





"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__UpperCamelCase     :						Union[str, Any]						       =  '''\
    Text data.
    Second line of data.'''

__UpperCamelCase     :						Tuple						       =  '''file'''

@pytest.fixture(scope='session'			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 lowerCAmelCase    				= tmp_path_factory.mktemp('data'			) / (FILE_PATH + '.zstd')
 lowerCAmelCase    				= bytes(_UpperCAmelCase  ,     'utf-8'			)
 with zstd.open(_UpperCAmelCase  ,     'wb'			) as f:
  f.write(_UpperCAmelCase			)
 return path

@pytest.fixture
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 with open(os.path.join(tmpfs.local_root_dir  ,     _UpperCAmelCase			)  ,     'w'			) as f:
  f.write(_UpperCAmelCase			)
 return FILE_PATH

@pytest.mark.parametrize('compression_format'  ,     ['gzip', 'xz', 'zstd']			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    int  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
 lowerCAmelCase    				= input_paths[compression_format]
 lowerCAmelCase    				= tmp_path / 'cache'
 lowerCAmelCase    				= DownloadConfig(cache_dir=_UpperCAmelCase  ,     extract_compressed_file=_UpperCAmelCase			)
 lowerCAmelCase    				= cached_path(_UpperCAmelCase  ,     download_config=_UpperCAmelCase			)
 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read()
 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read()
 assert extracted_file_content == expected_file_content

@pytest.mark.parametrize('default_extracted'  ,     [True, False]			)
@pytest.mark.parametrize('default_cache_dir'  ,     [True, False]			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    str			):
 lowerCAmelCase    				= 'custom_cache'
 lowerCAmelCase    				= 'custom_extracted_dir'
 lowerCAmelCase    				= tmp_path / 'custom_extracted_path'
 if default_extracted:
  lowerCAmelCase    				= ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
 else:
  monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'  ,     _UpperCAmelCase			)
  monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'  ,     str(_UpperCAmelCase			)			)
  lowerCAmelCase    				= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 lowerCAmelCase    				= xz_file
 lowerCAmelCase    				= (
     DownloadConfig(extract_compressed_file=_UpperCAmelCase			)
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir  ,     extract_compressed_file=_UpperCAmelCase			)
 )
 lowerCAmelCase    				= cached_path(_UpperCAmelCase  ,     download_config=_UpperCAmelCase			)
 assert Path(_UpperCAmelCase			).parent.parts[-2:] == expected

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 # absolute path
 lowerCAmelCase    				= str(Path(_UpperCAmelCase			).resolve()			)
 assert cached_path(_UpperCAmelCase			) == text_file
 # relative path
 lowerCAmelCase    				= str(Path(_UpperCAmelCase			).resolve().relative_to(Path(os.getcwd()			)			)			)
 assert cached_path(_UpperCAmelCase			) == text_file

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]			):
 # absolute path
 lowerCAmelCase    				= str(tmp_path.resolve() / '__missing_file__.txt'			)
 with pytest.raises(_UpperCAmelCase			):
  cached_path(_UpperCAmelCase			)
 # relative path
 lowerCAmelCase    				= './__missing_file__.txt'
 with pytest.raises(_UpperCAmelCase			):
  cached_path(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    int			):
 lowerCAmelCase    				= get_from_cache(F'tmp://{tmpfs_file}'			)
 with open(_UpperCAmelCase			) as f:
  lowerCAmelCase    				= f.read()
 assert output_file_content == FILE_CONTENT

@patch('datasets.config.HF_DATASETS_OFFLINE'  ,     _UpperCAmelCase			)
def 		_SCREAMING_SNAKE_CASE			():
 with pytest.raises(_UpperCAmelCase			):
  cached_path('https://huggingface.co'			)

@patch('datasets.config.HF_DATASETS_OFFLINE'  ,     _UpperCAmelCase			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]			):
 lowerCAmelCase    				= tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(_UpperCAmelCase			):
  http_get('https://huggingface.co'  ,     temp_file=_UpperCAmelCase			)
 with pytest.raises(_UpperCAmelCase			):
  http_head('https://huggingface.co'			)

@patch('datasets.config.HF_DATASETS_OFFLINE'  ,     _UpperCAmelCase			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Dict			):
 lowerCAmelCase    				= tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(_UpperCAmelCase			):
  ftp_get('ftp://huggingface.co'  ,     temp_file=_UpperCAmelCase			)
 with pytest.raises(_UpperCAmelCase			):
  ftp_head('ftp://huggingface.co'			)




@patch('datasets.config.HF_DATASETS_OFFLINE'  ,     _UpperCAmelCase			)
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= tmp_path_factory.mktemp('data'			) / 'file.html'
 with pytest.raises(_UpperCAmelCase			):
  fsspec_get('s3://huggingface.co'  ,     temp_file=_UpperCAmelCase			)
 with pytest.raises(_UpperCAmelCase			):
  fsspec_head('s3://huggingface.co'			)











'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						lowerCamelCase__   (				a							):
						# vision encoder
						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 						lowerCamelCase__   (				a    ,	a							):
						for key in orig_state_dict.copy().keys():
												__snake_case						 =   orig_state_dict.pop(a							)

												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(a							)
																		# 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 						lowerCamelCase__   (				):
						__snake_case						 =   'http://images.cocodataset.org/val2017/000000039769.jpg'
						__snake_case						 =   Image.open(requests.get(a    ,	stream=a							).raw							)
						return im
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a="groupvit-gcc-yfcc"    ,	a=False							):
						__snake_case						 =   GroupViTConfig()
						__snake_case						 =   GroupViTModel(a							).eval()

						__snake_case						 =   torch.load(a    ,	map_location='cpu'							)['model']
						__snake_case						 =   convert_state_dict(a    ,	a							)
						__snake_case ,       __snake_case						 =   model.load_state_dict(a    ,	strict=a							)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a							) == 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=a    ,	padding=a    ,	return_tensors='pt'							)

						with torch.no_grad():
												__snake_case						 =   model(**a							)

						if model_name == "groupvit-gcc-yfcc":
												__snake_case						 =   torch.tensor([[13.3_523, 6.3_629]]							)
						elif model_name == "groupvit-gcc-redcaps":
												__snake_case						 =   torch.tensor([[16.1_873, 8.6_230]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported.'							)
						assert torch.allclose(outputs.logits_per_image    ,	a    ,	atol=1E-3							)

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

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


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

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

