

"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# vision encoder
		if "img_encoder.pos_embed" in name:
				__lowerCAmelCase:	List[str]   				=      name.replace("img_encoder.pos_embed" ,			"vision_model.embeddings.position_embeddings"			)
		if "img_encoder.patch_embed.proj" in name:
				__lowerCAmelCase:	List[Any]   				=      name.replace("img_encoder.patch_embed.proj" ,			"vision_model.embeddings.patch_embeddings.projection"			)
		if "img_encoder.patch_embed.norm" in name:
				__lowerCAmelCase:	List[str]   				=      name.replace("img_encoder.patch_embed.norm" ,			"vision_model.embeddings.layernorm"			)
		if "img_encoder.layers" in name:
				__lowerCAmelCase:	int   				=      name.replace("img_encoder.layers" ,			"vision_model.encoder.stages"			)
		if "blocks" in name and "res" not in name:
				__lowerCAmelCase:	Dict   				=      name.replace("blocks" ,			"layers"			)
		if "attn" in name and "pre_assign" not in name:
				__lowerCAmelCase:	Union[str, Any]   				=      name.replace("attn" ,			"self_attn"			)
		if "proj" in name and "self_attn" in name and "text" not in name:
				__lowerCAmelCase:	Dict   				=      name.replace("proj" ,			"out_proj"			)
		if "pre_assign_attn.attn.proj" in name:
				__lowerCAmelCase:	Optional[Any]   				=      name.replace("pre_assign_attn.attn.proj" ,			"pre_assign_attn.attn.out_proj"			)
		if "norm1" in name:
				__lowerCAmelCase:	Dict   				=      name.replace("norm1" ,			"layer_norm1"			)
		if "norm2" in name and "pre_assign" not in name:
				__lowerCAmelCase:	Any   				=      name.replace("norm2" ,			"layer_norm2"			)
		if "img_encoder.norm" in name:
				__lowerCAmelCase:	Union[str, Any]   				=      name.replace("img_encoder.norm" ,			"vision_model.layernorm"			)
		# text encoder
		if "text_encoder.token_embedding" in name:
				__lowerCAmelCase:	Optional[int]   				=      name.replace("text_encoder.token_embedding" ,			"text_model.embeddings.token_embedding"			)
		if "text_encoder.positional_embedding" in name:
				__lowerCAmelCase:	List[Any]   				=      name.replace("text_encoder.positional_embedding" ,			"text_model.embeddings.position_embedding.weight"			)
		if "text_encoder.transformer.resblocks." in name:
				__lowerCAmelCase:	str   				=      name.replace("text_encoder.transformer.resblocks." ,			"text_model.encoder.layers."			)
		if "ln_1" in name:
				__lowerCAmelCase:	Tuple   				=      name.replace("ln_1" ,			"layer_norm1"			)
		if "ln_2" in name:
				__lowerCAmelCase:	List[Any]   				=      name.replace("ln_2" ,			"layer_norm2"			)
		if "c_fc" in name:
				__lowerCAmelCase:	Union[str, Any]   				=      name.replace("c_fc" ,			"fc1"			)
		if "c_proj" in name:
				__lowerCAmelCase:	Optional[int]   				=      name.replace("c_proj" ,			"fc2"			)
		if "text_encoder" in name:
				__lowerCAmelCase:	Union[str, Any]   				=      name.replace("text_encoder" ,			"text_model"			)
		if "ln_final" in name:
				__lowerCAmelCase:	Union[str, Any]   				=      name.replace("ln_final" ,			"final_layer_norm"			)
		# projection layers
		if "img_projector.linear_hidden." in name:
				__lowerCAmelCase:	List[str]   				=      name.replace("img_projector.linear_hidden." ,			"visual_projection."			)
		if "img_projector.linear_out." in name:
				__lowerCAmelCase:	Any   				=      name.replace("img_projector.linear_out." ,			"visual_projection.3."			)
		if "text_projector.linear_hidden" in name:
				__lowerCAmelCase:	Any   				=      name.replace("text_projector.linear_hidden" ,			"text_projection"			)
		if "text_projector.linear_out" in name:
				__lowerCAmelCase:	Optional[int]   				=      name.replace("text_projector.linear_out" ,			"text_projection.3"			)

		return name






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		for key in orig_state_dict.copy().keys():
				__lowerCAmelCase:	Tuple   				=      orig_state_dict.pop(__SCREAMING_SNAKE_CASE			)

				if "qkv" in key:
						# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						__lowerCAmelCase:	List[Any]   				=      key.split("."			)
						__lowerCAmelCase:	Union[str, Any]   				=      int(key_split[2]			), int(key_split[4]			)
						__lowerCAmelCase:	Dict   				=      config.vision_config.hidden_size
						if "weight" in key:
								__lowerCAmelCase:	Union[str, Any]   				=      val[:dim, :]
								__lowerCAmelCase:	Tuple   				=      val[dim : dim * 2, :]
								__lowerCAmelCase:	Union[str, Any]   				=      val[-dim:, :]
						else:
								__lowerCAmelCase:	Union[str, Any]   				=      val[:dim]
								__lowerCAmelCase:	str   				=      val[dim : dim * 2]
								__lowerCAmelCase:	Optional[int]   				=      val[-dim:]
				elif "in_proj" in key:
						# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						__lowerCAmelCase:	str   				=      key.split("."			)
						__lowerCAmelCase:	Optional[Any]   				=      int(key_split[3]			)
						__lowerCAmelCase:	str   				=      config.text_config.hidden_size
						if "weight" in key:
								__lowerCAmelCase:	Optional[Any]   				=      val[:dim, :]
								__lowerCAmelCase:	Dict   				=      val[
								    dim : dim * 2, :
								]
								__lowerCAmelCase:	Optional[int]   				=      val[-dim:, :]
						else:
								__lowerCAmelCase:	Optional[int]   				=      val[:dim]
								__lowerCAmelCase:	Optional[Any]   				=      val[dim : dim * 2]
								__lowerCAmelCase:	Tuple   				=      val[-dim:]
				else:
						__lowerCAmelCase:	List[Any]   				=      rename_key(__SCREAMING_SNAKE_CASE			)
						# squeeze if necessary
						if (
						    "text_projection.0" in new_name
						    or "text_projection.3" in new_name
						    or "visual_projection.0" in new_name
						    or "visual_projection.3" in new_name
						):
								__lowerCAmelCase:	int   				=      val.squeeze_()
						else:
								__lowerCAmelCase:	List[Any]   				=      val

		return orig_state_dict






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






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc" ,			__SCREAMING_SNAKE_CASE=False			)     ->     List[str]:
		__lowerCAmelCase:	Optional[Any]   				=      GroupViTConfig()
		__lowerCAmelCase:	List[str]   				=      GroupViTModel(__SCREAMING_SNAKE_CASE			).eval()

		__lowerCAmelCase:	int   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["model"]
		__lowerCAmelCase:	Optional[int]   				=      convert_state_dict(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      model.load_state_dict(__SCREAMING_SNAKE_CASE ,			strict=__SCREAMING_SNAKE_CASE			)
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__SCREAMING_SNAKE_CASE			) == 0)

		# verify result
		__lowerCAmelCase:	List[Any]   				=      CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"			)
		__lowerCAmelCase:	Union[str, Any]   				=      prepare_img()
		__lowerCAmelCase:	Dict   				=      processor(text=["a photo of a cat", "a photo of a dog"] ,			images=__SCREAMING_SNAKE_CASE ,			padding=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			)

		with torch.no_grad():
				__lowerCAmelCase:	Dict   				=      model(**__SCREAMING_SNAKE_CASE			)

		if model_name == "groupvit-gcc-yfcc":
				__lowerCAmelCase:	Any   				=      torch.tensor([[13.3523, 6.3629]]			)
		elif model_name == "groupvit-gcc-redcaps":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([[16.1873, 8.6230]]			)
		else:
				raise ValueError(F"Model name {model_name} not supported."			)
		assert torch.allclose(outputs.logits_per_image ,			__SCREAMING_SNAKE_CASE ,			atol=1E-3			)

		processor.save_pretrained(__SCREAMING_SNAKE_CASE			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)
		print("Successfully saved processor and model to" ,			__SCREAMING_SNAKE_CASE			)

		if push_to_hub:
				print("Pushing to the hub..."			)
				processor.push_to_hub(__SCREAMING_SNAKE_CASE ,			organization="nielsr"			)
				model.push_to_hub(__SCREAMING_SNAKE_CASE ,			organization="nielsr"			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
			)
			parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
			parser.add_argument(
			    "--model_name",
			    default="groupvit-gccy-fcc",
			    type=str,
			    help="Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'",
			)
			parser.add_argument(
			    "--push_to_hub",
			    action="store_true",
			    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
			)
			__A						       =      parser.parse_args()

			convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""


import comet  # From: unbabel-comet
import torch

import datasets


__A						       =      datasets.logging.get_logger(__name__)

__A						       =      "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

__A						       =      "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

__A						       =      "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





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

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://unbabel.github.io/COMET/html/index.html"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "sources": datasets.Value("string"					,  id="sequence"),
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/Unbabel/COMET"]					,  reference_urls=[
						        "https://github.com/Unbabel/COMET",
						        "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
						        "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
						    ]					,  )

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Any)->       Dict:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase:	Union[str, Any]   				=      comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"))
						else:
								__lowerCAmelCase:	Tuple   				=      comet.load_from_checkpoint(comet.download_model(self.config_name))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[int]=None					,  UpperCamelCase__							:    List[str]=False)->       str:



						'''simple docstring'''


						if gpus is None:
								__lowerCAmelCase:	Union[str, Any]   				=      1 if torch.cuda.is_available() else 0
						__lowerCAmelCase:	Dict   				=      {"src": sources, "mt": predictions, "ref": references}
						__lowerCAmelCase:	Union[str, Any]   				=      [dict(zip(UpperCamelCase__					,  UpperCamelCase__)) for t in zip(*data.values())]
						__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      self.scorer.predict(UpperCamelCase__					,  gpus=UpperCamelCase__					,  progress_bar=UpperCamelCase__)
						return {"mean_score": mean_score, "scores": scores}


