"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

							return name

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							for key in orig_state_dict.copy().keys():
														A__       :     Dict								=			orig_state_dict.pop(lowerCAmelCase							)

														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
																					A__       :     List[Any]								=			key.split("""."""							)
																					A__				,							A__       :     Tuple								=			int(key_split[2]							), int(key_split[4]							)
																					A__       :     Union[str, Any]								=			config.vision_config.hidden_size
																					if "weight" in key:
																												A__       :     int								=			val[:dim, :]
																												A__       :     str								=			val[dim : dim * 2, :]
																												A__       :     Optional[int]								=			val[-dim:, :]
																					else:
																												A__       :     Optional[Any]								=			val[:dim]
																												A__       :     Union[str, Any]								=			val[dim : dim * 2]
																												A__       :     Dict								=			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
																					A__       :     Tuple								=			key.split("""."""							)
																					A__       :     List[str]								=			int(key_split[3]							)
																					A__       :     int								=			config.text_config.hidden_size
																					if "weight" in key:
																												A__       :     str								=			val[:dim, :]
																												A__       :     int								=			val[
																												    dim : dim * 2, :
																												]
																												A__       :     int								=			val[-dim:, :]
																					else:
																												A__       :     Any								=			val[:dim]
																												A__       :     Union[str, Any]								=			val[dim : dim * 2]
																												A__       :     Tuple								=			val[-dim:]
														else:
																					A__       :     Union[str, Any]								=			rename_key(lowerCAmelCase							)
																					# 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
																					):
																												A__       :     Dict								=			val.squeeze_()
																					else:
																												A__       :     Dict								=			val

							return orig_state_dict

def  _A(		):
							A__       :     Dict								=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							A__       :     Optional[Any]								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							)
							return im

@torch.no_grad()
def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase="groupvit-gcc-yfcc"			,  lowerCAmelCase=False							):
							A__       :     Union[str, Any]								=			GroupViTConfig()
							A__       :     Any								=			GroupViTModel(lowerCAmelCase							).eval()

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

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

							with torch.no_grad():
														A__       :     Optional[int]								=			model(**lowerCAmelCase							)

							if model_name == "groupvit-gcc-yfcc":
														A__       :     Union[str, Any]								=			torch.tensor([[13.3523, 6.3629]]							)
							elif model_name == "groupvit-gcc-redcaps":
														A__       :     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			,  lowerCAmelCase			,  atol=1E-3							)

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

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


if __name__ == "__main__":
				_UpperCamelCase								=		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`.",
				)
				_UpperCamelCase								=		parser.parse_args()

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






"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





