
import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  Optional[int] 			=   """https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

       with open(SCREAMING_SNAKE_CASE__      ,			"""wb"""       ) as f:
              with requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ) as req:
                     for chunk in req.iter_content(chunk_size=2_048       ):
                            f.write(SCREAMING_SNAKE_CASE__       )







def 		a(						)				->					VivitConfig:
       lowercase							:  Optional[Any] 			=   VivitConfig()

       lowercase							:  Tuple 			=   400
       lowercase							:  int 			=   """huggingface/label-files"""
       lowercase							:  Any 			=   """kinetics400-id2label.json"""

       lowercase							:  Optional[int] 			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			repo_type="""dataset"""       )      ,			"""r"""       )       )
       lowercase							:  Dict 			=   {int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
       lowercase							:  Optional[Any] 			=   idalabel
       lowercase							:  Any 			=   {v: k for k, v in idalabel.items()}
       return config







def 		a(						)				->					Tuple:
       lowercase							:  int 			=   hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""      ,			filename="""eating_spaghetti_32_frames.npy"""      ,			repo_type="""dataset"""       )
       lowercase							:  int 			=   np.load(SCREAMING_SNAKE_CASE__       )
       return list(SCREAMING_SNAKE_CASE__       )







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       if np.ndim(SCREAMING_SNAKE_CASE__       ) == 2:
              return transform_attention_bias(SCREAMING_SNAKE_CASE__       )

       elif np.ndim(SCREAMING_SNAKE_CASE__       ) == 3:
              return transform_attention_kernel(SCREAMING_SNAKE_CASE__       )

       else:
              raise Exception(f"Invalid number of dimesions: {np.ndim(SCREAMING_SNAKE_CASE__       )}"       )







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Any:
       return current.flatten()







def 		a(						SCREAMING_SNAKE_CASE__       )				->					List[str]:
       return np.reshape(SCREAMING_SNAKE_CASE__      ,			(current.shape[0], current.shape[1] * current.shape[2])       ).T







def 		a(						SCREAMING_SNAKE_CASE__       )				->					int:
       return np.reshape(SCREAMING_SNAKE_CASE__      ,			(current.shape[0] * current.shape[1], current.shape[2])       ).T







def 		a(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""][f"encoderblock_{i}"]

       lowercase							:  List[Any] 			=   f"encoder.layer.{i}."
       lowercase							:  Any 			=   {
           prefix + """intermediate.dense.bias""": state["""MlpBlock_0"""]["""Dense_0"""]["""bias"""],
           prefix + """intermediate.dense.weight""": np.transpose(state["""MlpBlock_0"""]["""Dense_0"""]["""kernel"""]       ),
           prefix + """output.dense.bias""": state["""MlpBlock_0"""]["""Dense_1"""]["""bias"""],
           prefix + """output.dense.weight""": np.transpose(state["""MlpBlock_0"""]["""Dense_1"""]["""kernel"""]       ),
           prefix + """layernorm_before.bias""": state["""LayerNorm_0"""]["""bias"""],
           prefix + """layernorm_before.weight""": state["""LayerNorm_0"""]["""scale"""],
           prefix + """layernorm_after.bias""": state["""LayerNorm_1"""]["""bias"""],
           prefix + """layernorm_after.weight""": state["""LayerNorm_1"""]["""scale"""],
           prefix
           + """attention.attention.query.bias""": transform_attention(
               state["""MultiHeadDotProductAttention_0"""]["""query"""]["""bias"""]       ),
           prefix
           + """attention.attention.query.weight""": transform_attention(
               state["""MultiHeadDotProductAttention_0"""]["""query"""]["""kernel"""]       ),
           prefix
           + """attention.attention.key.bias""": transform_attention(state["""MultiHeadDotProductAttention_0"""]["""key"""]["""bias"""]       ),
           prefix
           + """attention.attention.key.weight""": transform_attention(
               state["""MultiHeadDotProductAttention_0"""]["""key"""]["""kernel"""]       ),
           prefix
           + """attention.attention.value.bias""": transform_attention(
               state["""MultiHeadDotProductAttention_0"""]["""value"""]["""bias"""]       ),
           prefix
           + """attention.attention.value.weight""": transform_attention(
               state["""MultiHeadDotProductAttention_0"""]["""value"""]["""kernel"""]       ),
           prefix + """attention.output.dense.bias""": state["""MultiHeadDotProductAttention_0"""]["""out"""]["""bias"""],
           prefix
           + """attention.output.dense.weight""": transform_attention_output_weight(
               state["""MultiHeadDotProductAttention_0"""]["""out"""]["""kernel"""]       ),
       }

       return new_state







def 		a(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]       )







def 		a(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=False       )				->					List[Any]:
       lowercase							:  List[str] 			=   get_n_layers(SCREAMING_SNAKE_CASE__       )

       lowercase							:  Any 			=   OrderedDict()

       lowercase							:  int 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
       lowercase							:  Optional[Any] 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

       lowercase							:  Optional[int] 			=   np.transpose(
           state_dict["""optimizer"""]["""target"""]["""embedding"""]["""kernel"""]      ,			(4, 3, 0, 1, 2)       )
       lowercase							:  Optional[int] 			=   state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

       lowercase							:  Dict 			=   state_dict["""optimizer"""]["""target"""]["""cls"""]
       lowercase							:  str 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
           """pos_embedding"""
       ]

       for i in range(SCREAMING_SNAKE_CASE__       ):
              new_state.update(transform_state_encoder_block(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )       )

       if classification_head:
              lowercase							:  List[str] 			=   {"""vivit.""" + k: v for k, v in new_state.items()}
              lowercase							:  List[Any] 			=   np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""kernel"""]       )
              lowercase							:  int 			=   np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""bias"""]       )

       return {k: torch.tensor(SCREAMING_SNAKE_CASE__       ) for k, v in new_state.items()}







def 		a(						)				->					VivitImageProcessor:
       lowercase							:  Tuple 			=   VivitImageProcessor()

       assert extractor.do_resize is True
       assert extractor.size == {"shortest_edge": 256}
       assert extractor.do_center_crop is True
       assert extractor.crop_size == {"width": 224, "height": 224}
       assert extractor.resample == PILImageResampling.BILINEAR

       # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
       # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
       # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
       assert extractor.do_normalize is False
       assert extractor.do_rescale is True
       assert extractor.rescale_factor == 1 / 255

       # zero-centering = True in original implementation
       assert extractor.do_zero_centering is True

       return extractor







def 		a(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       lowercase							:  Optional[int] 			=   """checkpoint"""

       if not os.path.exists(SCREAMING_SNAKE_CASE__       ):
              download_checkpoint(SCREAMING_SNAKE_CASE__       )

       lowercase							:  List[Any] 			=   restore_checkpoint(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  List[Any] 			=   transform_state(SCREAMING_SNAKE_CASE__      ,			classification_head=SCREAMING_SNAKE_CASE__       )

       lowercase							:  str 			=   get_vivit_config()

       assert config.image_size == 224
       assert config.num_frames == 32

       lowercase							:  Tuple 			=   VivitForVideoClassification(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()

       lowercase							:  Any 			=   get_processor()

       lowercase							:  Optional[int] 			=   prepare_video()
       lowercase							:  str 			=   extractor(SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )

       lowercase							:  str 			=   model(**SCREAMING_SNAKE_CASE__       )

       lowercase							:  Optional[Any] 			=   torch.Size([1, 400]       )
       lowercase							:  List[Any] 			=   torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       )

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :5]      ,			SCREAMING_SNAKE_CASE__      ,			atol=1e-4       ), outputs.logits[0, :5]

       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
       extractor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  lowercase						:      Tuple							=							argparse.ArgumentParser()

  parser.add_argument("""--output_model_name""", """-o""", type=str, help="""Output path for the converted HuggingFace model""")

  lowercase						:      Tuple							=							parser.parse_args()
  convert(args.output_model_name)


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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           return self.image_processor_tester.prepare_image_processor_dict()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(snake_case	,"""image_mean"""   )   )
           self.assertTrue(hasattr(snake_case	,"""image_std"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_normalize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_resize"""   )   )
           self.assertTrue(hasattr(snake_case	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  Any 			=   self.image_processing_class.from_dict(self.image_processor_dict	,size=42	,crop_size=84   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 42}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 84, """width""": 84}   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
