'''simple docstring'''







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    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	YolosConfig:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = YolosConfig()

							# size of the architecture
							if "yolos_ti" in yolos_name:
														SCREAMING_SNAKE_CASE__     :List[Any]						    = 1_9_2
														SCREAMING_SNAKE_CASE__     :Tuple						    = 7_6_8
														SCREAMING_SNAKE_CASE__     :int						    = 1_2
														SCREAMING_SNAKE_CASE__     :int						    = 3
														SCREAMING_SNAKE_CASE__     :str						    = [8_0_0, 1_3_3_3]
														SCREAMING_SNAKE_CASE__     :Dict						    = False
							elif yolos_name == "yolos_s_dWr":
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 3_3_0
														SCREAMING_SNAKE_CASE__     :Any						    = 1_4
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 6
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 1_3_2_0
							elif "yolos_s" in yolos_name:
														SCREAMING_SNAKE_CASE__     :int						    = 3_8_4
														SCREAMING_SNAKE_CASE__     :Tuple						    = 1_5_3_6
														SCREAMING_SNAKE_CASE__     :int						    = 1_2
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 6
							elif "yolos_b" in yolos_name:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = [8_0_0, 1_3_4_4]

							SCREAMING_SNAKE_CASE__     :Any						    = 9_1
							SCREAMING_SNAKE_CASE__     :Tuple						    = 'huggingface/label-files'
							SCREAMING_SNAKE_CASE__     :Tuple						    = 'coco-detection-id2label.json'
							SCREAMING_SNAKE_CASE__     :int						    = json.load(open(hf_hub_download(_snake_case				,						_snake_case				,						repo_type='dataset'		)				,						'r'		)		)
							SCREAMING_SNAKE_CASE__     :List[str]						    = {int(_snake_case		): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE__     :Any						    = idalabel
							SCREAMING_SNAKE_CASE__     :Tuple						    = {v: k for k, v in idalabel.items()}

							return config


def    lowerCamelCase       (			UpperCAmelCase__				:  dict				,						UpperCAmelCase__				:  YolosConfig				,						UpperCAmelCase__				:  bool = False		)			->	List[Any]:


							'''simple docstring'''






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


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	str:


							'''simple docstring'''






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

							return name


def    lowerCamelCase       (			UpperCAmelCase__				:  dict				,						UpperCAmelCase__				:  YolosForObjectDetection		)			->	dict:


							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__     :List[Any]						    = orig_state_dict.pop(_snake_case		)

														if "qkv" in key:
																					SCREAMING_SNAKE_CASE__     :Any						    = key.split('.'		)
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = int(key_split[2]		)
																					SCREAMING_SNAKE_CASE__     :str						    = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
																					if "weight" in key:
																												SCREAMING_SNAKE_CASE__     :List[Any]						    = val[:dim, :]
																												SCREAMING_SNAKE_CASE__     :Tuple						    = val[
																												    dim : dim * 2, :
																												]
																												SCREAMING_SNAKE_CASE__     :Tuple						    = val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE__     :Any						    = val[:dim]
																												SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = val[dim : dim * 2]
																												SCREAMING_SNAKE_CASE__     :List[str]						    = val[-dim:]
														else:
																					SCREAMING_SNAKE_CASE__     :Tuple						    = val

							return orig_state_dict


def    lowerCamelCase       (			)			->	torch.Tensor:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = 'http://images.cocodataset.org/val2017/000000039769.jpg'
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = Image.open(requests.get(_snake_case				,						stream=_snake_case		).raw		)
							return im


@torch.no_grad()
def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  bool = False		)			->	Union[str, Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = get_yolos_config(_snake_case		)

							# load original state_dict
							SCREAMING_SNAKE_CASE__     :int						    = torch.load(_snake_case				,						map_location='cpu'		)['model']

							# load 🤗 model
							SCREAMING_SNAKE_CASE__     :Tuple						    = YolosForObjectDetection(_snake_case		)
							model.eval()
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = convert_state_dict(_snake_case				,						_snake_case		)
							model.load_state_dict(_snake_case		)

							# Check outputs on an image, prepared by YolosImageProcessor
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 8_0_0 if yolos_name != 'yolos_ti' else 5_1_2
							SCREAMING_SNAKE_CASE__     :int						    = YolosImageProcessor(format='coco_detection'				,						size=_snake_case		)
							SCREAMING_SNAKE_CASE__     :Any						    = image_processor(images=prepare_img()				,						return_tensors='pt'		)
							SCREAMING_SNAKE_CASE__     :Dict						    = model(**_snake_case		)
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = outputs.logits, outputs.pred_boxes

							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = None, None
							if yolos_name == "yolos_ti":
														SCREAMING_SNAKE_CASE__     :int						    = torch.tensor(
														    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]		)
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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":
														SCREAMING_SNAKE_CASE__     :Dict						    = torch.tensor(
														    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		)
														SCREAMING_SNAKE_CASE__     :Dict						    = 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":
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.tensor(
														    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]		)
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 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":
														SCREAMING_SNAKE_CASE__     :str						    = torch.tensor(
														    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]		)
														SCREAMING_SNAKE_CASE__     :List[Any]						    = 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":
														SCREAMING_SNAKE_CASE__     :str						    = torch.tensor(
														    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]		)
														SCREAMING_SNAKE_CASE__     :Any						    = 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]				,						_snake_case				,						atol=1e-4		)
							assert torch.allclose(pred_boxes[0, :3, :3]				,						_snake_case				,						atol=1e-4		)

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

							if push_to_hub:
														SCREAMING_SNAKE_CASE__     :Dict						    = {
														    '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...'		)
														SCREAMING_SNAKE_CASE__     :Any						    = model_mapping[yolos_name]
														image_processor.push_to_hub(_snake_case				,						organization='hustvl'		)
														model.push_to_hub(_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)












"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

