




'''simple docstring'''

import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					if openai_config_file == "":
										snake_case_		     =							OpenAIGPTConfig()
					else:
										snake_case_		     =							OpenAIGPTConfig.from_json_file(__UpperCAmelCase      )
					snake_case_		     =							OpenAIGPTModel(__UpperCAmelCase      )

					# Load weights from numpy
					load_tf_weights_in_openai_gpt(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )

					# Save pytorch-model
					snake_case_		     =							pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					snake_case_		     =							pytorch_dump_folder_path + '''/''' + CONFIG_NAME
					print(F"Save PyTorch model to {pytorch_weights_dump_path}"      )
					torch.save(model.state_dict(), __UpperCAmelCase      )
					print(F"Save configuration file to {pytorch_config_dump_path}"      )
					with open(__UpperCAmelCase, '''w''', encoding='''utf-8'''      ) as f:
										f.write(config.to_json_string()      )


if __name__ == "__main__":
							a    :							Tuple										=    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--openai_checkpoint_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the TensorFlow checkpoint path.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--openai_config_file',
							    default='',
							    type=str,
							    help=(
							        'An optional config json file corresponding to the pre-trained OpenAI model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							a    :							Dict										=    parser.parse_args()
							convert_openai_checkpoint_to_pytorch(
							    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
							)







'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					if "img_encoder.pos_embed" in name:
										snake_case_		     =							name.replace('''img_encoder.pos_embed''', '''vision_model.embeddings.position_embeddings'''      )
					if "img_encoder.patch_embed.proj" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.proj''', '''vision_model.embeddings.patch_embeddings.projection'''      )
					if "img_encoder.patch_embed.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.patch_embed.norm''', '''vision_model.embeddings.layernorm'''      )
					if "img_encoder.layers" in name:
										snake_case_		     =							name.replace('''img_encoder.layers''', '''vision_model.encoder.stages'''      )
					if "blocks" in name and "res" not in name:
										snake_case_		     =							name.replace('''blocks''', '''layers'''      )
					if "attn" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''attn''', '''self_attn'''      )
					if "proj" in name and "self_attn" in name and "text" not in name:
										snake_case_		     =							name.replace('''proj''', '''out_proj'''      )
					if "pre_assign_attn.attn.proj" in name:
										snake_case_		     =							name.replace('''pre_assign_attn.attn.proj''', '''pre_assign_attn.attn.out_proj'''      )
					if "norm1" in name:
										snake_case_		     =							name.replace('''norm1''', '''layer_norm1'''      )
					if "norm2" in name and "pre_assign" not in name:
										snake_case_		     =							name.replace('''norm2''', '''layer_norm2'''      )
					if "img_encoder.norm" in name:
										snake_case_		     =							name.replace('''img_encoder.norm''', '''vision_model.layernorm'''      )
					# text encoder
					if "text_encoder.token_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.token_embedding''', '''text_model.embeddings.token_embedding'''      )
					if "text_encoder.positional_embedding" in name:
										snake_case_		     =							name.replace('''text_encoder.positional_embedding''', '''text_model.embeddings.position_embedding.weight'''      )
					if "text_encoder.transformer.resblocks." in name:
										snake_case_		     =							name.replace('''text_encoder.transformer.resblocks.''', '''text_model.encoder.layers.'''      )
					if "ln_1" in name:
										snake_case_		     =							name.replace('''ln_1''', '''layer_norm1'''      )
					if "ln_2" in name:
										snake_case_		     =							name.replace('''ln_2''', '''layer_norm2'''      )
					if "c_fc" in name:
										snake_case_		     =							name.replace('''c_fc''', '''fc1'''      )
					if "c_proj" in name:
										snake_case_		     =							name.replace('''c_proj''', '''fc2'''      )
					if "text_encoder" in name:
										snake_case_		     =							name.replace('''text_encoder''', '''text_model'''      )
					if "ln_final" in name:
										snake_case_		     =							name.replace('''ln_final''', '''final_layer_norm'''      )
					# projection layers
					if "img_projector.linear_hidden." in name:
										snake_case_		     =							name.replace('''img_projector.linear_hidden.''', '''visual_projection.'''      )
					if "img_projector.linear_out." in name:
										snake_case_		     =							name.replace('''img_projector.linear_out.''', '''visual_projection.3.'''      )
					if "text_projector.linear_hidden" in name:
										snake_case_		     =							name.replace('''text_projector.linear_hidden''', '''text_projection'''      )
					if "text_projector.linear_out" in name:
										snake_case_		     =							name.replace('''text_projector.linear_out''', '''text_projection.3'''      )

					return name


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_		     =							orig_state_dict.pop(__UpperCAmelCase      )

										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
															snake_case_		     =							key.split('''.'''      )
															snake_case_     ,snake_case_		     =							int(key_split[2]      ), int(key_split[4]      )
															snake_case_		     =							config.vision_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[dim : dim * 2, :]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							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
															snake_case_		     =							key.split('''.'''      )
															snake_case_		     =							int(key_split[3]      )
															snake_case_		     =							config.text_config.hidden_size
															if "weight" in key:
																				snake_case_		     =							val[:dim, :]
																				snake_case_		     =							val[
																				    dim : dim * 2, :
																				]
																				snake_case_		     =							val[-dim:, :]
															else:
																				snake_case_		     =							val[:dim]
																				snake_case_		     =							val[dim : dim * 2]
																				snake_case_		     =							val[-dim:]
										else:
															snake_case_		     =							rename_key(__UpperCAmelCase      )
															# 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
															):
																				snake_case_		     =							val.squeeze_()
															else:
																				snake_case_		     =							val

					return orig_state_dict


def 						__magic_name__       (      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_		     =							Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase      ).raw      )
					return im


@torch.no_grad()
def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase="groupvit-gcc-yfcc", __UpperCAmelCase=False      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							GroupViTConfig()
					snake_case_		     =							GroupViTModel(__UpperCAmelCase      ).eval()

					snake_case_		     =							torch.load(__UpperCAmelCase, map_location='''cpu'''      )['''model''']
					snake_case_		     =							convert_state_dict(__UpperCAmelCase, __UpperCAmelCase      )
					snake_case_     ,snake_case_		     =							model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase      )
					assert missing_keys == ["text_model.embeddings.position_ids"]
					assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCAmelCase      ) == 0)

					# verify result
					snake_case_		     =							CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''      )
					snake_case_		     =							prepare_img()
					snake_case_		     =							processor(text=['''a photo of a cat''', '''a photo of a dog'''], images=__UpperCAmelCase, padding=__UpperCAmelCase, return_tensors='''pt'''      )

					with torch.no_grad():
										snake_case_		     =							model(**__UpperCAmelCase      )

					if model_name == "groupvit-gcc-yfcc":
										snake_case_		     =							torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]      )
					elif model_name == "groupvit-gcc-redcaps":
										snake_case_		     =							torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]      )
					else:
										raise ValueError(F"Model name {model_name} not supported."      )
					assert torch.allclose(outputs.logits_per_image, __UpperCAmelCase, atol=1e-3      )

					processor.save_pretrained(__UpperCAmelCase      )
					model.save_pretrained(__UpperCAmelCase      )
					print('''Successfully saved processor and model to''', __UpperCAmelCase      )

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


if __name__ == "__main__":
							a    :							Optional[int]										=    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    :							str										=    parser.parse_args()

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

