(_SCREAMING_SNAKE_CASE  ): v for k, v in idalabel.items()}
					__lowerCAmelCase            = idalabel
					__lowerCAmelCase            = {v: k for k, v in idalabel.items()}

					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       List[Any]   ,   SCREAMING_SNAKE_CASE_							:       Optional[int] = False  ):
					for i in range(config.num_hidden_layers  ):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__lowerCAmelCase            = state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""  )
										__lowerCAmelCase            = state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""  )
										# next, add query, keys and values (in that order) to the state dict
										__lowerCAmelCase            = in_proj_weight[: config.hidden_size, :]
										__lowerCAmelCase            = in_proj_bias[: config.hidden_size]
										__lowerCAmelCase            = in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__lowerCAmelCase            = in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__lowerCAmelCase            = in_proj_weight[-config.hidden_size :, :]
										__lowerCAmelCase            = in_proj_bias[-config.hidden_size :]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					if "backbone" in name:
										__lowerCAmelCase            = name.replace("backbone"   ,   "vit"  )
					if "cls_token" in name:
										__lowerCAmelCase            = name.replace("cls_token"   ,   "embeddings.cls_token"  )
					if "det_token" in name:
										__lowerCAmelCase            = name.replace("det_token"   ,   "embeddings.detection_tokens"  )
					if "mid_pos_embed" in name:
										__lowerCAmelCase            = name.replace("mid_pos_embed"   ,   "encoder.mid_position_embeddings"  )
					if "pos_embed" in name:
										__lowerCAmelCase            = name.replace("pos_embed"   ,   "embeddings.position_embeddings"  )
					if "patch_embed.proj" in name:
										__lowerCAmelCase            = name.replace("patch_embed.proj"   ,   "embeddings.patch_embeddings.projection"  )
					if "blocks" in name:
										__lowerCAmelCase            = name.replace("blocks"   ,   "encoder.layer"  )
					if "attn.proj" in name:
										__lowerCAmelCase            = name.replace("attn.proj"   ,   "attention.output.dense"  )
					if "attn" in name:
										__lowerCAmelCase            = name.replace("attn"   ,   "attention.self"  )
					if "norm1" in name:
										__lowerCAmelCase            = name.replace("norm1"   ,   "layernorm_before"  )
					if "norm2" in name:
										__lowerCAmelCase            = name.replace("norm2"   ,   "layernorm_after"  )
					if "mlp.fc1" in name:
										__lowerCAmelCase            = name.replace("mlp.fc1"   ,   "intermediate.dense"  )
					if "mlp.fc2" in name:
										__lowerCAmelCase            = name.replace("mlp.fc2"   ,   "output.dense"  )
					if "class_embed" in name:
										__lowerCAmelCase            = name.replace("class_embed"   ,   "class_labels_classifier"  )
					if "bbox_embed" in name:
										__lowerCAmelCase            = name.replace("bbox_embed"   ,   "bbox_predictor"  )
					if "vit.norm" in name:
										__lowerCAmelCase            = name.replace("vit.norm"   ,   "vit.layernorm"  )

					return name







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ):
					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase            = orig_state_dict.pop(_SCREAMING_SNAKE_CASE  )

										if "qkv" in key:
															__lowerCAmelCase            = key.split("."  )
															__lowerCAmelCase            = int(key_split[2]  )
															__lowerCAmelCase            = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
															if "weight" in key:
																				__lowerCAmelCase            = val[:dim, :]
																				__lowerCAmelCase            = val[
																				    dim : dim * 2, :
																				]
																				__lowerCAmelCase            = val[-dim:, :]
															else:
																				__lowerCAmelCase            = val[:dim]
																				__lowerCAmelCase            = val[dim : dim * 2]
																				__lowerCAmelCase            = val[-dim:]
										else:
															__lowerCAmelCase            = val

					return orig_state_dict







def 			_a   (		):
					__lowerCAmelCase            = "http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase            = Image.open(requests.get(_SCREAMING_SNAKE_CASE   ,   stream=_SCREAMING_SNAKE_CASE  ).raw  )
					return im







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[int]   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       Tuple = False  ):
					__lowerCAmelCase            = get_yolos_config(_SCREAMING_SNAKE_CASE  )

					# load original state_dict
					__lowerCAmelCase            = torch.load(_SCREAMING_SNAKE_CASE   ,   map_location="cpu"  )["model"]

					# load 🤗 model
					__lowerCAmelCase            = YolosForObjectDetection(_SCREAMING_SNAKE_CASE  )
					model.eval()
					__lowerCAmelCase            = convert_state_dict(_SCREAMING_SNAKE_CASE   ,   _SCREAMING_SNAKE_CASE  )
					model.load_state_dict(_SCREAMING_SNAKE_CASE  )

					# Check outputs on an image, prepared by YolosImageProcessor
					__lowerCAmelCase            = 8_00 if yolos_name != "yolos_ti" else 5_12
					__lowerCAmelCase            = YolosImageProcessor(format="coco_detection"   ,   size=_SCREAMING_SNAKE_CASE  )
					__lowerCAmelCase            = image_processor(images=prepare_img()   ,   return_tensors="pt"  )
					__lowerCAmelCase            = model(**_SCREAMING_SNAKE_CASE  )
					__lowerCAmelCase     ,							__lowerCAmelCase            = outputs.logits, outputs.pred_boxes

					__lowerCAmelCase     ,							__lowerCAmelCase            = None, None
					if yolos_name == "yolos_ti":
										__lowerCAmelCase            = torch.tensor(
										    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]  )
					elif yolos_name == "yolos_s_200_pre":
										__lowerCAmelCase            = torch.tensor(
										    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]  )
					elif yolos_name == "yolos_s_300_pre":
										__lowerCAmelCase            = torch.tensor(
										    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]  )
					elif yolos_name == "yolos_s_dWr":
										__lowerCAmelCase            = torch.tensor(
										    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]  )
					elif yolos_name == "yolos_base":
										__lowerCAmelCase            = torch.tensor(
										    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]  )
										__lowerCAmelCase            = torch.tensor(
										    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]  )
					else:
										raise ValueError(F"""Unknown yolos_name: {yolos_name}"""  )

					assert torch.allclose(logits[0, :3, :3]   ,   _SCREAMING_SNAKE_CASE   ,   atol=1E-4  )
					assert torch.allclose(pred_boxes[0, :3, :3]   ,   _SCREAMING_SNAKE_CASE   ,   atol=1E-4  )

					Path(_SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )
					print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(_SCREAMING_SNAKE_CASE  )
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
					image_processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

					if push_to_hub:
										__lowerCAmelCase            = {
										    "yolos_ti": "yolos-tiny",
										    "yolos_s_200_pre": "yolos-small",
										    "yolos_s_300_pre": "yolos-small-300",
										    "yolos_s_dWr": "yolos-small-dwr",
										    "yolos_base": "yolos-base",
										}

										print("Pushing to the hub..."  )
										__lowerCAmelCase            = model_mapping[yolos_name]
										image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   ,   organization="hustvl"  )
										model.push_to_hub(_SCREAMING_SNAKE_CASE   ,   organization="hustvl"  )


if __name__ == "__main__":
						UpperCamelCase__					    =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--yolos_name""",
						    default="""yolos_s_200_pre""",
						    type=str,
						    help=(
						        """Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\',"""
						        """ \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'."""
						    ),
						)
						parser.add_argument(
						    """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)
						parser.add_argument(
						    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
						)

						UpperCamelCase__					    =    parser.parse_args()
						convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

UpperCamelCase__					    =    random.Random()







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       Union[str, Any]=1.0   ,   SCREAMING_SNAKE_CASE_							:       Any=None   ,   SCREAMING_SNAKE_CASE_							:       Any=None  ):
					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
@require_torch
@require_torchaudio
class     a__					(      unittest.TestCase   ):






						def __init__(     self	,    _A	,    _A=7	,    _A=4_0_0	,    _A=2_0_0_0	,    _A=2_4	,    _A=2_4	,    _A=0.0	,    _A=1_6_0_0_0	,    _A=True	,    _A=True	,    ):




											"""simple docstring"""



											__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            = num_mel_bins
											__lowerCAmelCase            = padding_value
											__lowerCAmelCase            = sampling_rate
											__lowerCAmelCase            = return_attention_mask
											__lowerCAmelCase            = do_normalize






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return {
											    "feature_size": self.feature_size,
											    "num_mel_bins": self.num_mel_bins,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											    "do_normalize": self.do_normalize,
											}






						def        __SCREAMING_SNAKE_CASE(     self	,    _A=False	,    _A=False    ):




											"""simple docstring"""



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

											if equal_length:
																__lowerCAmelCase            = [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
											else:
																# make sure that inputs increase in size
																__lowerCAmelCase            = [
																    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(_A    ) for x in speech_inputs]
											return speech_inputs






@require_torch
@require_torchaudio
class     a__					(      snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[int]      =					SpeechaTextFeatureExtractor if is_speech_available() else None






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = SpeechaTextFeatureExtractionTester(self    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											self.assertTrue(np.all(np.mean(_A	,    axis=0    ) < 1E-3    )    )
											self.assertTrue(np.all(np.abs(np.var(_A	,    axis=0    ) - 1    ) < 1E-3    )    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__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(8_0_0	,    1_4_0_0	,    2_0_0    )]
											__lowerCAmelCase            = [np.asarray(_A    ) for speech_input in speech_inputs]

											# Test feature size
											__lowerCAmelCase            = feature_extractor(_A	,    padding=_A	,    return_tensors="np"    ).input_features
											self.assertTrue(input_features.ndim == 3    )
											self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size    )

											# Test not batched input
											__lowerCAmelCase            = feature_extractor(speech_inputs[0]	,    return_tensors="np"    ).input_features
											__lowerCAmelCase            = feature_extractor(np_speech_inputs[0]	,    return_tensors="np"    ).input_features
											self.assertTrue(np.allclose(_A	,    _A	,    atol=1E-3    )    )

											# Test batched
											__lowerCAmelCase            = feature_extractor(_A	,    return_tensors="np"    ).input_features
											__lowerCAmelCase            = feature_extractor(_A	,    return_tensors="np"    ).input_features
											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.
											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
											__lowerCAmelCase            = np.asarray(_A    )
											__lowerCAmelCase            = feature_extractor(_A	,    return_tensors="np"    ).input_features
											__lowerCAmelCase            = feature_extractor(_A	,    return_tensors="np"    ).input_features
											for enc_seq_a, enc_seq_a in zip(_A	,    _A    ):
																self.assertTrue(np.allclose(_A	,    _A	,    atol=1E-3    )    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in range(8_0_0	,    1_4_0_0	,    2_0_0    )]

											__lowerCAmelCase            = ["longest", "max_length", "do_not_pad"]
											__lowerCAmelCase            = [None, 1_6, None]
											for max_length, padding in zip(_A	,    _A    ):
																__lowerCAmelCase            = feature_extractor(
																    _A	,    padding=_A	,    max_length=_A	,    return_attention_mask=_A    )
																__lowerCAmelCase            = inputs.input_features
																__lowerCAmelCase            = inputs.attention_mask
																__lowerCAmelCase            = [np.sum(_A    ) for x in attention_mask]

																self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
																self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
																self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in range(8_0_0	,    1_4_0_0	,    2_0_0    )]

											__lowerCAmelCase            = ["longest", "max_length", "do_not_pad"]
											__lowerCAmelCase            = [None, 1_6, None]
											for max_length, padding in zip(_A	,    _A    ):
																__lowerCAmelCase            = feature_extractor(
																    _A	,    max_length=_A	,    padding=_A	,    return_tensors="np"	,    return_attention_mask=_A    )
																__lowerCAmelCase            = inputs.input_features
																__lowerCAmelCase            = inputs.attention_mask
																__lowerCAmelCase            = [np.sum(_A    ) for x in attention_mask]

																self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
																self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6    )
																self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
																self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6    )
																self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in range(8_0_0	,    1_4_0_0	,    2_0_0    )]
											__lowerCAmelCase            = feature_extractor(
											    _A	,    padding="max_length"	,    max_length=4	,    truncation=_A	,    return_tensors="np"	,    return_attention_mask=_A	,    )
											__lowerCAmelCase            = inputs.input_features
											__lowerCAmelCase            = inputs.attention_mask
											__lowerCAmelCase            = np.sum(attention_mask == 1	,    axis=1    )

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
											self._check_zero_mean_unit_variance(input_features[1]    )
											self._check_zero_mean_unit_variance(input_features[2]    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in range(8_0_0	,    1_4_0_0	,    2_0_0    )]
											__lowerCAmelCase            = feature_extractor(
											    _A	,    padding="longest"	,    max_length=4	,    truncation=_A	,    return_tensors="np"	,    return_attention_mask=_A	,    )
											__lowerCAmelCase            = inputs.input_features
											__lowerCAmelCase            = inputs.attention_mask
											__lowerCAmelCase            = np.sum(attention_mask == 1	,    axis=1    )

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
											self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
											self._check_zero_mean_unit_variance(input_features[2]    )

											# make sure that if max_length < longest -> then pad to max_length
											self.assertEqual(input_features.shape	,    (3, 4, 2_4)    )

											__lowerCAmelCase            = [floats_list((1, x)    )[0] for x in range(8_0_0	,    1_4_0_0	,    2_0_0    )]
											__lowerCAmelCase            = feature_extractor(
											    _A	,    padding="longest"	,    max_length=1_6	,    truncation=_A	,    return_tensors="np"	,    return_attention_mask=_A	,    )
											__lowerCAmelCase            = inputs.input_features
											__lowerCAmelCase            = inputs.attention_mask
											__lowerCAmelCase            = np.sum(attention_mask == 1	,    axis=1    )

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
											self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
											self._check_zero_mean_unit_variance(input_features[2]    )

											# make sure that if max_length < longest -> then pad to max_length
											self.assertEqual(input_features.shape	,    (3, 6, 2_4)    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											import torch

											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = np.random.rand(1_0_0	,    3_2    ).astype(np.floataa    )
											__lowerCAmelCase            = np_speech_inputs.tolist()

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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											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(_A    )    )[:num_samples]["audio"]

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = np.array([
											    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
											    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
											    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
											]    )
											# fmt: on

											__lowerCAmelCase            = self._load_datasamples(1    )
											__lowerCAmelCase            = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											__lowerCAmelCase            = feature_extractor(_A	,    return_tensors="pt"    ).input_features
											self.assertEquals(input_features.shape	,    (1, 5_8_4, 2_4)    )
											self.assertTrue(np.allclose(input_features[0, 0, :3_0]	,    _A	,    atol=1E-4    )    )




