

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=()		,					UpperCAmelCase__=None		,					UpperCAmelCase__="no"		,					UpperCAmelCase__="29500"      ):

					"""simple docstring"""






					__lowerCAmelCase					=							False
					__lowerCAmelCase					=							False
					if any(key.startswith('KAGGLE'      ) for key in os.environ.keys()      ):
										__lowerCAmelCase					=							True
					elif "IPython" in sys.modules:
										__lowerCAmelCase					=							'google.colab' in str(sys.modules['IPython'].get_ipython()      )

					try:
										__lowerCAmelCase					=							PrecisionType(mixed_precision.lower()      )
					except ValueError:
										raise ValueError(
										    F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."""      )

					if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'		,					UpperCAmelCase__      ) is not None):
										# TPU launch
										import torch_xla.distributed.xla_multiprocessing as xmp

										if len(AcceleratorState._shared_state      ) > 0:
															raise ValueError(
															    'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
															    'your training function. Restart your notebook and make sure no cells initializes an '
															    '`Accelerator`.'      )
										if num_processes is None:
															__lowerCAmelCase					=							8

										__lowerCAmelCase					=							PrepareForLaunch(UpperCAmelCase__		,					distributed_type='TPU'      )
										print(F"""Launching a training on {num_processes} TPU cores."""      )
										xmp.spawn(UpperCAmelCase__		,					args=UpperCAmelCase__		,					nprocs=UpperCAmelCase__		,					start_method='fork'      )
					elif in_colab:
										# No need for a distributed launch otherwise as it's either CPU or one GPU.
										if torch.cuda.is_available():
															print('Launching training on one GPU.'      )
										else:
															print('Launching training on one CPU.'      )
										function(*UpperCAmelCase__      )
					else:
										if num_processes is None:
															raise ValueError(
															    'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'      )

										if num_processes > 1:
															# Multi-GPU launch
															from torch.multiprocessing import start_processes
															from torch.multiprocessing.spawn import ProcessRaisedException

															if len(AcceleratorState._shared_state      ) > 0:
																				raise ValueError(
																				    'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
																				    'inside your training function. Restart your notebook and make sure no cells initializes an '
																				    '`Accelerator`.'      )

															if torch.cuda.is_initialized():
																				raise ValueError(
																				    'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
																				    'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
																				    'function.'      )

															# torch.distributed will expect a few environment variable to be here. We set the ones common to each
															# process here (the other ones will be set be the launcher).
															with patch_environment(
															    world_size=UpperCAmelCase__		,					master_addr='127.0.01'		,					master_port=UpperCAmelCase__		,					mixed_precision=UpperCAmelCase__      ):
																				__lowerCAmelCase					=							PrepareForLaunch(UpperCAmelCase__		,					distributed_type='MULTI_GPU'      )
																				print(F"""Launching training on {num_processes} GPUs."""      )
																				try:
																									start_processes(UpperCAmelCase__		,					args=UpperCAmelCase__		,					nprocs=UpperCAmelCase__		,					start_method='fork'      )
																				except ProcessRaisedException as e:
																									if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																														raise RuntimeError(
																														    'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
																														    'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
																														    'Please review your imports and test them when running the `notebook_launcher()` to identify '
																														    'which one is problematic.'      ) from e

										else:
															# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
															if is_mps_available():
																				__lowerCAmelCase					=							'1'
																				print('Launching training on MPS.'      )
															elif torch.cuda.is_available():
																				print('Launching training on one GPU.'      )
															else:
																				print('Launching training on CPU.'      )
															function(*UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=()		,					UpperCAmelCase__=2      ):

					"""simple docstring"""






					from torch.multiprocessing import start_processes

					with tempfile.NamedTemporaryFile() as tmp_file:
										# torch.distributed will expect a few environment variable to be here. We set the ones common to each
										# process here (the other ones will be set be the launcher).
										with patch_environment(
										    world_size=UpperCAmelCase__		,					master_addr='127.0.01'		,					master_port='29500'		,					accelerate_mixed_precision='no'		,					accelerate_debug_rdv_file=tmp_file.name		,					accelerate_use_cpu='yes'		,					):
															__lowerCAmelCase					=							PrepareForLaunch(UpperCAmelCase__		,					debug=UpperCAmelCase__      )
															start_processes(UpperCAmelCase__		,					args=UpperCAmelCase__		,					nprocs=UpperCAmelCase__		,					start_method='fork'      )









import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

					return name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase					=							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
															__lowerCAmelCase					=							key.split('.'      )
															__lowerCAmelCase,	__lowerCAmelCase					=							int(key_split[2]      ), int(key_split[4]      )
															__lowerCAmelCase					=							config.vision_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[dim : dim * 2, :]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							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					=							key.split('.'      )
															__lowerCAmelCase					=							int(key_split[3]      )
															__lowerCAmelCase					=							config.text_config.hidden_size
															if "weight" in key:
																				__lowerCAmelCase					=							val[:dim, :]
																				__lowerCAmelCase					=							val[
																				    dim : dim * 2, :
																				]
																				__lowerCAmelCase					=							val[-dim:, :]
															else:
																				__lowerCAmelCase					=							val[:dim]
																				__lowerCAmelCase					=							val[dim : dim * 2]
																				__lowerCAmelCase					=							val[-dim:]
										else:
															__lowerCAmelCase					=							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
															):
																				__lowerCAmelCase					=							val.squeeze_()
															else:
																				__lowerCAmelCase					=							val

					return orig_state_dict


def        __lowercase    (  ):

					"""simple docstring"""






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


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__="groupvit-gcc-yfcc"		,					UpperCAmelCase__=False      ):

					"""simple docstring"""






					__lowerCAmelCase					=							GroupViTConfig()
					__lowerCAmelCase					=							GroupViTModel(UpperCAmelCase__      ).eval()

					__lowerCAmelCase					=							torch.load(UpperCAmelCase__		,					map_location='cpu'      )['model']
					__lowerCAmelCase					=							convert_state_dict(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase,	__lowerCAmelCase					=							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
					__lowerCAmelCase					=							CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
					__lowerCAmelCase					=							prepare_img()
					__lowerCAmelCase					=							processor(text=['a photo of a cat', 'a photo of a dog']		,					images=UpperCAmelCase__		,					padding=UpperCAmelCase__		,					return_tensors='pt'      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(**UpperCAmelCase__      )

					if model_name == "groupvit-gcc-yfcc":
										__lowerCAmelCase					=							torch.tensor([[13.3523, 6.3629]]      )
					elif model_name == "groupvit-gcc-redcaps":
										__lowerCAmelCase					=							torch.tensor([[16.1873, 8.6230]]      )
					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__":
		lowerCamelCase      				=     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`.''',
		)
		lowerCamelCase      				=     parser.parse_args()

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






