import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	old_name

 if "patch_embed" in old_name:
  __a							,  __a							,  __a							=	old_name.split("."       )

  if layer == "0":
   __a							=	old_name.replace("0"				,					"convolution1"       )
  elif layer == "1":
   __a							=	old_name.replace("1"				,					"batchnorm_before"       )
  elif layer == "3":
   __a							=	old_name.replace("3"				,					"convolution2"       )
  else:
   __a							=	old_name.replace("4"				,					"batchnorm_after"       )

 if "network" in old_name and re.search(r"\d\.\d"				,					_A       ):
  __a							=	r"\b\d{2}\b"
  if bool(re.search(_A				,					_A       )       ):
   __a							=	re.search(r"\d\.\d\d."				,					_A       ).group()
  else:
   __a							=	re.search(r"\d\.\d."				,					_A       ).group()
  if int(match[0]       ) < 6:
   __a							=	old_name.replace(_A				,					""       )
   __a							=	trimmed_name.replace("network"				,					match[0] + ".meta4D_layers.blocks." + match[2:-1]       )
   __a							=	"intermediate_stages." + trimmed_name
  else:
   __a							=	old_name.replace(_A				,					""       )
   if int(match[2]       ) < num_meta4D_last_stage:
    __a							=	trimmed_name.replace("network"				,					"meta4D_layers.blocks." + match[2]       )
   else:
    __a							=	str(int(match[2]       ) - num_meta4D_last_stage       )
    __a							=	trimmed_name.replace("network"				,					"meta3D_layers.blocks." + layer_index       )
    if "norm1" in old_name:
     __a							=	trimmed_name.replace("norm1"				,					"layernorm1"       )
    elif "norm2" in old_name:
     __a							=	trimmed_name.replace("norm2"				,					"layernorm2"       )
    elif "fc1" in old_name:
     __a							=	trimmed_name.replace("fc1"				,					"linear_in"       )
    elif "fc2" in old_name:
     __a							=	trimmed_name.replace("fc2"				,					"linear_out"       )

   __a							=	"last_stage." + trimmed_name

 elif "network" in old_name and re.search(r".\d."				,					_A       ):
  __a							=	old_name.replace("network"				,					"intermediate_stages"       )

 if "fc" in new_name:
  __a							=	new_name.replace("fc"				,					"convolution"       )
 elif ("norm1" in new_name) and ("layernorm1" not in new_name):
  __a							=	new_name.replace("norm1"				,					"batchnorm_before"       )
 elif ("norm2" in new_name) and ("layernorm2" not in new_name):
  __a							=	new_name.replace("norm2"				,					"batchnorm_after"       )
 if "proj" in new_name:
  __a							=	new_name.replace("proj"				,					"projection"       )
 if "dist_head" in new_name:
  __a							=	new_name.replace("dist_head"				,					"distillation_classifier"       )
 elif "head" in new_name:
  __a							=	new_name.replace("head"				,					"classifier"       )
 elif "patch_embed" in new_name:
  __a							=	"efficientformer." + new_name
 elif new_name == "norm.weight" or new_name == "norm.bias":
  __a							=	new_name.replace("norm"				,					"layernorm"       )
  __a							=	"efficientformer." + new_name
 else:
  __a							=	"efficientformer.encoder." + new_name

 return new_name



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 for key in checkpoint.copy().keys():
  __a							=	checkpoint.pop(_A       )
  __a							=	val

 return checkpoint



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )

 return image



def 	__A		(      _A				,					_A				,					_A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )["model"]
 __a							=	EfficientFormerConfig.from_json_file(_A       )
 __a							=	EfficientFormerForImageClassificationWithTeacher(_A       )
 __a							=	"_".join(checkpoint_path.split("/"       )[-1].split("."       )[0].split("_"       )[:-1]       )

 __a							=	config.depths[-1] - config.num_metaad_blocks + 1
 __a							=	convert_torch_checkpoint(_A				,					_A       )

 model.load_state_dict(_A       )
 model.eval()

 __a							=	{
     "bilinear": PILImageResampling.BILINEAR,
     "bicubic": PILImageResampling.BICUBIC,
     "nearest": PILImageResampling.NEAREST,
 }

 # prepare image
 __a							=	prepare_img()
 __a							=	256
 __a							=	224
 __a							=	EfficientFormerImageProcessor(
     size={"shortest_edge": image_size}				,					crop_size={"height": crop_size, "width": crop_size}				,					resample=pillow_resamplings["bicubic"]				,					)
 __a							=	processor(images=_A				,					return_tensors="pt"       ).pixel_values

 # original processing pipeline
 __a							=	Compose(
     [
         Resize(_A				,					interpolation=pillow_resamplings["bicubic"]       ),
         CenterCrop(_A       ),
         ToTensor(),
         Normalize(_A				,					_A       ),
     ]       )
 __a							=	image_transforms(_A       ).unsqueeze(0       )

 assert torch.allclose(_A				,					_A       )

 __a							=	model(_A       )
 __a							=	outputs.logits

 __a							=	(1, 1000)

 if "l1" in model_name:
  __a							=	torch.Tensor(
      [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]       )
  assert torch.allclose(logits[0, :10]				,					_A				,					atol=1E-3       )
  assert logits.shape == expected_shape
 elif "l3" in model_name:
  __a							=	torch.Tensor(
      [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]       )
  assert torch.allclose(logits[0, :10]				,					_A				,					atol=1E-3       )
  assert logits.shape == expected_shape
 elif "l7" in model_name:
  __a							=	torch.Tensor(
      [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]       )
  assert logits.shape == expected_shape
 else:
  raise ValueError(
      f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""       )

 # Save Checkpoints
 Path(_A       ).mkdir(exist_ok=_A       )
 model.save_pretrained(_A       )
 print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""       )
 processor.save_pretrained(_A       )
 print(f"""Processor successfuly saved at {pytorch_dump_path}"""       )

 if push_to_hub:
  print("Pushing model to the hub..."       )

  model.push_to_hub(
      repo_id=f"""Bearnardd/{pytorch_dump_path}"""				,					commit_message="Add model"				,					use_temp_dir=_A				,					)
  processor.push_to_hub(
      repo_id=f"""Bearnardd/{pytorch_dump_path}"""				,					commit_message="Add image processor"				,					use_temp_dir=_A				,					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--pytorch_model_path""",
      default=None,
      type=str,
      required=True,
      help="""Path to EfficientFormer pytorch checkpoint.""",
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help="""The json file for EfficientFormer model config.""",
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )

  parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
  parser.add_argument(
      """--no-push_to_hub""",
      dest="""push_to_hub""",
      action="""store_false""",
      help="""Do not push model and image processor to the hub""",
  )
  parser.set_defaults(push_to_hub=True)

  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=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)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

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

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	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"							)

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

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	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 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


