




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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   XCLIPTextConfig()

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

       if "large" in model_name:
              A__							=   768
              A__							=   3_072
              A__							=   12

              A__							=   1_024
              A__							=   4_096
              A__							=   16
              A__							=   24
              A__							=   768
              A__							=   3_072

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

       A__							=   XCLIPConfig.from_text_vision_configs(lowercase_			,    lowercase_						)

       if "large" in model_name:
              A__							=   768

       return config







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

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

       return name







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

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

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=None			,    lowercase_=False						)       ->	List[str]:



       """simple docstring"""

       A__							=   {
           # 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''',
       }

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

       A__							=   get_xclip_config(lowercase_			,    lowercase_						)
       A__							=   XCLIPModel(lowercase_						)
       model.eval()

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

       A__							=   convert_state_dict(lowercase_			,    lowercase_						)

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

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

       A__							=   prepare_video(lowercase_						)
       A__							=   processor(
           text=['''playing sports''', '''eating spaghetti''', '''go shopping''']			,    videos=lowercase_			,    return_tensors='''pt'''			,    padding=lowercase_						)

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

       with torch.no_grad():
              A__							=   model(**lowercase_						)

       # Verify outputs
       A__							=   outputs.logits_per_video
       A__							=   logits_per_video.softmax(dim=1						)
       print('''Probs:'''			,    lowercase_						)
       # kinetics-400
       if model_name == "xclip-base-patch32":
              A__							=   torch.tensor([[0.00_19, 0.99_51, 0.00_30]]						)
       elif model_name == "xclip-base-patch32-16-frames":
              A__							=   torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]						)
       elif model_name == "xclip-base-patch16":
              A__							=   torch.tensor([[0.00_83, 0.96_81, 0.02_36]]						)
       elif model_name == "xclip-base-patch16-16-frames":
              A__							=   torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]						)
       elif model_name == "xclip-large-patch14":
              A__							=   torch.tensor([[0.00_62, 0.98_64, 0.00_75]]						)
       elif model_name == "xclip-large-patch14-16-frames":
              A__							=   torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]						)
       # kinetics-600
       elif model_name == "xclip-base-patch16-kinetics-600":
              A__							=   torch.tensor([[0.05_55, 0.89_14, 0.05_31]]						)
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              A__							=   torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]						)
       elif model_name == "xclip-large-patch14-kinetics-600":
              A__							=   torch.tensor([[0.00_36, 0.99_20, 0.00_45]]						)
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              A__							=   torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-4-shot":
              A__							=   torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-8-shot":
              A__							=   torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]						)
       elif model_name == "xclip-base-patch16-hmdb-16-shot":
              A__							=   torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-2-shot":
              A__							=   torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-4-shot":
              A__							=   torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]						)
       elif model_name == "xclip-base-patch16-ucf-8-shot":
              A__							=   torch.tensor([[0.00_27, 0.99_04, 0.00_70]]						)
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              A__							=   torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]						)
       # zero shot
       elif model_name == "xclip-base-patch16-zero-shot":
              A__							=   torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]						)
       else:
              raise ValueError(f"""Model name {model_name} not supported"""						)
       assert torch.allclose(lowercase_			,    lowercase_			,    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(lowercase_						)

       if push_to_hub:
              print('''Pushing model, processor and slow tokenizer files to the hub...'''						)
              model.push_to_hub(lowercase_			,    organization='''nielsr'''						)
              processor.push_to_hub(lowercase_			,    organization='''nielsr'''						)
              slow_tokenizer.push_to_hub(lowercase_			,    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     :		Any								=       parser.parse_args()
  convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       A__							=   []
       A__							=   []
       A__							=   {
           '''^''': 3,
           '''*''': 2,
           '''/''': 2,
           '''%''': 2,
           '''+''': 1,
           '''-''': 1,
       }  # Priority of each operator
       A__							=   len(lowercase_						) if (len(lowercase_						) > 7) else 7

       # Print table header for output
       print(
           '''Symbol'''.center(8						)			,    '''Stack'''.center(lowercase_						)			,    '''Postfix'''.center(lowercase_						)			,    sep=''' | '''			,    )
       print('''-''' * (print_width * 3 + 7)						)

       for x in infix:
              if x.isalpha() or x.isdigit():
                     post_fix.append(lowercase_						)  # if x is Alphabet / Digit, add it to Postfix
              elif x == "(":
                     stack.append(lowercase_						)  # if x is "(" push to Stack
              elif x == ")":  # if x is ")" pop stack until "(" is encountered
                     while stack[-1] != "(":
                            post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
                     stack.pop()
              else:
                     if len(lowercase_						) == 0:
                            stack.append(lowercase_						)  # If stack is empty, push x to stack
                     else:  # while priority of x is not > priority of element in the stack
                            while len(lowercase_						) > 0 and priority[x] <= priority[stack[-1]]:
                                   post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                            stack.append(lowercase_						)  # push x to stack

              print(
                  x.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       while len(lowercase_						) > 0:  # while stack is not empty
              post_fix.append(stack.pop()						)  # pop stack & add to Postfix
              print(
                  ''' '''.center(8						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    (''''''.join(lowercase_						)).ljust(lowercase_						)			,    sep=''' | '''			,    )  # Output in tabular format

       return "".join(lowercase_						)  # return Postfix as str







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

       A__							=   list(infix[::-1]						)  # reverse the infix equation

       for i in range(len(lowercase_						)						):
              if infix[i] == "(":
                     A__							=   ''')'''  # change "(" to ")"
              elif infix[i] == ")":
                     A__							=   '''('''  # change ")" to "("

       return (infix_2_postfix(''''''.join(lowercase_						)						))[
           ::-1
       ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
  _lowerCamelCase     :		List[str]								=       input("""\nEnter an Infix Equation = """)  # Input an Infix equation
  _lowerCamelCase     :		Dict								=       """""".join(Infix.split())  # Remove spaces from the input
  print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")


