






'''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 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = XCLIPTextConfig()

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

						if "large" in model_name:
												UpperCamelCase__         = 768
												UpperCamelCase__         = 3072
												UpperCamelCase__         = 12

												UpperCamelCase__         = 1024
												UpperCamelCase__         = 4096
												UpperCamelCase__         = 16
												UpperCamelCase__         = 24
												UpperCamelCase__         = 768
												UpperCamelCase__         = 3072

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

						UpperCamelCase__         = XCLIPConfig.from_text_vision_configs(_A						,      _A					)

						if "large" in model_name:
												UpperCamelCase__         = 768

						return config







def 	__magic_name__( _A					):
						'''simple docstring'''





						if name == "token_embedding.weight":
												UpperCamelCase__         = name.replace("""token_embedding.weight"""						,      """text_model.embeddings.token_embedding.weight"""					)
						if name == "positional_embedding":
												UpperCamelCase__         = name.replace("""positional_embedding"""						,      """text_model.embeddings.position_embedding.weight"""					)
						if "ln_1" in name:
												UpperCamelCase__         = name.replace("""ln_1"""						,      """layer_norm1"""					)
						if "ln_2" in name:
												UpperCamelCase__         = name.replace("""ln_2"""						,      """layer_norm2"""					)
						if "c_fc" in name:
												UpperCamelCase__         = name.replace("""c_fc"""						,      """fc1"""					)
						if "c_proj" in name:
												UpperCamelCase__         = name.replace("""c_proj"""						,      """fc2"""					)
						if name.startswith("""transformer.resblocks"""					):
												UpperCamelCase__         = name.replace("""transformer.resblocks"""						,      """text_model.encoder.layers"""					)
						if "attn.out_proj" in name and "message" not in name:
												UpperCamelCase__         = name.replace("""attn.out_proj"""						,      """self_attn.out_proj"""					)
						if "ln_final" in name:
												UpperCamelCase__         = name.replace("""ln_final"""						,      """text_model.final_layer_norm"""					)
						# visual encoder
						if name == "visual.class_embedding":
												UpperCamelCase__         = name.replace("""visual.class_embedding"""						,      """vision_model.embeddings.class_embedding"""					)
						if name == "visual.positional_embedding":
												UpperCamelCase__         = name.replace("""visual.positional_embedding"""						,      """vision_model.embeddings.position_embedding.weight"""					)
						if name.startswith("""visual.transformer.resblocks"""					):
												UpperCamelCase__         = name.replace("""visual.transformer.resblocks"""						,      """vision_model.encoder.layers"""					)
						if "visual.conv1" in name:
												UpperCamelCase__         = name.replace("""visual.conv1"""						,      """vision_model.embeddings.patch_embedding"""					)
						if "visual.ln_pre" in name:
												UpperCamelCase__         = name.replace("""visual.ln_pre"""						,      """vision_model.pre_layernorm"""					)
						if "visual.ln_post" in name:
												UpperCamelCase__         = name.replace("""visual.ln_post"""						,      """vision_model.post_layernorm"""					)
						if "visual.proj" in name:
												UpperCamelCase__         = name.replace("""visual.proj"""						,      """visual_projection.weight"""					)
						if "text_projection" in name:
												UpperCamelCase__         = name.replace("""text_projection"""						,      """text_projection.weight"""					)
						# things on top
						if "prompts_visual_proj" in name:
												UpperCamelCase__         = name.replace("""prompts_visual_proj"""						,      """prompts_visual_projection"""					)
						if "prompts_visual_ln" in name:
												UpperCamelCase__         = name.replace("""prompts_visual_ln"""						,      """prompts_visual_layernorm"""					)
						# mit
						if name == "mit.positional_embedding":
												UpperCamelCase__         = name.replace("""positional"""						,      """position"""					)
						if name.startswith("""mit.resblocks"""					):
												UpperCamelCase__         = name.replace("""mit.resblocks"""						,      """mit.encoder.layers"""					)
						# prompts generator
						if name.startswith("""prompts_generator.norm"""					):
												UpperCamelCase__         = name.replace("""prompts_generator.norm"""						,      """prompts_generator.layernorm"""					)

						return name







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						for key in orig_state_dict.copy().keys():
												UpperCamelCase__         = orig_state_dict.pop(_A					)

												if "attn.in_proj" in key:
																		UpperCamelCase__         = key.split("""."""					)
																		if key.startswith("""visual"""					):
																								UpperCamelCase__         = key_split[3]
																								UpperCamelCase__         = config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				UpperCamelCase__         = val[
																																				    :dim, :
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCamelCase__         = val[
																																				    :dim
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				UpperCamelCase__         = val[
																																				    :dim, :
																																				]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2, :
																																				]
																																				UpperCamelCase__         = val[
																																				    -dim:, :
																																				]
																														else:
																																				UpperCamelCase__         = val[:dim]
																																				UpperCamelCase__         = val[
																																				    dim : dim * 2
																																				]
																																				UpperCamelCase__         = val[-dim:]
																		elif key.startswith("""mit"""					):
																								UpperCamelCase__         = key_split[2]
																								UpperCamelCase__         = config.vision_config.mit_hidden_size
																								if "weight" in key:
																														UpperCamelCase__         = val[:dim, :]
																														UpperCamelCase__         = val[dim : dim * 2, :]
																														UpperCamelCase__         = val[-dim:, :]
																								else:
																														UpperCamelCase__         = val[:dim]
																														UpperCamelCase__         = val[dim : dim * 2]
																														UpperCamelCase__         = val[-dim:]
																		else:
																								UpperCamelCase__         = key_split[2]
																								UpperCamelCase__         = config.text_config.hidden_size
																								if "weight" in key:
																														UpperCamelCase__         = val[:dim, :]
																														UpperCamelCase__         = val[
																														    dim : dim * 2, :
																														]
																														UpperCamelCase__         = val[-dim:, :]
																								else:
																														UpperCamelCase__         = val[:dim]
																														UpperCamelCase__         = val[
																														    dim : dim * 2
																														]
																														UpperCamelCase__         = val[-dim:]
												else:
																		UpperCamelCase__         = rename_key(_A					)
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								UpperCamelCase__         = val.T
																		UpperCamelCase__         = val

						return orig_state_dict







def 	__magic_name__( _A					):
						'''simple docstring'''





						if num_frames == 8:
												UpperCamelCase__         = """eating_spaghetti_8_frames.npy"""
						elif num_frames == 16:
												UpperCamelCase__         = """eating_spaghetti.npy"""
						elif num_frames == 32:
												UpperCamelCase__         = """eating_spaghetti_32_frames.npy"""
						UpperCamelCase__         = hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""						,      filename=_A						,      repo_type="""dataset"""						,      )
						UpperCamelCase__         = np.load(_A					)
						return list(_A					)







def 	__magic_name__( _A						,      _A=None						,      _A=False					):
						'''simple docstring'''





						UpperCamelCase__         = {
						    # 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""",
						}

						UpperCamelCase__         = model_to_url[model_name]
						UpperCamelCase__         = 8
						if "16-frames" in model_name:
												UpperCamelCase__         = 16
						elif "shot" in model_name:
												UpperCamelCase__         = 32

						UpperCamelCase__         = get_xclip_config(_A						,      _A					)
						UpperCamelCase__         = XCLIPModel(_A					)
						model.eval()

						if "drive" in checkpoint_url:
												UpperCamelCase__         = """pytorch_model.bin"""
												gdown.cached_download(_A						,      _A						,      quiet=_A					)
												UpperCamelCase__         = torch.load(_A						,      map_location="""cpu"""					)["""model"""]
						else:
												UpperCamelCase__         = torch.hub.load_state_dict_from_url(_A					)["""model"""]

						UpperCamelCase__         = convert_state_dict(_A						,      _A					)

						UpperCamelCase__         = XCLIPModel(_A					)
						UpperCamelCase__    ,   UpperCamelCase__         = model.load_state_dict(_A						,      strict=_A					)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						UpperCamelCase__         = 336 if model_name == """xclip-large-patch14-16-frames""" else 224
						UpperCamelCase__         = VideoMAEImageProcessor(size=_A					)
						UpperCamelCase__         = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""					)
						UpperCamelCase__         = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""					)
						UpperCamelCase__         = XCLIPProcessor(image_processor=_A						,      tokenizer=_A					)

						UpperCamelCase__         = prepare_video(_A					)
						UpperCamelCase__         = processor(
						    text=["""playing sports""", """eating spaghetti""", """go shopping"""]						,      videos=_A						,      return_tensors="""pt"""						,      padding=_A					)

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

						with torch.no_grad():
												UpperCamelCase__         = model(**_A					)

						# Verify outputs
						UpperCamelCase__         = outputs.logits_per_video
						UpperCamelCase__         = logits_per_video.softmax(dim=1					)
						print("""Probs:"""						,      _A					)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												UpperCamelCase__         = torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]					)
						elif model_name == "xclip-base-patch32-16-frames":
												UpperCamelCase__         = torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]					)
						elif model_name == "xclip-base-patch16":
												UpperCamelCase__         = torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]					)
						elif model_name == "xclip-base-patch16-16-frames":
												UpperCamelCase__         = torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]					)
						elif model_name == "xclip-large-patch14":
												UpperCamelCase__         = torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]					)
						elif model_name == "xclip-large-patch14-16-frames":
												UpperCamelCase__         = torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]					)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												UpperCamelCase__         = torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]					)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												UpperCamelCase__         = torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]					)
						elif model_name == "xclip-large-patch14-kinetics-600":
												UpperCamelCase__         = torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]					)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												UpperCamelCase__         = torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												UpperCamelCase__         = torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												UpperCamelCase__         = torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]					)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												UpperCamelCase__         = torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												UpperCamelCase__         = torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												UpperCamelCase__         = torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]					)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												UpperCamelCase__         = torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]					)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												UpperCamelCase__         = torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]					)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												UpperCamelCase__         = torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]					)
						else:
												raise ValueError(f"Model name {model_name} not supported"					)
						assert torch.allclose(_A						,      _A						,      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(_A					)

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


if __name__ == "__main__":
					lowerCamelCase_				: 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.'''
					)

					lowerCamelCase_				: Optional[int]    	=							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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_				: Tuple    	=							{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Tuple    	=							['''LayoutLMv3TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Union[str, Any]    	=							[
					    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LayoutLMv3ForQuestionAnswering''',
					    '''LayoutLMv3ForSequenceClassification''',
					    '''LayoutLMv3ForTokenClassification''',
					    '''LayoutLMv3Model''',
					    '''LayoutLMv3PreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							[
					    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLayoutLMv3ForQuestionAnswering''',
					    '''TFLayoutLMv3ForSequenceClassification''',
					    '''TFLayoutLMv3ForTokenClassification''',
					    '''TFLayoutLMv3Model''',
					    '''TFLayoutLMv3PreTrainedModel''',
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_				: Optional[Any]    	=							['''LayoutLMv3FeatureExtractor''']
					lowerCamelCase_				: Dict    	=							['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
					from .configuration_layoutlmva import (
					    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LayoutLMvaConfig,
					    LayoutLMvaOnnxConfig,
					)
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlmva import (
										    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMvaForQuestionAnswering,
										    TFLayoutLMvaForSequenceClassification,
										    TFLayoutLMvaForTokenClassification,
										    TFLayoutLMvaModel,
										    TFLayoutLMvaPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
										from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
					import sys

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