"""simple docstring"""
import argparse
import json

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

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]   )   ->			Union[str, Any]:





      """simple docstring"""






      snake_case        =     VideoMAEConfig()

      set_architecture_configs(A__	,     A__   )

      if "finetuned" not in model_name:
            snake_case        =     False

      if "finetuned" in model_name:
            snake_case        =     """huggingface/label-files"""
            if "kinetics" in model_name:
                  snake_case        =     4_0_0
                  snake_case        =     """kinetics400-id2label.json"""
            elif "ssv2" in model_name:
                  snake_case        =     1_7_4
                  snake_case        =     """something-something-v2-id2label.json"""
            else:
                  raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'   )
            snake_case        =     json.load(open(hf_hub_download(A__	,     A__	,     repo_type='dataset'   )	,     'r'   )   )
            snake_case        =     {int(A__   ): v for k, v in idalabel.items()}
            snake_case        =     idalabel
            snake_case        =     {v: k for k, v in idalabel.items()}

      return config





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[Any]   )   ->			List[Any]:





      """simple docstring"""






      if "small" in model_name:
            snake_case        =     3_8_4
            snake_case        =     1_5_3_6
            snake_case        =     1_2
            snake_case        =     1_6
            snake_case        =     1_2
            snake_case        =     3
            snake_case        =     1_9_2
            snake_case        =     7_6_8
      elif "large" in model_name:
            snake_case        =     1_0_2_4
            snake_case        =     4_0_9_6
            snake_case        =     2_4
            snake_case        =     1_6
            snake_case        =     1_2
            snake_case        =     8
            snake_case        =     5_1_2
            snake_case        =     2_0_4_8
      elif "huge" in model_name:
            snake_case        =     1_2_8_0
            snake_case        =     5_1_2_0
            snake_case        =     3_2
            snake_case        =     1_6
            snake_case        =     1_2
            snake_case        =     8
            snake_case        =     6_4_0
            snake_case        =     2_5_6_0
      elif "base" not in model_name:
            raise ValueError('Model name should include either \"small\", \"base\", \"large\", or \"huge\"'   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]   )   ->			str:





      """simple docstring"""






      if "encoder." in name:
            snake_case        =     name.replace('encoder.'	,     ''   )
      if "cls_token" in name:
            snake_case        =     name.replace('cls_token'	,     'videomae.embeddings.cls_token'   )
      if "decoder_pos_embed" in name:
            snake_case        =     name.replace('decoder_pos_embed'	,     'decoder.decoder_pos_embed'   )
      if "pos_embed" in name and "decoder" not in name:
            snake_case        =     name.replace('pos_embed'	,     'videomae.embeddings.position_embeddings'   )
      if "patch_embed.proj" in name:
            snake_case        =     name.replace('patch_embed.proj'	,     'videomae.embeddings.patch_embeddings.projection'   )
      if "patch_embed.norm" in name:
            snake_case        =     name.replace('patch_embed.norm'	,     'videomae.embeddings.norm'   )
      if "decoder.blocks" in name:
            snake_case        =     name.replace('decoder.blocks'	,     'decoder.decoder_layers'   )
      if "blocks" in name:
            snake_case        =     name.replace('blocks'	,     'videomae.encoder.layer'   )
      if "attn.proj" in name:
            snake_case        =     name.replace('attn.proj'	,     'attention.output.dense'   )
      if "attn" in name and "bias" not in name:
            snake_case        =     name.replace('attn'	,     'attention.self'   )
      if "attn" in name:
            snake_case        =     name.replace('attn'	,     'attention.attention'   )
      if "norm1" in name:
            snake_case        =     name.replace('norm1'	,     'layernorm_before'   )
      if "norm2" in name:
            snake_case        =     name.replace('norm2'	,     'layernorm_after'   )
      if "mlp.fc1" in name:
            snake_case        =     name.replace('mlp.fc1'	,     'intermediate.dense'   )
      if "mlp.fc2" in name:
            snake_case        =     name.replace('mlp.fc2'	,     'output.dense'   )
      if "decoder_embed" in name:
            snake_case        =     name.replace('decoder_embed'	,     'decoder.decoder_embed'   )
      if "decoder_norm" in name:
            snake_case        =     name.replace('decoder_norm'	,     'decoder.decoder_norm'   )
      if "decoder_pred" in name:
            snake_case        =     name.replace('decoder_pred'	,     'decoder.decoder_pred'   )
      if "norm.weight" in name and "decoder" not in name and "fc" not in name:
            snake_case        =     name.replace('norm.weight'	,     'videomae.layernorm.weight'   )
      if "norm.bias" in name and "decoder" not in name and "fc" not in name:
            snake_case        =     name.replace('norm.bias'	,     'videomae.layernorm.bias'   )
      if "head" in name and "decoder" not in name:
            snake_case        =     name.replace('head'	,     'classifier'   )

      return name





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int   )   ->			Tuple:





      """simple docstring"""






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

            if key.startswith('encoder.'   ):
                  snake_case        =     key.replace('encoder.'	,     ''   )

            if "qkv" in key:
                  snake_case        =     key.split('.'   )
                  if key.startswith('decoder.blocks'   ):
                        snake_case        =     config.decoder_hidden_size
                        snake_case        =     int(key_split[2]   )
                        snake_case        =     """decoder.decoder_layers."""
                        if "weight" in key:
                              snake_case        =     val[:dim, :]
                              snake_case        =     val[dim : dim * 2, :]
                              snake_case        =     val[-dim:, :]
                  else:
                        snake_case        =     config.hidden_size
                        snake_case        =     int(key_split[1]   )
                        snake_case        =     """videomae.encoder.layer."""
                        if "weight" in key:
                              snake_case        =     val[:dim, :]
                              snake_case        =     val[dim : dim * 2, :]
                              snake_case        =     val[-dim:, :]
            else:
                  snake_case        =     val

      return orig_state_dict





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





      """simple docstring"""






      snake_case        =     hf_hub_download(
          repo_id='hf-internal-testing/spaghetti-video'	,     filename='eating_spaghetti.npy'	,     repo_type='dataset'   )
      snake_case        =     np.load(A__   )
      return list(A__   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			int   )   ->			Tuple:





      """simple docstring"""






      snake_case        =     get_videomae_config(A__   )

      if "finetuned" in model_name:
            snake_case        =     VideoMAEForVideoClassification(A__   )
      else:
            snake_case        =     VideoMAEForPreTraining(A__   )

      # download original checkpoint, hosted on Google Drive
      snake_case        =     """pytorch_model.bin"""
      gdown.cached_download(A__	,     A__	,     quiet=A__   )
      snake_case        =     torch.load(A__	,     map_location='cpu'   )
      if "model" in files:
            snake_case        =     files["""model"""]
      else:
            snake_case        =     files["""module"""]
      snake_case        =     convert_state_dict(A__	,     A__   )

      model.load_state_dict(A__   )
      model.eval()

      # verify model on basic input
      snake_case        =     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,     image_std=[0.5, 0.5, 0.5]   )
      snake_case        =     prepare_video()
      snake_case        =     image_processor(A__	,     return_tensors='pt'   )

      if "finetuned" not in model_name:
            snake_case        =     hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'	,     filename='bool_masked_pos.pt'   )
            snake_case        =     torch.load(A__   )

      snake_case        =     model(**A__   )
      snake_case        =     outputs.logits

      snake_case        =     [
          """videomae-small-finetuned-kinetics""",
          """videomae-small-finetuned-ssv2""",
          # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
          """videomae-base-short""",
          """videomae-base-short-finetuned-kinetics""",
          """videomae-base""",
          """videomae-base-finetuned-kinetics""",
          """videomae-large""",
          """videomae-large-finetuned-kinetics""",
          """videomae-huge-finetuned-kinetics""",
          # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
          """videomae-base-short-ssv2""",
          """videomae-base-short-finetuned-ssv2""",
          """videomae-base-ssv2""",
          """videomae-base-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 == "videomae-small-finetuned-kinetics":
            snake_case        =     torch.Size([1, 4_0_0]   )
            snake_case        =     torch.tensor([-0.92_91, -0.40_61, -0.93_07]   )
      elif model_name == "videomae-small-finetuned-ssv2":
            snake_case        =     torch.Size([1, 1_7_4]   )
            snake_case        =     torch.tensor([0.26_71, -0.46_89, -0.82_35]   )
      elif model_name == "videomae-base":
            snake_case        =     torch.Size([1, 1_4_0_8, 1_5_3_6]   )
            snake_case        =     torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]   )
      elif model_name == "videomae-base-short":
            snake_case        =     torch.Size([1, 1_4_0_8, 1_5_3_6]   )
            snake_case        =     torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]   )
            # we verified the loss both for normalized and unnormalized targets for this one
            snake_case        =     torch.tensor([0.51_42]   ) if config.norm_pix_loss else torch.tensor([0.64_69]   )
      elif model_name == "videomae-large":
            snake_case        =     torch.Size([1, 1_4_0_8, 1_5_3_6]   )
            snake_case        =     torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]   )
      elif model_name == "videomae-large-finetuned-kinetics":
            snake_case        =     torch.Size([1, 4_0_0]   )
            snake_case        =     torch.tensor([0.07_71, 0.00_11, -0.36_25]   )
      elif model_name == "videomae-huge-finetuned-kinetics":
            snake_case        =     torch.Size([1, 4_0_0]   )
            snake_case        =     torch.tensor([0.24_33, 0.16_32, -0.48_94]   )
      elif model_name == "videomae-base-short-finetuned-kinetics":
            snake_case        =     torch.Size([1, 4_0_0]   )
            snake_case        =     torch.tensor([0.65_88, 0.09_90, -0.24_93]   )
      elif model_name == "videomae-base-finetuned-kinetics":
            snake_case        =     torch.Size([1, 4_0_0]   )
            snake_case        =     torch.tensor([0.36_69, -0.06_88, -0.24_21]   )
      elif model_name == "videomae-base-short-ssv2":
            snake_case        =     torch.Size([1, 1_4_0_8, 1_5_3_6]   )
            snake_case        =     torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]   )
      elif model_name == "videomae-base-short-finetuned-ssv2":
            snake_case        =     torch.Size([1, 1_7_4]   )
            snake_case        =     torch.tensor([-0.05_37, -0.15_39, -0.32_66]   )
      elif model_name == "videomae-base-ssv2":
            snake_case        =     torch.Size([1, 1_4_0_8, 1_5_3_6]   )
            snake_case        =     torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]   )
      elif model_name == "videomae-base-finetuned-ssv2":
            snake_case        =     torch.Size([1, 1_7_4]   )
            snake_case        =     torch.tensor([0.19_61, -0.83_37, -0.63_89]   )
      else:
            raise ValueError(f"""Model name not supported. Should be one of {model_names}"""   )

      # verify logits
      assert logits.shape == expected_shape
      if "finetuned" in model_name:
            assert torch.allclose(logits[0, :3]	,     A__	,     atol=1e-4   )
      else:
            print('Logits:'	,     logits[0, :3, :3]   )
            assert torch.allclose(logits[0, :3, :3]	,     A__	,     atol=1e-4   )
      print('Logits ok!'   )

      # verify loss, if applicable
      if model_name == "videomae-base-short":
            snake_case        =     outputs.loss
            assert torch.allclose(A__	,     A__	,     atol=1e-4   )
            print('Loss 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(A__	,     organization='nielsr'   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4",
         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="/Users/nielsrogge/Documents/VideoMAE/Test",
         type=str,
         help="Path to the output PyTorch model directory.",
     )
     parser.add_argument("--model_name", default="videomae-base", 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."
     )

     SCREAMING_SNAKE_CASE__   =					parser.parse_args()
     convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


