
"""simple docstring"""






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 		_snake_case			(      lowercase__						:    Tuple						)    ->							List[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

			with open(UpperCamelCase__ ,	"""wb"""						) as f:
						with requests.get(UpperCamelCase__ ,	stream=UpperCamelCase__						) as req:
									for chunk in req.iter_content(chunk_size=2_0_4_8						):
												f.write(UpperCamelCase__						)






def 		_snake_case			(      )    ->							VivitConfig:


			'''simple docstring'''





			lowerCAmelCase_       :Optional[Any]     	=    VivitConfig()

			lowerCAmelCase_       :Tuple     	=    4_0_0
			lowerCAmelCase_       :Optional[Any]     	=    '''huggingface/label-files'''
			lowerCAmelCase_       :Optional[Any]     	=    '''kinetics400-id2label.json'''

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






def 		_snake_case			(      )    ->							Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_       :List[str]     	=    hf_hub_download(
			    repo_id="""hf-internal-testing/spaghetti-video""" ,	filename="""eating_spaghetti_32_frames.npy""" ,	repo_type="""dataset"""						)
			lowerCAmelCase_       :str     	=    np.load(UpperCamelCase__						)
			return list(UpperCamelCase__						)






def 		_snake_case			(      lowercase__						:    np.ndarray						)    ->							Dict:


			'''simple docstring'''





			if np.ndim(UpperCamelCase__						) == 2:
						return transform_attention_bias(UpperCamelCase__						)

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

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






def 		_snake_case			(      lowercase__						:    np.ndarray						)    ->							Union[str, Any]:


			'''simple docstring'''





			return current.flatten()






def 		_snake_case			(      lowercase__						:    np.ndarray						)    ->							Optional[int]:


			'''simple docstring'''





			return np.reshape(UpperCamelCase__ ,	(current.shape[0], current.shape[1] * current.shape[2])						).T






def 		_snake_case			(      lowercase__						:    np.ndarray						)    ->							Dict:


			'''simple docstring'''





			return np.reshape(UpperCamelCase__ ,	(current.shape[0] * current.shape[1], current.shape[2])						).T






def 		_snake_case			(      lowercase__						:    Optional[int] ,	lowercase__						:    Dict						)    ->							int:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

			lowerCAmelCase_       :Union[str, Any]     	=    f"""encoder.layer.{i}."""
			lowerCAmelCase_       :List[str]     	=    {
			    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 		_snake_case			(      lowercase__						:    Optional[int]						)    ->							Dict:


			'''simple docstring'''





			return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]						)






def 		_snake_case			(      lowercase__						:    str ,	lowercase__						:    Dict=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    get_n_layers(UpperCamelCase__						)

			lowerCAmelCase_       :Any     	=    OrderedDict()

			lowerCAmelCase_       :List[str]     	=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
			lowerCAmelCase_       :Tuple     	=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

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

			lowerCAmelCase_       :Any     	=    state_dict['''optimizer''']['''target''']['''cls''']
			lowerCAmelCase_       :List[str]     	=    state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
			    '''pos_embedding'''
			]

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

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

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






def 		_snake_case			(      )    ->							VivitImageProcessor:


			'''simple docstring'''





			lowerCAmelCase_       :List[Any]     	=    VivitImageProcessor()

			assert extractor.do_resize is True
			assert extractor.size == {"shortest_edge": 2_5_6}
			assert extractor.do_center_crop is True
			assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
			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 / 2_5_5

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

			return extractor






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


			'''simple docstring'''





			lowerCAmelCase_       :int     	=    '''checkpoint'''

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

			lowerCAmelCase_       :Dict     	=    restore_checkpoint(UpperCamelCase__ ,	UpperCamelCase__						)
			lowerCAmelCase_       :Tuple     	=    transform_state(UpperCamelCase__ ,	classification_head=UpperCamelCase__						)

			lowerCAmelCase_       :Any     	=    get_vivit_config()

			assert config.image_size == 2_2_4
			assert config.num_frames == 3_2

			lowerCAmelCase_       :Any     	=    VivitForVideoClassification(UpperCamelCase__						)
			model.load_state_dict(UpperCamelCase__						)
			model.eval()

			lowerCAmelCase_       :Optional[int]     	=    get_processor()

			lowerCAmelCase_       :int     	=    prepare_video()
			lowerCAmelCase_       :List[Any]     	=    extractor(UpperCamelCase__ ,	return_tensors="""pt"""						)

			lowerCAmelCase_       :Any     	=    model(**UpperCamelCase__						)

			lowerCAmelCase_       :List[Any]     	=    torch.Size([1, 4_0_0]						)
			lowerCAmelCase_       :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] ,	UpperCamelCase__ ,	atol=1E-4						), outputs.logits[0, :5]

			model.save_pretrained(UpperCamelCase__						)
			extractor.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
				__UpperCAmelCase 					=    argparse.ArgumentParser()

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

				__UpperCAmelCase 					=    parser.parse_args()
				convert(args.output_model_name)













'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class 	_lowercase    (				a					):
   _UpperCamelCase									= """mobilevit"""


   def __init__(			self     , _UpperCAmelCase=3     , _UpperCAmelCase=256     , _UpperCAmelCase=2     , _UpperCAmelCase=[144, 192, 240]     , _UpperCAmelCase=[16, 32, 64, 96, 128, 160, 640]     , _UpperCAmelCase=4     , _UpperCAmelCase=2.0     , _UpperCAmelCase=4.0     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.0     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=1E-5     , _UpperCAmelCase=True     , _UpperCAmelCase=256     , _UpperCAmelCase=[6, 12, 18]     , _UpperCAmelCase=0.1     , _UpperCAmelCase=255     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Union[str, Any]				=    num_channels
       A							:							Union[str, Any]				=    image_size
       A							:							List[str]				=    patch_size
       A							:							Any				=    hidden_sizes
       A							:							Dict				=    neck_hidden_sizes
       A							:							int				=    num_attention_heads
       A							:							List[str]				=    mlp_ratio
       A							:							List[Any]				=    expand_ratio
       A							:							Dict				=    hidden_act
       A							:							List[Any]				=    conv_kernel_size
       A							:							List[str]				=    output_stride
       A							:							Dict				=    hidden_dropout_prob
       A							:							List[str]				=    attention_probs_dropout_prob
       A							:							Optional[Any]				=    classifier_dropout_prob
       A							:							Any				=    initializer_range
       A							:							Union[str, Any]				=    layer_norm_eps
       A							:							Union[str, Any]				=    qkv_bias

       # decode head attributes for semantic segmentation
       A							:							int				=    aspp_out_channels
       A							:							Any				=    atrous_rates
       A							:							Tuple				=    aspp_dropout_prob
       A							:							List[Any]				=    semantic_loss_ignore_index







class 	_lowercase    (				a					):
   _UpperCamelCase									= version.parse("""1.11"""					)


   @property
   def        snake_case					(			self       ):
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]       )


   @property
   def        snake_case					(			self       ):
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]       )
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]       )


   @property
   def        snake_case					(			self       ):
       return 1E-4
