





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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
    lowercase__			    =						XCLIPTextConfig()

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

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

        lowercase__			    =						1024
        lowercase__			    =						4096
        lowercase__			    =						16
        lowercase__			    =						24
        lowercase__			    =						768
        lowercase__			    =						3072

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

    lowercase__			    =						XCLIPConfig.from_text_vision_configs(__A	,		__A	)

    if "large" in model_name:
        lowercase__			    =						768

    return config




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Dict:




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

    return name




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:




    for key in orig_state_dict.copy().keys():
        lowercase__			    =						orig_state_dict.pop(__A	)

        if "attn.in_proj" in key:
            lowercase__			    =						key.split('.'	)
            if key.startswith('visual'	):
                lowercase__			    =						key_split[3]
                lowercase__			    =						config.vision_config.hidden_size
                if "message_attn" in key:
                    if "weight" in key:
                        lowercase__			    =						val[
                            :dim, :
                        ]
                        lowercase__			    =						val[
                            dim : dim * 2, :
                        ]
                        lowercase__			    =						val[
                            -dim:, :
                        ]
                    else:
                        lowercase__			    =						val[
                            :dim
                        ]
                        lowercase__			    =						val[
                            dim : dim * 2
                        ]
                        lowercase__			    =						val[
                            -dim:
                        ]
                else:
                    if "weight" in key:
                        lowercase__			    =						val[
                            :dim, :
                        ]
                        lowercase__			    =						val[
                            dim : dim * 2, :
                        ]
                        lowercase__			    =						val[
                            -dim:, :
                        ]
                    else:
                        lowercase__			    =						val[:dim]
                        lowercase__			    =						val[
                            dim : dim * 2
                        ]
                        lowercase__			    =						val[-dim:]
            elif key.startswith('mit'	):
                lowercase__			    =						key_split[2]
                lowercase__			    =						config.vision_config.mit_hidden_size
                if "weight" in key:
                    lowercase__			    =						val[:dim, :]
                    lowercase__			    =						val[dim : dim * 2, :]
                    lowercase__			    =						val[-dim:, :]
                else:
                    lowercase__			    =						val[:dim]
                    lowercase__			    =						val[dim : dim * 2]
                    lowercase__			    =						val[-dim:]
            else:
                lowercase__			    =						key_split[2]
                lowercase__			    =						config.text_config.hidden_size
                if "weight" in key:
                    lowercase__			    =						val[:dim, :]
                    lowercase__			    =						val[
                        dim : dim * 2, :
                    ]
                    lowercase__			    =						val[-dim:, :]
                else:
                    lowercase__			    =						val[:dim]
                    lowercase__			    =						val[
                        dim : dim * 2
                    ]
                    lowercase__			    =						val[-dim:]
        else:
            lowercase__			    =						rename_key(__A	)
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                lowercase__			    =						val.T
            lowercase__			    =						val

    return orig_state_dict




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:




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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=False	)       ->     int:
    lowercase__			    =						{
        # 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__			    =						model_to_url[model_name]
    lowercase__			    =						8
    if "16-frames" in model_name:
        lowercase__			    =						16
    elif "shot" in model_name:
        lowercase__			    =						32

    lowercase__			    =						get_xclip_config(__A	,		__A	)
    lowercase__			    =						XCLIPModel(__A	)
    model.eval()

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

    lowercase__			    =						convert_state_dict(__A	,		__A	)

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

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

    lowercase__			    =						prepare_video(__A	)
    lowercase__			    =						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():
        lowercase__			    =						model(**__A	)

    # Verify outputs
    lowercase__			    =						outputs.logits_per_video
    lowercase__			    =						logits_per_video.softmax(dim=1	)
    print('Probs:'	,		__A	)
    # kinetics-400
    if model_name == "xclip-base-patch32":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]	)
    elif model_name == "xclip-base-patch16":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]	)
    elif model_name == "xclip-large-patch14":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]	)
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]	)
    elif model_name == "xclip-large-patch14-kinetics-600":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        lowercase__			    =						torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        lowercase__			    =						torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]	)
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        lowercase__			    =						torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        lowercase__			    =						torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        lowercase__			    =						torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]	)
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        lowercase__			    =						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":
        lowercase__			    =						torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]	)
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        lowercase__			    =						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__":
      lowercase_					     =	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_					     =	parser.parse_args()
      convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from typing import TYPE_CHECKING

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


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase_					     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


