





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViltImageProcessor





class A_   (     unittest.TestCase  ):




   def __init__(self      :Dict       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Dict=7       ,  _UpperCamelCase      :List[Any]=3       ,  _UpperCamelCase      :Dict=18       ,  _UpperCamelCase      :str=30       ,  _UpperCamelCase      :Any=400       ,  _UpperCamelCase      :Optional[Any]=True       ,  _UpperCamelCase      :Any=None       ,  _UpperCamelCase      :Tuple=2       ,  _UpperCamelCase      :Any=True       ,  _UpperCamelCase      :Optional[int]=[0.5, 0.5, 0.5]       ,  _UpperCamelCase      :Union[str, Any]=[0.5, 0.5, 0.5]       ,  )->   Tuple:
         __A			  =							size if size is not None else {'''shortest_edge''': 30}
         __A			  =							parent
         __A			  =							batch_size
         __A			  =							num_channels
         __A			  =							image_size
         __A			  =							min_resolution
         __A			  =							max_resolution
         __A			  =							do_resize
         __A			  =							size
         __A			  =							size_divisor
         __A			  =							do_normalize
         __A			  =							image_mean
         __A			  =							image_std




   def _lowerCAmelCase (self      :Dict					)->   Any:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }






   def _lowerCAmelCase (self      :List[Any]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Dict=False					)->   Optional[Any]:
         if not batched:
               __A			  =							self.size['''shortest_edge''']
               __A			  =							image_inputs[0]
               if isinstance(_lowerCamelCase       ,  Image.Image					):
                     __A			  =							image.size
               else:
                     __A			  =							image.shape[1], image.shape[2]
               __A			  =							size / min(_lowerCamelCase       ,  _lowerCamelCase					)
               if h < w:
                     __A			  =							size, scale * w
               else:
                     __A			  =							scale * h, size

               __A			  =							int((1333 / 800) * size					)
               if max(_lowerCamelCase       ,  _lowerCamelCase					) > max_size:
                     __A			  =							max_size / max(_lowerCamelCase       ,  _lowerCamelCase					)
                     __A			  =							newh * scale
                     __A			  =							neww * scale

               __A			  =							int(newh + 0.5					), int(neww + 0.5					)
               __A			  =							(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               __A			  =							[]
               for image in image_inputs:
                     __A			  =							self.get_expected_values([image]					)
                     expected_values.append((expected_height, expected_width)					)
               __A			  =							max(_lowerCamelCase       ,  key=lambda _UpperCamelCase					: item[0]					)[0]
               __A			  =							max(_lowerCamelCase       ,  key=lambda _UpperCamelCase					: item[1]					)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class A_   (     a__					, unittest.TestCase  ):
   lowerCAmelCase__  	=					ViltImageProcessor if is_vision_available() else None




   def _lowerCAmelCase (self      :int					)->   List[Any]:
         __A			  =							ViltImageProcessingTester(self					)




   @property
   def _lowerCAmelCase (self      :Dict					)->   Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()




   def _lowerCAmelCase (self      :Optional[Any]					)->   Tuple:
         __A			  =							self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''image_mean'''					)					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''image_std'''					)					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''do_normalize'''					)					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''do_resize'''					)					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''size'''					)					)
         self.assertTrue(hasattr(_lowerCamelCase       ,  '''size_divisor'''					)					)




   def _lowerCAmelCase (self      :int					)->   int:
         __A			  =							self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size       ,  {'''shortest_edge''': 30}					)

         __A			  =							self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42					)
         self.assertEqual(image_processor.size       ,  {'''shortest_edge''': 42}					)




   def _lowerCAmelCase (self      :Tuple					)->   Union[str, Any]:
         pass




   def _lowerCAmelCase (self      :List[Any]					)->   Optional[int]:
         # Initialize image_processing
         __A			  =							self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         __A			  =							prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCamelCase					)
         for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase       ,  Image.Image					)

         # Test not batched input
         __A			  =							image_processing(image_inputs[0]       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

         # Test batched
         __A			  =							image_processing(_lowerCamelCase       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,  batched=_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,  )




   def _lowerCAmelCase (self      :str					)->   Optional[Any]:
         # Initialize image_processing
         __A			  =							self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         __A			  =							prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCamelCase       ,  numpify=_lowerCamelCase					)
         for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase       ,  np.ndarray					)

         # Test not batched input
         __A			  =							image_processing(image_inputs[0]       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

         # Test batched
         __A			  =							image_processing(_lowerCamelCase       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,  batched=_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,  )






   def _lowerCAmelCase (self      :List[Any]					)->   str:
         # Initialize image_processing
         __A			  =							self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         __A			  =							prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowerCamelCase       ,  torchify=_lowerCamelCase					)
         for image in image_inputs:
               self.assertIsInstance(_lowerCamelCase       ,  torch.Tensor					)

         # Test not batched input
         __A			  =							image_processing(image_inputs[0]       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

         # Test batched
         __A			  =							image_processing(_lowerCamelCase       ,  return_tensors='''pt'''					).pixel_values

         __A			  =							self.image_processor_tester.get_expected_values(_lowerCamelCase       ,  batched=_lowerCamelCase					)
         self.assertEqual(
             encoded_images.shape       ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,  )







import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''huggingface/label-files'''
    if "k400" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 400
        SCREAMING_SNAKE_CASE				:				Tuple				      = '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        SCREAMING_SNAKE_CASE				:				Any				      = 600
        SCREAMING_SNAKE_CASE				:				int				      = '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 174
        SCREAMING_SNAKE_CASE				:				int				      = '''something-something-v2-id2label.json'''
    else:
        raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = {int(a__   ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE				:				str				      = idalabel
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {v: k for k, v in idalabel.items()}

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    if "encoder." in name:
        SCREAMING_SNAKE_CASE				:				Tuple				      = name.replace('''encoder.''' ,						''''''   )
    if "cls_token" in name:
        SCREAMING_SNAKE_CASE				:				List[str]				      = name.replace('''cls_token''' ,						'''timesformer.embeddings.cls_token'''   )
    if "pos_embed" in name:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = name.replace('''pos_embed''' ,						'''timesformer.embeddings.position_embeddings'''   )
    if "time_embed" in name:
        SCREAMING_SNAKE_CASE				:				Any				      = name.replace('''time_embed''' ,						'''timesformer.embeddings.time_embeddings'''   )
    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE				:				Any				      = name.replace('''patch_embed.proj''' ,						'''timesformer.embeddings.patch_embeddings.projection'''   )
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''patch_embed.norm''' ,						'''timesformer.embeddings.norm'''   )
    if "blocks" in name:
        SCREAMING_SNAKE_CASE				:				Any				      = name.replace('''blocks''' ,						'''timesformer.encoder.layer'''   )
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''attn.proj''' ,						'''attention.output.dense'''   )
    if "attn" in name and "bias" not in name and "temporal" not in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''attn''' ,						'''attention.self'''   )
    if "attn" in name and "temporal" not in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''attn''' ,						'''attention.attention'''   )
    if "temporal_norm1" in name:
        SCREAMING_SNAKE_CASE				:				List[str]				      = name.replace('''temporal_norm1''' ,						'''temporal_layernorm'''   )
    if "temporal_attn.proj" in name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = name.replace('''temporal_attn''' ,						'''temporal_attention.output.dense'''   )
    if "temporal_fc" in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''temporal_fc''' ,						'''temporal_dense'''   )
    if "norm1" in name and "temporal" not in name:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = name.replace('''norm1''' ,						'''layernorm_before'''   )
    if "norm2" in name:
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = name.replace('''norm2''' ,						'''layernorm_after'''   )
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE				:				List[str]				      = name.replace('''mlp.fc1''' ,						'''intermediate.dense'''   )
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''mlp.fc2''' ,						'''output.dense'''   )
    if "norm.weight" in name and "fc" not in name and "temporal" not in name:
        SCREAMING_SNAKE_CASE				:				Optional[int]				      = name.replace('''norm.weight''' ,						'''timesformer.layernorm.weight'''   )
    if "norm.bias" in name and "fc" not in name and "temporal" not in name:
        SCREAMING_SNAKE_CASE				:				int				      = name.replace('''norm.bias''' ,						'''timesformer.layernorm.bias'''   )
    if "head" in name:
        SCREAMING_SNAKE_CASE				:				Any				      = name.replace('''head''' ,						'''classifier'''   )

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

        if key.startswith('''model.'''   ):
            SCREAMING_SNAKE_CASE				:				Dict				      = key.replace('''model.''' ,						''''''   )

        if "qkv" in key:
            SCREAMING_SNAKE_CASE				:				int				      = key.split('''.'''   )
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = int(key_split[1]   )
            SCREAMING_SNAKE_CASE				:				int				      = '''timesformer.encoder.layer.'''
            if "temporal" in key:
                SCREAMING_SNAKE_CASE				:				Tuple				      = '''.temporal_attention.attention.qkv.'''
            else:
                SCREAMING_SNAKE_CASE				:				List[Any]				      = '''.attention.attention.qkv.'''
            if "weight" in key:
                SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = val
            else:
                SCREAMING_SNAKE_CASE				:				Optional[int]				      = val
        else:
            SCREAMING_SNAKE_CASE				:				Tuple				      = val

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,						filename='''eating_spaghetti.npy''' ,						repo_type='''dataset'''   )
    SCREAMING_SNAKE_CASE				:				int				      = np.load(a__   )
    return list(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

    # download original checkpoint, hosted on Google Drive
    SCREAMING_SNAKE_CASE				:				Tuple				      = '''pytorch_model.bin'''
    gdown.cached_download(a__ ,						a__ ,						quiet=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = torch.load(a__ ,						map_location='''cpu'''   )
    if "model" in files:
        SCREAMING_SNAKE_CASE				:				Tuple				      = files['''model''']
    elif "module" in files:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = files['''module''']
    else:
        SCREAMING_SNAKE_CASE				:				Any				      = files['''model_state''']
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = convert_state_dict(a__ ,						a__   )

    model.load_state_dict(a__   )
    model.eval()

    # verify model on basic input
    SCREAMING_SNAKE_CASE				:				str				      = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,						image_std=[0.5, 0.5, 0.5]   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = prepare_video()
    SCREAMING_SNAKE_CASE				:				str				      = image_processor(video[:8] ,						return_tensors='''pt'''   )

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
        '''timesformer-base-finetuned-k400''',
        '''timesformer-large-finetuned-k400''',
        '''timesformer-hr-finetuned-k400''',
        # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
        '''timesformer-base-finetuned-k600''',
        '''timesformer-large-finetuned-k600''',
        '''timesformer-hr-finetuned-k600''',
        # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
        '''timesformer-base-finetuned-ssv2''',
        '''timesformer-large-finetuned-ssv2''',
        '''timesformer-hr-finetuned-ssv2''',
    ]

    # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
    if model_name == "timesformer-base-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-3.6_756, -0.7_513, 0.7_180]   )
    else:
        raise ValueError(F"""Model name not supported. Should be one of {model_names}"""   )

    # verify logits
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3] ,						a__ ,						atol=1e-4   )
    print('''Logits ok!'''   )

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""   )
        image_processor.save_pretrained(a__   )
        model.save_pretrained(a__   )

    if push_to_hub:
        print('''Pushing to the hub...'''   )
        model.push_to_hub(F"""fcakyon/{model_name}"""   )


if __name__ == "__main__":
      a__				:      Optional[int]    		=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
          type=str,
          help=(
              '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
              ''' download link.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''''',
          type=str,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', type=str, help='''Name of the model.''')
      parser.add_argument(
          '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
      )

      a__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
