


"""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 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      _lowerCamelCase	:					Union[str, Any]   =		192
      _lowerCamelCase	:					int   =		768
      _lowerCamelCase	:					Optional[Any]   =		12
      _lowerCamelCase	:					Optional[int]   =		3
      _lowerCamelCase	:					str   =		[800, 1333]
      _lowerCamelCase	:					Dict   =		False
   elif yolos_name == "yolos_s_dWr":
      _lowerCamelCase	:					List[str]   =		330
      _lowerCamelCase	:					Tuple   =		14
      _lowerCamelCase	:					List[Any]   =		6
      _lowerCamelCase	:					Optional[int]   =		1320
   elif "yolos_s" in yolos_name:
      _lowerCamelCase	:					int   =		384
      _lowerCamelCase	:					Optional[Any]   =		1536
      _lowerCamelCase	:					Union[str, Any]   =		12
      _lowerCamelCase	:					Any   =		6
   elif "yolos_b" in yolos_name:
      _lowerCamelCase	:					List[Any]   =		[800, 1344]

   _lowerCamelCase	:					Dict   =		91
   _lowerCamelCase	:					int   =		"""huggingface/label-files"""
   _lowerCamelCase	:					List[Any]   =		"""coco-detection-id2label.json"""
   _lowerCamelCase	:					Optional[Any]   =		json.load(open(hf_hub_download(__snake_case						, __snake_case						, repo_type="""dataset"""   )						, """r"""   )   )
   _lowerCamelCase	:					Optional[Any]   =		{int(__snake_case   ): v for k, v in idalabel.items()}
   _lowerCamelCase	:					int   =		idalabel
   _lowerCamelCase	:					Optional[Any]   =		{v: k for k, v in idalabel.items()}

   return config

def 				_snake_case      (   __snake_case							:      dict						, __snake_case							:      YolosConfig						, __snake_case							:      bool = False   ):




   """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)
      _lowerCamelCase	:					int   =		state_dict.pop(F'blocks.{i}.attn.qkv.weight'   )
      _lowerCamelCase	:					Any   =		state_dict.pop(F'blocks.{i}.attn.qkv.bias'   )
      # next, add query, keys and values (in that order) to the state dict
      _lowerCamelCase	:					Tuple   =		in_proj_weight[: config.hidden_size, :]
      _lowerCamelCase	:					Optional[int]   =		in_proj_bias[: config.hidden_size]
      _lowerCamelCase	:					List[str]   =		in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      _lowerCamelCase	:					int   =		in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      _lowerCamelCase	:					int   =		in_proj_weight[-config.hidden_size :, :]
      _lowerCamelCase	:					Dict   =		in_proj_bias[-config.hidden_size :]

def 				_snake_case      (   __snake_case							:      str   ):




   """simple docstring"""







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

   return name

def 				_snake_case      (   __snake_case							:      dict						, __snake_case							:      YolosForObjectDetection   ):




   """simple docstring"""







   for key in orig_state_dict.copy().keys():
      _lowerCamelCase	:					List[str]   =		orig_state_dict.pop(__snake_case   )

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

   return orig_state_dict

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					List[Any]   =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _lowerCamelCase	:					Any   =		Image.open(requests.get(__snake_case						, stream=__snake_case   ).raw   )
   return im

@torch.no_grad()
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str						, __snake_case							:      str						, __snake_case							:      bool = False   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		get_yolos_config(__snake_case   )

   # load original state_dict
   _lowerCamelCase	:					Any   =		torch.load(__snake_case						, map_location="""cpu"""   )["""model"""]

   # load 🤗 model
   _lowerCamelCase	:					Dict   =		YolosForObjectDetection(__snake_case   )
   model.eval()
   _lowerCamelCase	:					Optional[Any]   =		convert_state_dict(__snake_case						, __snake_case   )
   model.load_state_dict(__snake_case   )

   # Check outputs on an image, prepared by YolosImageProcessor
   _lowerCamelCase	:					List[Any]   =		800 if yolos_name != """yolos_ti""" else 512
   _lowerCamelCase	:					str   =		YolosImageProcessor(format="""coco_detection"""						, size=__snake_case   )
   _lowerCamelCase	:					List[str]   =		image_processor(images=prepare_img()						, return_tensors="""pt"""   )
   _lowerCamelCase	:					Optional[Any]   =		model(**__snake_case   )
   _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		outputs.logits, outputs.pred_boxes

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




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
A									=  logging.getLogger()

A									=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				os.makedirs(__UpperCamelCase			,					exist_ok=__UpperCamelCase				)
				snake_case_   		=			{'source': 'What is love ?', 'target': 'life'}
				snake_case_   		=			{'train': 12, 'val': 2, 'test': 2}
				for split in ["train", "test", "val"]:
					for field in ["source", "target"]:
						snake_case_   		=			'\n'.join([contents[field]] * n_lines[split]				)
						with open(os.path.join(__UpperCamelCase			,					f"""{split}.{field}"""				)			,					'w'				) as f:
							f.write(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase = "pytorch"				):

				"""simple docstring"""


				snake_case_   		=			self.get_auto_remove_tmp_dir()
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'output'				)
				snake_case_   		=			os.path.join(__UpperCamelCase			,					'data'				)
				self._create_dummy_data(data_dir=__UpperCamelCase				)

				snake_case_   		=			f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

				if gpus > 0:
					testargs.append(f"""--gpus={gpus}"""				)
					if is_apex_available():
						testargs.append('--fp16'				)
				else:
					testargs.append('--gpus=0'				)
					testargs.append('--distributed_backend=ddp_cpu'				)
					testargs.append('--num_processes=2'				)

				snake_case_   		=			[sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
				execute_subprocess_async(__UpperCamelCase			,					env=self.get_env()				)

				snake_case_   		=			os.path.join(__UpperCamelCase			,					'metrics.json'				)
				with open(__UpperCamelCase				) as f:
					snake_case_   		=			json.load(__UpperCamelCase				)
				return result


			@require_torch_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_multi_gpu
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=2				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)


			@require_torch_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)







			@require_torch_multi_gpu
			@require_ray
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self._run_finetune(gpus=1			,					distributed_retriever='ray'				)
				self.assertGreaterEqual(result['test'][0]['test_avg_em']			,					0.2				)
