





'''simple docstring'''






import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__UpperCamelCase     :   Optional[Any]										=  get_logger(__name__)


if TYPE_CHECKING:
	from .builder import DatasetBuilder







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase = None   )   ->							str:



			"""simple docstring"""


			__snake_case				:			Any       			=    importlib.import_module(_lowerCamelCase   )

			# TODO(QL): always update the module to add subsequent new authentication
			if hasattr(_lowerCamelCase						,  """_patched_for_streaming"""   ) and module._patched_for_streaming:
						return

			def wrap_auth(_lowerCamelCase   ):
						@wraps(_lowerCamelCase   )
						def wrapper(*_lowerCamelCase						,  **_lowerCamelCase   ):
									return function(*_lowerCamelCase						,  use_auth_token=_lowerCamelCase						,  **_lowerCamelCase   )

						__snake_case				:			Union[str, Any]       			=    """wrap_auth"""
						return wrapper

			# open files in a streaming fashion
			patch_submodule(_lowerCamelCase						,  """open"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """os.listdir"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """os.walk"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """glob.glob"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			# allow to navigate in remote zip files
			patch_submodule(_lowerCamelCase						,  """os.path.join"""						,  _lowerCamelCase   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.dirname"""						,  _lowerCamelCase   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.basename"""						,  _lowerCamelCase   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.relpath"""						,  _lowerCamelCase   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.split"""						,  _lowerCamelCase   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.splitext"""						,  _lowerCamelCase   ).start()
			# allow checks on paths
			patch_submodule(_lowerCamelCase						,  """os.path.exists"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.isdir"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.isfile"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """os.path.getsize"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """pathlib.Path"""						,  _lowerCamelCase   ).start()
			# file readers
			patch_submodule(_lowerCamelCase						,  """gzip.open"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """numpy.load"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """pandas.read_csv"""						,  wrap_auth(_lowerCamelCase   )						,  attrs=["""__version__"""]   ).start()
			patch_submodule(_lowerCamelCase						,  """pandas.read_excel"""						,  wrap_auth(_lowerCamelCase   )						,  attrs=["""__version__"""]   ).start()
			patch_submodule(_lowerCamelCase						,  """scipy.io.loadmat"""						,  wrap_auth(_lowerCamelCase   )						,  attrs=["""__version__"""]   ).start()
			patch_submodule(_lowerCamelCase						,  """xml.etree.ElementTree.parse"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			patch_submodule(_lowerCamelCase						,  """xml.dom.minidom.parse"""						,  wrap_auth(_lowerCamelCase   )   ).start()
			__snake_case				:			Any       			=    True







def 				_a	(					_lowerCamelCase   )   ->							str:



			"""simple docstring"""


			extend_module_for_streaming(builder.__module__						,  use_auth_token=builder.use_auth_token   )
			# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
			if not builder.__module__.startswith("""datasets."""   ):  # check that it's not a packaged builder like csv
						for imports in get_imports(inspect.getfile(builder.__class__   )   ):
									if imports[0] == "internal":
												__snake_case				:			Dict       			=    imports[1]
												__snake_case				:			Optional[int]       			=    """.""".join(builder.__module__.split("""."""   )[:-1] + [internal_import_name]   )
												extend_module_for_streaming(_lowerCamelCase						,  use_auth_token=builder.use_auth_token   )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
			from .builder import DatasetBuilder

			__snake_case				:			Any       			=    [
			    cls.__module__
			    for cls in type(_lowerCamelCase   ).__mro__[1:]  # make sure it's not the same module we've already patched
			    if issubclass(cls						,  _lowerCamelCase   ) and cls.__module__ != DatasetBuilder.__module__
			]  # check it's not a standard builder from datasets.builder
			for module in parent_builder_modules:
						extend_module_for_streaming(_lowerCamelCase						,  use_auth_token=builder.use_auth_token   )









'''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 				_a	(					_lowerCamelCase   )   ->							Optional[Any]:



			"""simple docstring"""


			__snake_case				:			str       			=    """https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

			with open(_lowerCamelCase						,  """wb"""   ) as f:
						with requests.get(_lowerCamelCase						,  stream=_lowerCamelCase   ) as req:
									for chunk in req.iter_content(chunk_size=2048   ):
												f.write(_lowerCamelCase   )







def 				_a	(					)   ->							VivitConfig:



			"""simple docstring"""


			__snake_case				:			str       			=    VivitConfig()

			__snake_case				:			int       			=    400
			__snake_case				:			str       			=    """huggingface/label-files"""
			__snake_case				:			Any       			=    """kinetics400-id2label.json"""

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







def 				_a	(					)   ->							Any:



			"""simple docstring"""


			__snake_case				:			Any       			=    hf_hub_download(
			    repo_id="""hf-internal-testing/spaghetti-video"""						,  filename="""eating_spaghetti_32_frames.npy"""						,  repo_type="""dataset"""   )
			__snake_case				:			Any       			=    np.load(_lowerCamelCase   )
			return list(_lowerCamelCase   )







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



			"""simple docstring"""


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

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

			else:
						raise Exception(F'''Invalid number of dimesions: {np.ndim(_lowerCamelCase   )}'''   )







def 				_a	(					_lowerCamelCase   )   ->							Any:



			"""simple docstring"""


			return current.flatten()







def 				_a	(					_lowerCamelCase   )   ->							Optional[int]:



			"""simple docstring"""


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







def 				_a	(					_lowerCamelCase   )   ->							int:



			"""simple docstring"""


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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							Dict:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'''encoderblock_{i}''']

			__snake_case				:			str       			=    F'''encoder.layer.{i}.'''
			__snake_case				:			Optional[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	(					_lowerCamelCase   )   ->							List[Any]:



			"""simple docstring"""


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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase=False   )   ->							str:



			"""simple docstring"""


			__snake_case				:			Optional[int]       			=    get_n_layers(_lowerCamelCase   )

			__snake_case				:			Optional[Any]       			=    OrderedDict()

			__snake_case				:			Tuple       			=    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
			__snake_case				:			str       			=    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

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

			__snake_case				:			Optional[int]       			=    state_dict["""optimizer"""]["""target"""]["""cls"""]
			__snake_case				:			Dict       			=    state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
			    """pos_embedding"""
			]

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

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

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







def 				_a	(					)   ->							VivitImageProcessor:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    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	(					_lowerCamelCase   )   ->							Optional[int]:



			"""simple docstring"""


			__snake_case				:			Optional[int]       			=    """checkpoint"""

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

			__snake_case				:			int       			=    restore_checkpoint(_lowerCamelCase						,  _lowerCamelCase   )
			__snake_case				:			Union[str, Any]       			=    transform_state(_lowerCamelCase						,  classification_head=_lowerCamelCase   )

			__snake_case				:			Optional[Any]       			=    get_vivit_config()

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

			__snake_case				:			List[Any]       			=    VivitForVideoClassification(_lowerCamelCase   )
			model.load_state_dict(_lowerCamelCase   )
			model.eval()

			__snake_case				:			int       			=    get_processor()

			__snake_case				:			int       			=    prepare_video()
			__snake_case				:			List[str]       			=    extractor(_lowerCamelCase						,  return_tensors="""pt"""   )

			__snake_case				:			Optional[int]       			=    model(**_lowerCamelCase   )

			__snake_case				:			List[Any]       			=    torch.Size([1, 400]   )
			__snake_case				:			Tuple       			=    torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]   )

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

			model.save_pretrained(_lowerCamelCase   )
			extractor.save_pretrained(_lowerCamelCase   )


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)


