



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch

if is_datasets_available():
							from datasets import load_dataset

SCREAMING_SNAKE_CASE__       =      random.Random()


def A						(       __UpperCamelCase ,	__UpperCamelCase=1.0 ,	__UpperCamelCase=None ,	__UpperCamelCase=None					)						->      Optional[int]:
			if rng is None:
						A__       =    global_rng

			A__       =    []
			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					:       Dict				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       str=7				,					_snake_case					:       List[str]=4_00				,					_snake_case					:       Union[str, Any]=20_00				,					_snake_case					:       str=20_48				,					_snake_case					:       int=1_28				,					_snake_case					:       Optional[int]=1				,					_snake_case					:       Dict=5_12				,					_snake_case					:       Tuple=30				,					_snake_case					:       int=4_41_00				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    min_seq_length
								A__       =    max_seq_length
								A__       =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A__       =    spectrogram_length
								A__       =    feature_size
								A__       =    num_audio_channels
								A__       =    hop_length
								A__       =    chunk_length
								A__       =    sampling_rate



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}



					def     _a							(     self					:       int				,					_snake_case					:       Tuple=False				,					_snake_case					:       str=False					):






								"""simple docstring"""


								def _flatten(_snake_case					:       List[str]					):
											return list(itertools.chain(*_snake_case					)					)

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







@require_torch
@require_torchaudio
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Optional[Any]			       =    TvltFeatureExtractor



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    TvltFeatureExtractionTester(self					)



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.feature_extraction_class(**self.feat_extract_dict					)
								self.assertTrue(hasattr(_snake_case				,					'spectrogram_length'					)					)
								self.assertTrue(hasattr(_snake_case				,					'feature_size'					)					)
								self.assertTrue(hasattr(_snake_case				,					'num_audio_channels'					)					)
								self.assertTrue(hasattr(_snake_case				,					'hop_length'					)					)
								self.assertTrue(hasattr(_snake_case				,					'chunk_length'					)					)
								self.assertTrue(hasattr(_snake_case				,					'sampling_rate'					)					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.feature_extraction_class(**self.feat_extract_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											A__       =    feat_extract_first.save_pretrained(_snake_case					)[0]
											check_json_file_has_correct_format(_snake_case					)
											A__       =    self.feature_extraction_class.from_pretrained(_snake_case					)

								A__       =    feat_extract_first.to_dict()
								A__       =    feat_extract_second.to_dict()
								A__       =    dict_first.pop('mel_filters'					)
								A__       =    dict_second.pop('mel_filters'					)
								self.assertTrue(np.allclose(_snake_case				,					_snake_case					)					)
								self.assertEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.feature_extraction_class(**self.feat_extract_dict					)

								with tempfile.TemporaryDirectory() as tmpdirname:
											A__       =    os.path.join(_snake_case				,					'feat_extract.json'					)
											feat_extract_first.to_json_file(_snake_case					)
											A__       =    self.feature_extraction_class.from_json_file(_snake_case					)

								A__       =    feat_extract_first.to_dict()
								A__       =    feat_extract_second.to_dict()
								A__       =    dict_first.pop('mel_filters'					)
								A__       =    dict_second.pop('mel_filters'					)
								self.assertTrue(np.allclose(_snake_case				,					_snake_case					)					)
								self.assertEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.feature_extraction_class(**self.feat_extract_dict					)

								# create three inputs of length 800, 1000, and 1200
								A__       =    [floats_list((1, x)					)[0] for x in range(8_00				,					14_00				,					2_00					)]
								A__       =    [np.asarray(_snake_case					) for speech_input in speech_inputs]

								# Test not batched input
								A__       =    feature_extractor(np_speech_inputs[0]				,					return_tensors='np'				,					sampling_rate=4_41_00					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test batched
								A__       =    feature_extractor(_snake_case				,					return_tensors='np'				,					sampling_rate=4_41_00					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test audio masking
								A__       =    feature_extractor(
								    _snake_case				,					return_tensors='np'				,					sampling_rate=4_41_00				,					mask_audio=_snake_case					).audio_values

								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)

								# Test 2-D numpy arrays are batched.
								A__       =    [floats_list((1, x)					)[0] for x in (8_00, 8_00, 8_00)]
								A__       =    np.asarray(_snake_case					)
								A__       =    feature_extractor(_snake_case				,					return_tensors='np'				,					sampling_rate=4_41_00					).audio_values
								self.assertTrue(encoded_audios.ndim == 4					)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size					)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length					)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


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

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



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self._load_datasamples(1					)
								A__       =    TvltFeatureExtractor()
								A__       =    feature_extractor(_snake_case				,					return_tensors='pt'					).audio_values

								self.assertEquals(audio_values.shape				,					(1, 1, 1_92, 1_28)					)

								A__       =    torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]					)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,					_snake_case				,					atol=1E-4					)					)









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()
SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


def A						(       __UpperCamelCase					)						->      YolosConfig:
			A__       =    YolosConfig()

			# size of the architecture
			if "yolos_ti" in yolos_name:
						A__       =    192
						A__       =    768
						A__       =    12
						A__       =    3
						A__       =    [800, 1_333]
						A__       =    False
			elif yolos_name == "yolos_s_dWr":
						A__       =    330
						A__       =    14
						A__       =    6
						A__       =    1_320
			elif "yolos_s" in yolos_name:
						A__       =    384
						A__       =    1_536
						A__       =    12
						A__       =    6
			elif "yolos_b" in yolos_name:
						A__       =    [800, 1_344]

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

			return config


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      str:
			for i in range(config.num_hidden_layers					):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''					)
						A__       =    state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''					)
						# next, add query, keys and values (in that order) to the state dict
						A__       =    in_proj_weight[: config.hidden_size, :]
						A__       =    in_proj_bias[: config.hidden_size]
						A__       =    in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						A__       =    in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						A__       =    in_proj_weight[-config.hidden_size :, :]
						A__       =    in_proj_bias[-config.hidden_size :]


def A						(       __UpperCamelCase					)						->      str:
			if "backbone" in name:
						A__       =    name.replace('backbone' ,	'vit'					)
			if "cls_token" in name:
						A__       =    name.replace('cls_token' ,	'embeddings.cls_token'					)
			if "det_token" in name:
						A__       =    name.replace('det_token' ,	'embeddings.detection_tokens'					)
			if "mid_pos_embed" in name:
						A__       =    name.replace('mid_pos_embed' ,	'encoder.mid_position_embeddings'					)
			if "pos_embed" in name:
						A__       =    name.replace('pos_embed' ,	'embeddings.position_embeddings'					)
			if "patch_embed.proj" in name:
						A__       =    name.replace('patch_embed.proj' ,	'embeddings.patch_embeddings.projection'					)
			if "blocks" in name:
						A__       =    name.replace('blocks' ,	'encoder.layer'					)
			if "attn.proj" in name:
						A__       =    name.replace('attn.proj' ,	'attention.output.dense'					)
			if "attn" in name:
						A__       =    name.replace('attn' ,	'attention.self'					)
			if "norm1" in name:
						A__       =    name.replace('norm1' ,	'layernorm_before'					)
			if "norm2" in name:
						A__       =    name.replace('norm2' ,	'layernorm_after'					)
			if "mlp.fc1" in name:
						A__       =    name.replace('mlp.fc1' ,	'intermediate.dense'					)
			if "mlp.fc2" in name:
						A__       =    name.replace('mlp.fc2' ,	'output.dense'					)
			if "class_embed" in name:
						A__       =    name.replace('class_embed' ,	'class_labels_classifier'					)
			if "bbox_embed" in name:
						A__       =    name.replace('bbox_embed' ,	'bbox_predictor'					)
			if "vit.norm" in name:
						A__       =    name.replace('vit.norm' ,	'vit.layernorm'					)

			return name


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      dict:
			for key in orig_state_dict.copy().keys():
						A__       =    orig_state_dict.pop(__UpperCamelCase					)

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

			return orig_state_dict


def A						(       )						->      torch.Tensor:
			A__       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__       =    Image.open(requests.get(__UpperCamelCase ,	stream=__UpperCamelCase					).raw					)
			return im


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase = False					)						->      List[str]:
			A__       =    get_yolos_config(__UpperCamelCase					)

			# load original state_dict
			A__       =    torch.load(__UpperCamelCase ,	map_location='cpu'					)['model']

			# load 🤗 model
			A__       =    YolosForObjectDetection(__UpperCamelCase					)
			model.eval()
			A__       =    convert_state_dict(__UpperCamelCase ,	__UpperCamelCase					)
			model.load_state_dict(__UpperCamelCase					)

			# Check outputs on an image, prepared by YolosImageProcessor
			A__       =    800 if yolos_name != 'yolos_ti' else 512
			A__       =    YolosImageProcessor(format='coco_detection' ,	size=__UpperCamelCase					)
			A__       =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
			A__       =    model(**__UpperCamelCase					)
			A__     , A__       =    outputs.logits, outputs.pred_boxes

			A__     , A__       =    None, None
			if yolos_name == "yolos_ti":
						A__       =    torch.tensor(
						    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
						A__       =    torch.tensor(
						    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
			elif yolos_name == "yolos_s_200_pre":
						A__       =    torch.tensor(
						    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
						A__       =    torch.tensor(
						    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
			elif yolos_name == "yolos_s_300_pre":
						A__       =    torch.tensor(
						    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
						A__       =    torch.tensor(
						    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
			elif yolos_name == "yolos_s_dWr":
						A__       =    torch.tensor(
						    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
						A__       =    torch.tensor(
						    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
			elif yolos_name == "yolos_base":
						A__       =    torch.tensor(
						    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
						A__       =    torch.tensor(
						    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
			else:
						raise ValueError(f'''Unknown yolos_name: {yolos_name}'''					)

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

			Path(__UpperCamelCase					).mkdir(exist_ok=__UpperCamelCase					)
			print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(__UpperCamelCase					)
			print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
			image_processor.save_pretrained(__UpperCamelCase					)

			if push_to_hub:
						A__       =    {
						    '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...'					)
						A__       =    model_mapping[yolos_name]
						image_processor.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)
						model.push_to_hub(__UpperCamelCase ,	organization='hustvl'					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      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.'''
							)

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




