
"""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 ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			List[str]       =     XCLIPTextConfig()

  # derive patch size from model name
  _lowercase:			Optional[int]       =     model_name.find('''patch'''						)
  _lowercase:			Dict       =     int(model_name[start_idx + len('''patch'''						) : start_idx + len('''patch'''						) + 2]						)
  _lowercase:			List[str]       =     XCLIPVisionConfig(patch_size=_UpperCamelCase ,				num_frames=_UpperCamelCase						)

  if "large" in model_name:
    _lowercase:			Any       =     768
    _lowercase:			Optional[Any]       =     3_072
    _lowercase:			str       =     12

    _lowercase:			Any       =     1_024
    _lowercase:			str       =     4_096
    _lowercase:			Any       =     16
    _lowercase:			List[str]       =     24
    _lowercase:			Optional[Any]       =     768
    _lowercase:			Union[str, Any]       =     3_072

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

  _lowercase:			Optional[Any]       =     XCLIPConfig.from_text_vision_configs(_UpperCamelCase ,				_UpperCamelCase						)

  if "large" in model_name:
    _lowercase:			Tuple       =     768

  return config




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if name == "token_embedding.weight":
    _lowercase:			Dict       =     name.replace('''token_embedding.weight''' ,				'''text_model.embeddings.token_embedding.weight'''						)
  if name == "positional_embedding":
    _lowercase:			Optional[Any]       =     name.replace('''positional_embedding''' ,				'''text_model.embeddings.position_embedding.weight'''						)
  if "ln_1" in name:
    _lowercase:			int       =     name.replace('''ln_1''' ,				'''layer_norm1'''						)
  if "ln_2" in name:
    _lowercase:			str       =     name.replace('''ln_2''' ,				'''layer_norm2'''						)
  if "c_fc" in name:
    _lowercase:			Union[str, Any]       =     name.replace('''c_fc''' ,				'''fc1'''						)
  if "c_proj" in name:
    _lowercase:			List[Any]       =     name.replace('''c_proj''' ,				'''fc2'''						)
  if name.startswith('''transformer.resblocks'''						):
    _lowercase:			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:			Dict       =     name.replace('''ln_final''' ,				'''text_model.final_layer_norm'''						)
  # visual encoder
  if name == "visual.class_embedding":
    _lowercase:			int       =     name.replace('''visual.class_embedding''' ,				'''vision_model.embeddings.class_embedding'''						)
  if name == "visual.positional_embedding":
    _lowercase:			Tuple       =     name.replace('''visual.positional_embedding''' ,				'''vision_model.embeddings.position_embedding.weight'''						)
  if name.startswith('''visual.transformer.resblocks'''						):
    _lowercase:			int       =     name.replace('''visual.transformer.resblocks''' ,				'''vision_model.encoder.layers'''						)
  if "visual.conv1" in name:
    _lowercase:			List[Any]       =     name.replace('''visual.conv1''' ,				'''vision_model.embeddings.patch_embedding'''						)
  if "visual.ln_pre" in name:
    _lowercase:			Union[str, Any]       =     name.replace('''visual.ln_pre''' ,				'''vision_model.pre_layernorm'''						)
  if "visual.ln_post" in name:
    _lowercase:			Optional[Any]       =     name.replace('''visual.ln_post''' ,				'''vision_model.post_layernorm'''						)
  if "visual.proj" in name:
    _lowercase:			Optional[int]       =     name.replace('''visual.proj''' ,				'''visual_projection.weight'''						)
  if "text_projection" in name:
    _lowercase:			str       =     name.replace('''text_projection''' ,				'''text_projection.weight'''						)
  # things on top
  if "prompts_visual_proj" in name:
    _lowercase:			Tuple       =     name.replace('''prompts_visual_proj''' ,				'''prompts_visual_projection'''						)
  if "prompts_visual_ln" in name:
    _lowercase:			Tuple       =     name.replace('''prompts_visual_ln''' ,				'''prompts_visual_layernorm'''						)
  # mit
  if name == "mit.positional_embedding":
    _lowercase:			Optional[int]       =     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:			str       =     name.replace('''prompts_generator.norm''' ,				'''prompts_generator.layernorm'''						)

  return name




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  for key in orig_state_dict.copy().keys():
    _lowercase:			Union[str, Any]       =     orig_state_dict.pop(_UpperCamelCase						)

    if "attn.in_proj" in key:
      _lowercase:			Union[str, Any]       =     key.split('''.'''						)
      if key.startswith('''visual'''						):
        _lowercase:			Any       =     key_split[3]
        _lowercase:			Optional[Any]       =     config.vision_config.hidden_size
        if "message_attn" in key:
          if "weight" in key:
            _lowercase:			Dict       =     val[
                :dim, :
            ]
            _lowercase:			Tuple       =     val[
                dim : dim * 2, :
            ]
            _lowercase:			Any       =     val[
                -dim:, :
            ]
          else:
            _lowercase:			Dict       =     val[
                :dim
            ]
            _lowercase:			Tuple       =     val[
                dim : dim * 2
            ]
            _lowercase:			int       =     val[
                -dim:
            ]
        else:
          if "weight" in key:
            _lowercase:			List[Any]       =     val[
                :dim, :
            ]
            _lowercase:			Optional[int]       =     val[
                dim : dim * 2, :
            ]
            _lowercase:			Union[str, Any]       =     val[
                -dim:, :
            ]
          else:
            _lowercase:			Any       =     val[:dim]
            _lowercase:			List[str]       =     val[
                dim : dim * 2
            ]
            _lowercase:			Optional[Any]       =     val[-dim:]
      elif key.startswith('''mit'''						):
        _lowercase:			Tuple       =     key_split[2]
        _lowercase:			str       =     config.vision_config.mit_hidden_size
        if "weight" in key:
          _lowercase:			str       =     val[:dim, :]
          _lowercase:			Union[str, Any]       =     val[dim : dim * 2, :]
          _lowercase:			int       =     val[-dim:, :]
        else:
          _lowercase:			str       =     val[:dim]
          _lowercase:			Optional[Any]       =     val[dim : dim * 2]
          _lowercase:			Any       =     val[-dim:]
      else:
        _lowercase:			List[Any]       =     key_split[2]
        _lowercase:			int       =     config.text_config.hidden_size
        if "weight" in key:
          _lowercase:			List[str]       =     val[:dim, :]
          _lowercase:			Dict       =     val[
              dim : dim * 2, :
          ]
          _lowercase:			Dict       =     val[-dim:, :]
        else:
          _lowercase:			int       =     val[:dim]
          _lowercase:			Dict       =     val[
              dim : dim * 2
          ]
          _lowercase:			Any       =     val[-dim:]
    else:
      _lowercase:			str       =     rename_key(_UpperCamelCase						)
      if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
        _lowercase:			Any       =     val.T
      _lowercase:			Any       =     val

  return orig_state_dict




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None ,				_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			str       =     {
      # 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:			Optional[int]       =     model_to_url[model_name]
  _lowercase:			List[str]       =     8
  if "16-frames" in model_name:
    _lowercase:			Optional[Any]       =     16
  elif "shot" in model_name:
    _lowercase:			Optional[int]       =     32

  _lowercase:			Union[str, Any]       =     get_xclip_config(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			Any       =     XCLIPModel(_UpperCamelCase						)
  model.eval()

  if "drive" in checkpoint_url:
    _lowercase:			int       =     '''pytorch_model.bin'''
    gdown.cached_download(_UpperCamelCase ,				_UpperCamelCase ,				quiet=_UpperCamelCase						)
    _lowercase:			Union[str, Any]       =     torch.load(_UpperCamelCase ,				map_location='''cpu'''						)['''model''']
  else:
    _lowercase:			List[Any]       =     torch.hub.load_state_dict_from_url(_UpperCamelCase						)['''model''']

  _lowercase:			Tuple       =     convert_state_dict(_UpperCamelCase ,				_UpperCamelCase						)

  _lowercase:			List[Any]       =     XCLIPModel(_UpperCamelCase						)
  _lowercase ,				_lowercase:			Any       =     model.load_state_dict(_UpperCamelCase ,				strict=_UpperCamelCase						)
  assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
  model.eval()

  _lowercase:			Union[str, Any]       =     336 if model_name == '''xclip-large-patch14-16-frames''' else 224
  _lowercase:			List[Any]       =     VideoMAEImageProcessor(size=_UpperCamelCase						)
  _lowercase:			Tuple       =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
  _lowercase:			Dict       =     CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''						)
  _lowercase:			Dict       =     XCLIPProcessor(image_processor=_UpperCamelCase ,				tokenizer=_UpperCamelCase						)

  _lowercase:			Dict       =     prepare_video(_UpperCamelCase						)
  _lowercase:			Dict       =     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:			Union[str, Any]       =     model(**_UpperCamelCase						)

  # Verify outputs
  _lowercase:			str       =     outputs.logits_per_video
  _lowercase:			str       =     logits_per_video.softmax(dim=1						)
  print('''Probs:''' ,				_UpperCamelCase						)
  # kinetics-400
  if model_name == "xclip-base-patch32":
    _lowercase:			Optional[int]       =     torch.tensor([[0.0_019, 0.9_951, 0.0_030]]						)
  elif model_name == "xclip-base-patch32-16-frames":
    _lowercase:			Dict       =     torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]						)
  elif model_name == "xclip-base-patch16":
    _lowercase:			Dict       =     torch.tensor([[0.0_083, 0.9_681, 0.0_236]]						)
  elif model_name == "xclip-base-patch16-16-frames":
    _lowercase:			List[str]       =     torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]						)
  elif model_name == "xclip-large-patch14":
    _lowercase:			List[Any]       =     torch.tensor([[0.0_062, 0.9_864, 0.0_075]]						)
  elif model_name == "xclip-large-patch14-16-frames":
    _lowercase:			Dict       =     torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]						)
  # kinetics-600
  elif model_name == "xclip-base-patch16-kinetics-600":
    _lowercase:			Union[str, Any]       =     torch.tensor([[0.0_555, 0.8_914, 0.0_531]]						)
  elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
    _lowercase:			Any       =     torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]						)
  elif model_name == "xclip-large-patch14-kinetics-600":
    _lowercase:			List[Any]       =     torch.tensor([[0.0_036, 0.9_920, 0.0_045]]						)
  # few shot
  elif model_name == "xclip-base-patch16-hmdb-2-shot":
    _lowercase:			str       =     torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]						)
  elif model_name == "xclip-base-patch16-hmdb-4-shot":
    _lowercase:			Dict       =     torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]						)
  elif model_name == "xclip-base-patch16-hmdb-8-shot":
    _lowercase:			Union[str, Any]       =     torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]						)
  elif model_name == "xclip-base-patch16-hmdb-16-shot":
    _lowercase:			Optional[Any]       =     torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]						)
  elif model_name == "xclip-base-patch16-ucf-2-shot":
    _lowercase:			Any       =     torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]						)
  elif model_name == "xclip-base-patch16-ucf-4-shot":
    _lowercase:			List[Any]       =     torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]						)
  elif model_name == "xclip-base-patch16-ucf-8-shot":
    _lowercase:			List[str]       =     torch.tensor([[0.0_027, 0.9_904, 0.0_070]]						)
  elif model_name == "xclip-base-patch16-ucf-16-shot":
    _lowercase:			Tuple       =     torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]						)
  # zero shot
  elif model_name == "xclip-base-patch16-zero-shot":
    _lowercase:			Any       =     torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-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__":
  A__   :					Optional[int]							=   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.'
  )

  A__   :					Dict							=   parser.parse_args()
  convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A__   :					Tuple							=   pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
A__   :					Optional[int]							=   dataset.iloc[:, 1:2].values
A__   :					Union[str, Any]							=   dataset.iloc[:, 2].values


A__							, A__							, A__							, A__   :					Dict							=   train_test_split(X, y, test_size=0.2, random_state=0)


A__   :					Union[str, Any]							=   PolynomialFeatures(degree=4)
A__   :					List[Any]							=   poly_reg.fit_transform(X)
A__   :					Dict							=   LinearRegression()
pol_reg.fit(X_poly, y)




def  _lowerCAmelCase		(		):






  """simple docstring"""



  plt.scatter(_UpperCamelCase ,				_UpperCamelCase ,				color='''red'''						)
  plt.plot(_UpperCamelCase ,				pol_reg.predict(poly_reg.fit_transform(_UpperCamelCase						)						) ,				color='''blue'''						)
  plt.title('''Truth or Bluff (Linear Regression)'''						)
  plt.xlabel('''Position level'''						)
  plt.ylabel('''Salary'''						)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003
