'''simple docstring'''




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   Optional[int]:
							lowercase_      :    Optional[Any]       		= XCLIPTextConfig()

							# derive patch size from model name
							lowercase_      :    Dict       		= model_name.find("""patch"""				)
							lowercase_      :    Dict       		= int(model_name[start_idx + len("""patch"""				) : start_idx + len("""patch"""				) + 2]				)
							lowercase_      :    Optional[int]       		= XCLIPVisionConfig(patch_size=UpperCAmelCase__   ,   num_frames=UpperCAmelCase__				)

							if "large" in model_name:
														lowercase_      :    List[str]       		= 768
														lowercase_      :    Optional[int]       		= 3072
														lowercase_      :    Dict       		= 12

														lowercase_      :    Dict       		= 1024
														lowercase_      :    Any       		= 4096
														lowercase_      :    List[str]       		= 16
														lowercase_      :    str       		= 24
														lowercase_      :    int       		= 768
														lowercase_      :    Tuple       		= 3072

							if model_name == "xclip-large-patch14-16-frames":
														lowercase_      :    Any       		= 336

							lowercase_      :    List[str]       		= XCLIPConfig.from_text_vision_configs(UpperCAmelCase__   ,   UpperCAmelCase__				)

							if "large" in model_name:
														lowercase_      :    List[Any]       		= 768

							return config




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]				)    ->   Optional[int]:
							# text encoder
							if name == "token_embedding.weight":
														lowercase_      :    List[str]       		= name.replace("""token_embedding.weight"""   ,   """text_model.embeddings.token_embedding.weight"""				)
							if name == "positional_embedding":
														lowercase_      :    List[str]       		= name.replace("""positional_embedding"""   ,   """text_model.embeddings.position_embedding.weight"""				)
							if "ln_1" in name:
														lowercase_      :    str       		= name.replace("""ln_1"""   ,   """layer_norm1"""				)
							if "ln_2" in name:
														lowercase_      :    List[str]       		= name.replace("""ln_2"""   ,   """layer_norm2"""				)
							if "c_fc" in name:
														lowercase_      :    int       		= name.replace("""c_fc"""   ,   """fc1"""				)
							if "c_proj" in name:
														lowercase_      :    Dict       		= name.replace("""c_proj"""   ,   """fc2"""				)
							if name.startswith("""transformer.resblocks"""				):
														lowercase_      :    Optional[Any]       		= name.replace("""transformer.resblocks"""   ,   """text_model.encoder.layers"""				)
							if "attn.out_proj" in name and "message" not in name:
														lowercase_      :    Optional[int]       		= name.replace("""attn.out_proj"""   ,   """self_attn.out_proj"""				)
							if "ln_final" in name:
														lowercase_      :    Optional[int]       		= name.replace("""ln_final"""   ,   """text_model.final_layer_norm"""				)
							# visual encoder
							if name == "visual.class_embedding":
														lowercase_      :    Any       		= name.replace("""visual.class_embedding"""   ,   """vision_model.embeddings.class_embedding"""				)
							if name == "visual.positional_embedding":
														lowercase_      :    str       		= name.replace("""visual.positional_embedding"""   ,   """vision_model.embeddings.position_embedding.weight"""				)
							if name.startswith("""visual.transformer.resblocks"""				):
														lowercase_      :    List[Any]       		= name.replace("""visual.transformer.resblocks"""   ,   """vision_model.encoder.layers"""				)
							if "visual.conv1" in name:
														lowercase_      :    Dict       		= name.replace("""visual.conv1"""   ,   """vision_model.embeddings.patch_embedding"""				)
							if "visual.ln_pre" in name:
														lowercase_      :    List[Any]       		= name.replace("""visual.ln_pre"""   ,   """vision_model.pre_layernorm"""				)
							if "visual.ln_post" in name:
														lowercase_      :    List[str]       		= name.replace("""visual.ln_post"""   ,   """vision_model.post_layernorm"""				)
							if "visual.proj" in name:
														lowercase_      :    Any       		= name.replace("""visual.proj"""   ,   """visual_projection.weight"""				)
							if "text_projection" in name:
														lowercase_      :    int       		= name.replace("""text_projection"""   ,   """text_projection.weight"""				)
							# things on top
							if "prompts_visual_proj" in name:
														lowercase_      :    Optional[Any]       		= name.replace("""prompts_visual_proj"""   ,   """prompts_visual_projection"""				)
							if "prompts_visual_ln" in name:
														lowercase_      :    Union[str, Any]       		= name.replace("""prompts_visual_ln"""   ,   """prompts_visual_layernorm"""				)
							# mit
							if name == "mit.positional_embedding":
														lowercase_      :    List[Any]       		= name.replace("""positional"""   ,   """position"""				)
							if name.startswith("""mit.resblocks"""				):
														lowercase_      :    List[Any]       		= name.replace("""mit.resblocks"""   ,   """mit.encoder.layers"""				)
							# prompts generator
							if name.startswith("""prompts_generator.norm"""				):
														lowercase_      :    Any       		= name.replace("""prompts_generator.norm"""   ,   """prompts_generator.layernorm"""				)

							return name




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      Dict				)    ->   List[Any]:
							for key in orig_state_dict.copy().keys():
														lowercase_      :    List[str]       		= orig_state_dict.pop(UpperCAmelCase__				)

														if "attn.in_proj" in key:
																					lowercase_      :    Union[str, Any]       		= key.split("""."""				)
																					if key.startswith("""visual"""				):
																												lowercase_      :    Union[str, Any]       		= key_split[3]
																												lowercase_      :    Optional[Any]       		= config.vision_config.hidden_size
																												if "message_attn" in key:
																																			if "weight" in key:
																																										lowercase_      :    Union[str, Any]       		= val[
																																										    :dim, :
																																										]
																																										lowercase_      :    List[str]       		= val[
																																										    dim : dim * 2, :
																																										]
																																										lowercase_      :    Dict       		= val[
																																										    -dim:, :
																																										]
																																			else:
																																										lowercase_      :    Union[str, Any]       		= val[
																																										    :dim
																																										]
																																										lowercase_      :    Optional[int]       		= val[
																																										    dim : dim * 2
																																										]
																																										lowercase_      :    Tuple       		= val[
																																										    -dim:
																																										]
																												else:
																																			if "weight" in key:
																																										lowercase_      :    int       		= val[
																																										    :dim, :
																																										]
																																										lowercase_      :    Any       		= val[
																																										    dim : dim * 2, :
																																										]
																																										lowercase_      :    Optional[int]       		= val[
																																										    -dim:, :
																																										]
																																			else:
																																										lowercase_      :    str       		= val[:dim]
																																										lowercase_      :    Tuple       		= val[
																																										    dim : dim * 2
																																										]
																																										lowercase_      :    List[Any]       		= val[-dim:]
																					elif key.startswith("""mit"""				):
																												lowercase_      :    Tuple       		= key_split[2]
																												lowercase_      :    Optional[Any]       		= config.vision_config.mit_hidden_size
																												if "weight" in key:
																																			lowercase_      :    Any       		= val[:dim, :]
																																			lowercase_      :    Optional[int]       		= val[dim : dim * 2, :]
																																			lowercase_      :    int       		= val[-dim:, :]
																												else:
																																			lowercase_      :    Dict       		= val[:dim]
																																			lowercase_      :    int       		= val[dim : dim * 2]
																																			lowercase_      :    List[Any]       		= val[-dim:]
																					else:
																												lowercase_      :    List[Any]       		= key_split[2]
																												lowercase_      :    Optional[Any]       		= config.text_config.hidden_size
																												if "weight" in key:
																																			lowercase_      :    List[Any]       		= val[:dim, :]
																																			lowercase_      :    Any       		= val[
																																			    dim : dim * 2, :
																																			]
																																			lowercase_      :    Dict       		= val[-dim:, :]
																												else:
																																			lowercase_      :    List[str]       		= val[:dim]
																																			lowercase_      :    Union[str, Any]       		= val[
																																			    dim : dim * 2
																																			]
																																			lowercase_      :    List[str]       		= val[-dim:]
														else:
																					lowercase_      :    str       		= rename_key(UpperCAmelCase__				)
																					if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																												lowercase_      :    int       		= val.T
																					lowercase_      :    List[str]       		= val

							return orig_state_dict




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[int]				)    ->   List[Any]:
							if num_frames == 8:
														lowercase_      :    Tuple       		= """eating_spaghetti_8_frames.npy"""
							elif num_frames == 16:
														lowercase_      :    Union[str, Any]       		= """eating_spaghetti.npy"""
							elif num_frames == 32:
														lowercase_      :    List[Any]       		= """eating_spaghetti_32_frames.npy"""
							lowercase_      :    List[Any]       		= hf_hub_download(
							    repo_id="""hf-internal-testing/spaghetti-video"""   ,   filename=UpperCAmelCase__   ,   repo_type="""dataset"""   ,   )
							lowercase_      :    str       		= np.load(UpperCAmelCase__				)
							return list(UpperCAmelCase__				)




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      Optional[Any]=None   ,   UpperCAmelCase__ :      Any=False				)    ->   Union[str, Any]:
							lowercase_      :    Dict       		= {
							    # fully supervised kinetics-400 checkpoints
							    """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
							    """xclip-base-patch32-16-frames""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
							    ),
							    """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
							    """xclip-base-patch16-16-frames""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
							    ),
							    """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
							    """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
							    # fully supervised kinetics-600 checkpoints
							    """xclip-base-patch16-kinetics-600""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
							    ),
							    """xclip-base-patch16-kinetics-600-16-frames""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
							    ),
							    """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
							    # few shot
							    """xclip-base-patch16-hmdb-2-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
							    ),
							    """xclip-base-patch16-hmdb-4-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
							    ),
							    """xclip-base-patch16-hmdb-8-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
							    ),
							    """xclip-base-patch16-hmdb-16-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
							    ),
							    """xclip-base-patch16-ucf-2-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
							    ),
							    """xclip-base-patch16-ucf-4-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
							    ),
							    """xclip-base-patch16-ucf-8-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
							    ),
							    """xclip-base-patch16-ucf-16-shot""": (
							        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
							    ),
							    # zero shot
							    """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
							}

							lowercase_      :    Tuple       		= model_to_url[model_name]
							lowercase_      :    List[str]       		= 8
							if "16-frames" in model_name:
														lowercase_      :    Any       		= 16
							elif "shot" in model_name:
														lowercase_      :    Dict       		= 32

							lowercase_      :    Tuple       		= get_xclip_config(UpperCAmelCase__   ,   UpperCAmelCase__				)
							lowercase_      :    Optional[int]       		= XCLIPModel(UpperCAmelCase__				)
							model.eval()

							if "drive" in checkpoint_url:
														lowercase_      :    List[Any]       		= """pytorch_model.bin"""
														gdown.cached_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   quiet=UpperCAmelCase__				)
														lowercase_      :    Dict       		= torch.load(UpperCAmelCase__   ,   map_location="""cpu"""				)["""model"""]
							else:
														lowercase_      :    List[str]       		= torch.hub.load_state_dict_from_url(UpperCAmelCase__				)["""model"""]

							lowercase_      :    str       		= convert_state_dict(UpperCAmelCase__   ,   UpperCAmelCase__				)

							lowercase_      :    List[str]       		= XCLIPModel(UpperCAmelCase__				)
							lowercase_		,		lowercase_      :    Tuple       		= model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__				)
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							lowercase_      :    Any       		= 336 if model_name == """xclip-large-patch14-16-frames""" else 224
							lowercase_      :    Optional[int]       		= VideoMAEImageProcessor(size=UpperCAmelCase__				)
							lowercase_      :    List[str]       		= CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""				)
							lowercase_      :    str       		= CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""				)
							lowercase_      :    Optional[int]       		= XCLIPProcessor(image_processor=UpperCAmelCase__   ,   tokenizer=UpperCAmelCase__				)

							lowercase_      :    List[Any]       		= prepare_video(UpperCAmelCase__				)
							lowercase_      :    str       		= processor(
							    text=["""playing sports""", """eating spaghetti""", """go shopping"""]   ,   videos=UpperCAmelCase__   ,   return_tensors="""pt"""   ,   padding=UpperCAmelCase__				)

							print("""Shape of pixel values:"""   ,   inputs.pixel_values.shape				)

							with torch.no_grad():
														lowercase_      :    Any       		= model(**UpperCAmelCase__				)

							# Verify outputs
							lowercase_      :    List[str]       		= outputs.logits_per_video
							lowercase_      :    Dict       		= logits_per_video.softmax(dim=1				)
							print("""Probs:"""   ,   UpperCAmelCase__				)
							# kinetics-400
							if model_name == "xclip-base-patch32":
														lowercase_      :    List[str]       		= torch.tensor([[0.0019, 0.9951, 0.0030]]				)
							elif model_name == "xclip-base-patch32-16-frames":
														lowercase_      :    List[Any]       		= torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]				)
							elif model_name == "xclip-base-patch16":
														lowercase_      :    Dict       		= torch.tensor([[0.0083, 0.9681, 0.0236]]				)
							elif model_name == "xclip-base-patch16-16-frames":
														lowercase_      :    str       		= torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]				)
							elif model_name == "xclip-large-patch14":
														lowercase_      :    Tuple       		= torch.tensor([[0.0062, 0.9864, 0.0075]]				)
							elif model_name == "xclip-large-patch14-16-frames":
														lowercase_      :    Optional[Any]       		= torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]				)
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														lowercase_      :    Tuple       		= torch.tensor([[0.0555, 0.8914, 0.0531]]				)
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														lowercase_      :    Optional[Any]       		= torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]				)
							elif model_name == "xclip-large-patch14-kinetics-600":
														lowercase_      :    Tuple       		= torch.tensor([[0.0036, 0.9920, 0.0045]]				)
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														lowercase_      :    Optional[Any]       		= torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														lowercase_      :    int       		= torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														lowercase_      :    Any       		= torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]				)
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														lowercase_      :    Union[str, Any]       		= torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														lowercase_      :    Optional[Any]       		= torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														lowercase_      :    Union[str, Any]       		= torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]				)
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														lowercase_      :    Tuple       		= torch.tensor([[0.0027, 0.9904, 0.0070]]				)
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														lowercase_      :    Tuple       		= torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]				)
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														lowercase_      :    Any       		= torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]				)
							else:
														raise ValueError(F'''Model name {model_name} not supported'''				)
							assert torch.allclose(UpperCAmelCase__   ,   UpperCAmelCase__   ,   atol=1e-3				)
							print("""Looks ok!"""				)

							if pytorch_dump_folder_path is not None:
														print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
														model.save_pretrained(UpperCAmelCase__				)

							if push_to_hub:
														print("""Pushing model, processor and slow tokenizer files to the hub..."""				)
														model.push_to_hub(UpperCAmelCase__   ,   organization="""nielsr"""				)
														processor.push_to_hub(UpperCAmelCase__   ,   organization="""nielsr"""				)
														slow_tokenizer.push_to_hub(UpperCAmelCase__   ,   organization="""nielsr"""				)


if __name__ == "__main__":
						_lowercase		:				Any	       =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="xclip-base-patch32",
						    type=str,
						    help="Name of the model.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
						)

						_lowercase		:				Optional[Any]	       =		parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase		:				Optional[Any]	       =		{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				Any	       =		[
						    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SwiftFormerForImageClassification",
						    "SwiftFormerModel",
						    "SwiftFormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

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





