

"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			)     ->     Optional[int]:
		__lowerCAmelCase:	List[Any]   				=      []
		for i in range(config.num_hidden_layers			):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F"blocks.{i}.norm1.weight", F"deit.encoder.layer.{i}.layernorm_before.weight")			)
				rename_keys.append((F"blocks.{i}.norm1.bias", F"deit.encoder.layer.{i}.layernorm_before.bias")			)
				rename_keys.append((F"blocks.{i}.attn.proj.weight", F"deit.encoder.layer.{i}.attention.output.dense.weight")			)
				rename_keys.append((F"blocks.{i}.attn.proj.bias", F"deit.encoder.layer.{i}.attention.output.dense.bias")			)
				rename_keys.append((F"blocks.{i}.norm2.weight", F"deit.encoder.layer.{i}.layernorm_after.weight")			)
				rename_keys.append((F"blocks.{i}.norm2.bias", F"deit.encoder.layer.{i}.layernorm_after.bias")			)
				rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"deit.encoder.layer.{i}.intermediate.dense.weight")			)
				rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"deit.encoder.layer.{i}.intermediate.dense.bias")			)
				rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"deit.encoder.layer.{i}.output.dense.weight")			)
				rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"deit.encoder.layer.{i}.output.dense.bias")			)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ("cls_token", "deit.embeddings.cls_token"),
		        ("dist_token", "deit.embeddings.distillation_token"),
		        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
		        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
		        ("pos_embed", "deit.embeddings.position_embeddings"),
		    ]			)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ("norm.weight", "layernorm.weight"),
				        ("norm.bias", "layernorm.bias"),
				        ("pre_logits.fc.weight", "pooler.dense.weight"),
				        ("pre_logits.fc.bias", "pooler.dense.bias"),
				    ]			)

				# if just the base model, we should remove "deit" from all keys that start with "deit"
				__lowerCAmelCase:	List[str]   				=      [(pair[0], pair[1][4:]) if pair[1].startswith("deit"			) else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ("norm.weight", "deit.layernorm.weight"),
				        ("norm.bias", "deit.layernorm.bias"),
				        ("head.weight", "cls_classifier.weight"),
				        ("head.bias", "cls_classifier.bias"),
				        ("head_dist.weight", "distillation_classifier.weight"),
				        ("head_dist.bias", "distillation_classifier.bias"),
				    ]			)

		return rename_keys






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			)     ->     str:




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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	List[str]   				=      dct.pop(lowerCamelCase__			)
		__lowerCAmelCase:	int   				=      val






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Tuple   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	Optional[int]   				=      Image.open(requests.get(lowerCamelCase__ ,			stream=lowerCamelCase__			).raw			)
		return im






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	Any   				=      DeiTConfig()
		# all deit models have fine-tuned heads
		__lowerCAmelCase:	Optional[int]   				=      False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		__lowerCAmelCase:	str   				=      1_0_0_0
		__lowerCAmelCase:	Optional[int]   				=      "huggingface/label-files"
		__lowerCAmelCase:	List[str]   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	Optional[int]   				=      json.load(open(hf_hub_download(lowerCamelCase__ ,			lowerCamelCase__ ,			repo_type="dataset"			) ,			"r"			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      {int(lowerCamelCase__			): v for k, v in idalabel.items()}
		__lowerCAmelCase:	List[Any]   				=      idalabel
		__lowerCAmelCase:	Tuple   				=      {v: k for k, v in idalabel.items()}
		__lowerCAmelCase:	List[str]   				=      int(deit_name[-6:-4]			)
		__lowerCAmelCase:	Tuple   				=      int(deit_name[-3:]			)
		# size of the architecture
		if deit_name[9:].startswith("tiny"			):
				__lowerCAmelCase:	Optional[Any]   				=      1_9_2
				__lowerCAmelCase:	Any   				=      7_6_8
				__lowerCAmelCase:	str   				=      1_2
				__lowerCAmelCase:	Tuple   				=      3
		elif deit_name[9:].startswith("small"			):
				__lowerCAmelCase:	List[str]   				=      3_8_4
				__lowerCAmelCase:	Optional[Any]   				=      1_5_3_6
				__lowerCAmelCase:	Optional[int]   				=      1_2
				__lowerCAmelCase:	List[Any]   				=      6
		if deit_name[9:].startswith("base"			):
				pass
		elif deit_name[4:].startswith("large"			):
				__lowerCAmelCase:	int   				=      1_0_2_4
				__lowerCAmelCase:	List[Any]   				=      4_0_9_6
				__lowerCAmelCase:	Union[str, Any]   				=      2_4
				__lowerCAmelCase:	List[Any]   				=      1_6

		# load original model from timm
		__lowerCAmelCase:	Tuple   				=      timm.create_model(lowerCamelCase__ ,			pretrained=lowerCamelCase__			)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		__lowerCAmelCase:	Tuple   				=      timm_model.state_dict()
		__lowerCAmelCase:	int   				=      create_rename_keys(lowerCamelCase__ ,			lowerCamelCase__			)
		for src, dest in rename_keys:
				rename_key(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__			)
		read_in_q_k_v(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__			)

		# load HuggingFace model
		__lowerCAmelCase:	Union[str, Any]   				=      DeiTForImageClassificationWithTeacher(lowerCamelCase__			).eval()
		model.load_state_dict(lowerCamelCase__			)

		# Check outputs on an image, prepared by DeiTImageProcessor
		__lowerCAmelCase:	int   				=      int(
		    (2_5_6 / 2_2_4) * config.image_size			)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		__lowerCAmelCase:	str   				=      DeiTImageProcessor(size=lowerCamelCase__ ,			crop_size=config.image_size			)
		__lowerCAmelCase:	List[Any]   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			)
		__lowerCAmelCase:	int   				=      encoding["pixel_values"]
		__lowerCAmelCase:	List[Any]   				=      model(lowerCamelCase__			)

		__lowerCAmelCase:	Optional[Any]   				=      timm_model(lowerCamelCase__			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowerCamelCase__ ,			outputs.logits ,			atol=1E-3			)

		Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
		print(F"Saving model {deit_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__			)


if __name__ == "__main__":
			__A            =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--deit_name",
			    default="vit_deit_base_distilled_patch16_224",
			    type=str,
			    help="Name of the DeiT 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."
			)

			__A            =      parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





"""simple docstring"""


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__A						       =      datasets.logging.get_logger(__name__)


__A						       =      "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

__A						       =      "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

__A						       =      "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

__A						       =      {
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION  )
class    snake_case     (    datasets.Metric  ):

				def 							lowercase_					(     self							:    Optional[int])->       Any:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://github.com/google-research/bleurt"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/google-research/bleurt"]					,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]					,  )

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						if self.config_name == "default":
								logger.warning(
								    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
								    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
								__lowerCAmelCase:	List[str]   				=      "bleurt-base-128"

						if self.config_name.lower() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Optional[int]   				=      self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Tuple   				=      self.config_name.upper()

						else:
								raise KeyError(
								    f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

						# download the model checkpoint specified by self.config_name and set up the scorer
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						__lowerCAmelCase:	Dict   				=      score.BleurtScorer(os.path.join(UpperCamelCase__					,  UpperCamelCase__))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.scorer.score(references=UpperCamelCase__					,  candidates=UpperCamelCase__)
						return {"scores": scores}


