






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

		return name

def      lowerCAmelCase__	(    _a       :			Optional[Any]  ,      _a       :			Optional[Any]    ):
		for key in orig_state_dict.copy().keys():
				snake_case_				:							Dict						 =       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_				:							int						 =       key.split("."    )
						snake_case_ ,       snake_case_				:							Dict						 =       int(key_split[2]    ), int(key_split[4]    )
						snake_case_				:							Union[str, Any]						 =       config.vision_config.hidden_size
						if "weight" in key:
								snake_case_				:							Optional[Any]						 =       val[:dim, :]
								snake_case_				:							Optional[Any]						 =       val[dim : dim * 2, :]
								snake_case_				:							Optional[int]						 =       val[-dim:, :]
						else:
								snake_case_				:							int						 =       val[:dim]
								snake_case_				:							str						 =       val[dim : dim * 2]
								snake_case_				:							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
						snake_case_				:							Union[str, Any]						 =       key.split("."    )
						snake_case_				:							Optional[int]						 =       int(key_split[3]    )
						snake_case_				:							List[str]						 =       config.text_config.hidden_size
						if "weight" in key:
								snake_case_				:							int						 =       val[:dim, :]
								snake_case_				:							List[str]						 =       val[
								    dim : dim * 2, :
								]
								snake_case_				:							List[str]						 =       val[-dim:, :]
						else:
								snake_case_				:							Tuple						 =       val[:dim]
								snake_case_				:							Union[str, Any]						 =       val[dim : dim * 2]
								snake_case_				:							str						 =       val[-dim:]
				else:
						snake_case_				:							Any						 =       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_				:							List[str]						 =       val.squeeze_()
						else:
								snake_case_				:							Dict						 =       val

		return orig_state_dict

def      lowerCAmelCase__	(    ):
		snake_case_				:							Optional[Any]						 =       "http://images.cocodataset.org/val2017/000000039769.jpg"
		snake_case_				:							int						 =       Image.open(requests.get(_a  ,      stream=_a    ).raw    )
		return im

@torch.no_grad()
def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Dict  ,      _a       :			Tuple="groupvit-gcc-yfcc"  ,      _a       :			Any=False    ):
		snake_case_				:							int						 =       GroupViTConfig()
		snake_case_				:							Union[str, Any]						 =       GroupViTModel(_a    ).eval()

		snake_case_				:							str						 =       torch.load(_a  ,      map_location="cpu"    )["model"]
		snake_case_				:							List[Any]						 =       convert_state_dict(_a  ,      _a    )
		snake_case_ ,       snake_case_				:							Tuple						 =       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_				:							Dict						 =       CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"    )
		snake_case_				:							Any						 =       prepare_img()
		snake_case_				:							Optional[int]						 =       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_				:							Optional[int]						 =       model(**_a    )

		if model_name == "groupvit-gcc-yfcc":
				snake_case_				:							str						 =       torch.tensor([[13.3_523, 6.3_629]]    )
		elif model_name == "groupvit-gcc-redcaps":
				snake_case_				:							Optional[Any]						 =       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  :							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`.''',
							)
							lowercase  :							Tuple			  =    parser.parse_args()

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


lowercase  :							int			  =    {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

							lowercase  :							Optional[int]			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
