
'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = SwinvaConfig()
      _lowerCamelCase					: List[Any]		    = swinva_name.split("_"				)

      _lowerCamelCase					: Optional[int]		    = name_split[1]
      if "to" in name_split[3]:
            _lowerCamelCase					: Optional[int]		    = int(name_split[3][-3:]				)
      else:
            _lowerCamelCase					: Tuple		    = int(name_split[3]				)
      if "to" in name_split[2]:
            _lowerCamelCase					: str		    = int(name_split[2][-2:]				)
      else:
            _lowerCamelCase					: str		    = int(name_split[2][6:]				)

      if model_size == "tiny":
            _lowerCamelCase					: Optional[Any]		    = 96
            _lowerCamelCase					: List[Any]		    = (2, 2, 6, 2)
            _lowerCamelCase					: int		    = (3, 6, 12, 24)
      elif model_size == "small":
            _lowerCamelCase					: Optional[int]		    = 96
            _lowerCamelCase					: int		    = (2, 2, 18, 2)
            _lowerCamelCase					: List[str]		    = (3, 6, 12, 24)
      elif model_size == "base":
            _lowerCamelCase					: Dict		    = 128
            _lowerCamelCase					: List[str]		    = (2, 2, 18, 2)
            _lowerCamelCase					: Dict		    = (4, 8, 16, 32)
      else:
            _lowerCamelCase					: List[Any]		    = 192
            _lowerCamelCase					: Any		    = (2, 2, 18, 2)
            _lowerCamelCase					: Optional[int]		    = (6, 12, 24, 48)

      if "to" in swinva_name:
            _lowerCamelCase					: Optional[int]		    = (12, 12, 12, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            _lowerCamelCase					: Dict		    = 21841
            _lowerCamelCase					: List[Any]		    = "huggingface/label-files"
            _lowerCamelCase					: List[Any]		    = "imagenet-22k-id2label.json"
            _lowerCamelCase					: Union[str, Any]		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
            _lowerCamelCase					: int		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
            _lowerCamelCase					: Union[str, Any]		    = idalabel
            _lowerCamelCase					: Union[str, Any]		    = {v: k for k, v in idalabel.items()}

      else:
            _lowerCamelCase					: str		    = 1000
            _lowerCamelCase					: int		    = "huggingface/label-files"
            _lowerCamelCase					: Optional[Any]		    = "imagenet-1k-id2label.json"
            _lowerCamelCase					: Dict		    = json.load(open(hf_hub_download(_lowerCAmelCase					,						_lowerCAmelCase					,						repo_type="dataset"				)					,						"r"				)				)
            _lowerCamelCase					: Tuple		    = {int(_lowerCAmelCase				): v for k, v in idalabel.items()}
            _lowerCamelCase					: Union[str, Any]		    = idalabel
            _lowerCamelCase					: Union[str, Any]		    = {v: k for k, v in idalabel.items()}

      _lowerCamelCase					: Any		    = img_size
      _lowerCamelCase					: Tuple		    = num_classes
      _lowerCamelCase					: Any		    = embed_dim
      _lowerCamelCase					: str		    = depths
      _lowerCamelCase					: List[Any]		    = num_heads
      _lowerCamelCase					: List[Any]		    = window_size

      return config







def 		A_							(						_lowerCAmelCase :     int				):



      """simple docstring"""




      if "patch_embed.proj" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("patch_embed.proj"					,						"embeddings.patch_embeddings.projection"				)
      if "patch_embed.norm" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("patch_embed.norm"					,						"embeddings.norm"				)
      if "layers" in name:
            _lowerCamelCase					: List[str]		    = "encoder." + name
      if "attn.proj" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("attn.proj"					,						"attention.output.dense"				)
      if "attn" in name:
            _lowerCamelCase					: int		    = 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					: str		    = name.replace("mlp.fc1"					,						"intermediate.dense"				)
      if "mlp.fc2" in name:
            _lowerCamelCase					: int		    = name.replace("mlp.fc2"					,						"output.dense"				)
      if "q_bias" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("q_bias"					,						"query.bias"				)
      if "k_bias" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("k_bias"					,						"key.bias"				)
      if "v_bias" in name:
            _lowerCamelCase					: Any		    = name.replace("v_bias"					,						"value.bias"				)
      if "cpb_mlp" in name:
            _lowerCamelCase					: Any		    = name.replace("cpb_mlp"					,						"continuous_position_bias_mlp"				)
      if name == "norm.weight":
            _lowerCamelCase					: List[str]		    = "layernorm.weight"
      if name == "norm.bias":
            _lowerCamelCase					: List[Any]		    = "layernorm.bias"

      if "head" in name:
            _lowerCamelCase					: Tuple		    = name.replace("head"					,						"classifier"				)
      else:
            _lowerCamelCase					: Any		    = "swinv2." + name

      return name







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      for key in orig_state_dict.copy().keys():
            _lowerCamelCase					: Any		    = orig_state_dict.pop(_lowerCAmelCase				)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  _lowerCamelCase					: str		    = key.split("."				)
                  _lowerCamelCase					: Any		    = int(key_split[1]				)
                  _lowerCamelCase					: Optional[Any]		    = int(key_split[3]				)
                  _lowerCamelCase					: Tuple		    = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _lowerCamelCase					: Optional[int]		    = val[:dim, :]
                        _lowerCamelCase					: Dict		    = val[dim : dim * 2, :]
                        _lowerCamelCase					: Union[str, Any]		    = val[-dim:, :]
                  else:
                        _lowerCamelCase					: int		    = val[:dim]
                        _lowerCamelCase					: str		    = val[
                            dim : dim * 2
                        ]
                        _lowerCamelCase					: str		    = val[-dim:]
            else:
                  _lowerCamelCase					: List[Any]		    = val

      return orig_state_dict







def 		A_							(						_lowerCAmelCase :     Dict					,						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase					: List[str]		    = timm.create_model(_lowerCAmelCase					,						pretrained=_lowerCAmelCase				)
      timm_model.eval()

      _lowerCamelCase					: Dict		    = get_swinva_config(_lowerCAmelCase				)
      _lowerCamelCase					: Any		    = SwinvaForImageClassification(_lowerCAmelCase				)
      model.eval()

      _lowerCamelCase					: Optional[int]		    = convert_state_dict(timm_model.state_dict()					,						_lowerCAmelCase				)
      model.load_state_dict(_lowerCAmelCase				)

      _lowerCamelCase					: List[str]		    = "http://images.cocodataset.org/val2017/000000039769.jpg"

      _lowerCamelCase					: Tuple		    = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"					,						"-"				)				)				)
      _lowerCamelCase					: Tuple		    = Image.open(requests.get(_lowerCAmelCase					,						stream=_lowerCAmelCase				).raw				)
      _lowerCamelCase					: Any		    = image_processor(images=_lowerCAmelCase					,						return_tensors="pt"				)

      _lowerCamelCase					: Dict		    = timm_model(inputs["pixel_values"]				)
      _lowerCamelCase					: List[Any]		    = model(**_lowerCAmelCase				).logits

      assert torch.allclose(_lowerCAmelCase					,						_lowerCAmelCase					,						atol=1E-3				)

      print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'				)
      model.save_pretrained(_lowerCAmelCase				)

      print(F'Saving image processor to {pytorch_dump_folder_path}'				)
      image_processor.save_pretrained(_lowerCAmelCase				)

      model.push_to_hub(
          repo_path_or_name=Path(_lowerCAmelCase					,						_lowerCAmelCase				)					,						organization="nandwalritik"					,						commit_message="Add model"					,						)


if __name__ == "__main__":
 UpperCAmelCase_							: Union[str, Any] =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swinv2_name',
     default='swinv2_tiny_patch4_window8_256',
     type=str,
     help='Name of the Swinv2 timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 UpperCAmelCase_							: Tuple =							parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)


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				)
