

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        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Tuple    =				old_name

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

		if layer == "0":
			SCREAMING_SNAKE_CASE      :					Tuple    =				old_name.replace("0"							,	"convolution1")
		elif layer == "1":
			SCREAMING_SNAKE_CASE      :					List[str]    =				old_name.replace("1"							,	"batchnorm_before")
		elif layer == "3":
			SCREAMING_SNAKE_CASE      :					str    =				old_name.replace("3"							,	"convolution2")
		else:
			SCREAMING_SNAKE_CASE      :					Tuple    =				old_name.replace("4"							,	"batchnorm_after")

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

			SCREAMING_SNAKE_CASE      :					List[str]    =				"last_stage." + trimmed_name

	elif "network" in old_name and re.search(r".\d."							,	_a):
		SCREAMING_SNAKE_CASE      :					List[str]    =				old_name.replace("network"							,	"intermediate_stages")

	if "fc" in new_name:
		SCREAMING_SNAKE_CASE      :					str    =				new_name.replace("fc"							,	"convolution")
	elif ("norm1" in new_name) and ("layernorm1" not in new_name):
		SCREAMING_SNAKE_CASE      :					Any    =				new_name.replace("norm1"							,	"batchnorm_before")
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				new_name.replace("norm2"							,	"batchnorm_after")
	if "proj" in new_name:
		SCREAMING_SNAKE_CASE      :					Any    =				new_name.replace("proj"							,	"projection")
	if "dist_head" in new_name:
		SCREAMING_SNAKE_CASE      :					int    =				new_name.replace("dist_head"							,	"distillation_classifier")
	elif "head" in new_name:
		SCREAMING_SNAKE_CASE      :					Tuple    =				new_name.replace("head"							,	"classifier")
	elif "patch_embed" in new_name:
		SCREAMING_SNAKE_CASE      :					int    =				"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		SCREAMING_SNAKE_CASE      :					Tuple    =				new_name.replace("norm"							,	"layernorm")
		SCREAMING_SNAKE_CASE      :					List[Any]    =				"efficientformer." + new_name
	else:
		SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"efficientformer.encoder." + new_name

	return new_name

def        lowerCamelCase__       (    _a							,	_a):
	for key in checkpoint.copy().keys():
		SCREAMING_SNAKE_CASE      :					List[Any]    =				checkpoint.pop(_a)
		SCREAMING_SNAKE_CASE      :					Dict    =				val

	return checkpoint

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					int    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				Image.open(requests.get(_a							,	stream=_a).raw)

	return image

def        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				torch.load(_a							,	map_location="cpu")["model"]
	SCREAMING_SNAKE_CASE      :					Dict    =				EfficientFormerConfig.from_json_file(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				EfficientFormerForImageClassificationWithTeacher(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				"_".join(checkpoint_path.split("/")[-1].split(".")[0].split("_")[:-1])

	SCREAMING_SNAKE_CASE      :					Tuple    =				config.depths[-1] - config.num_metaad_blocks + 1
	SCREAMING_SNAKE_CASE      :					str    =				convert_torch_checkpoint(_a							,	_a)

	model.load_state_dict(_a)
	model.eval()

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

	# prepare image
	SCREAMING_SNAKE_CASE      :					Tuple    =				prepare_img()
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				256
	SCREAMING_SNAKE_CASE      :					Any    =				224
	SCREAMING_SNAKE_CASE      :					List[str]    =				EfficientFormerImageProcessor(
	    size={"shortest_edge": image_size}							,	crop_size={"height": crop_size, "width": crop_size}							,	resample=pillow_resamplings["bicubic"]							,	)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				processor(images=_a							,	return_tensors="pt").pixel_values

	# original processing pipeline
	SCREAMING_SNAKE_CASE      :					str    =				Compose(
	    [
	        Resize(_a							,	interpolation=pillow_resamplings["bicubic"]),
	        CenterCrop(_a),
	        ToTensor(),
	        Normalize(_a							,	_a),
	    ])
	SCREAMING_SNAKE_CASE      :					List[str]    =				image_transforms(_a).unsqueeze(0)

	assert torch.allclose(_a							,	_a)

	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				model(_a)
	SCREAMING_SNAKE_CASE      :					List[Any]    =				outputs.logits

	SCREAMING_SNAKE_CASE      :					Tuple    =				(1, 1000)

	if "l1" in model_name:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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:
		SCREAMING_SNAKE_CASE      :					Any    =				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:
		SCREAMING_SNAKE_CASE      :					int    =				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__":
			a_     =			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)

			a_     =			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 argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(snake_case_     )
      m.model.load_state_dict(snake_case_,strict=snake_case_     )
      m.half()
      m.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

