
"""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 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       """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 				lowercase      ():
						__lowerCAmelCase        =       VivitConfig()

						__lowerCAmelCase        =       400
						__lowerCAmelCase        =       """huggingface/label-files"""
						__lowerCAmelCase        =       """kinetics400-id2label.json"""

						__lowerCAmelCase        =       json.load(open(hf_hub_download(_lowerCAmelCase				,  _lowerCAmelCase				,  repo_type="""dataset"""		)				,  """r"""		)		)
						__lowerCAmelCase        =       {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
						__lowerCAmelCase        =       idalabel
						__lowerCAmelCase        =       {v: k for k, v in idalabel.items()}
						return config

def 				lowercase      ():
						__lowerCAmelCase        =       hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""				,  filename="""eating_spaghetti_32_frames.npy"""				,  repo_type="""dataset"""		)
						__lowerCAmelCase        =       np.load(_lowerCAmelCase		)
						return list(_lowerCAmelCase		)

def 				lowercase      (_lowerCAmelCase		):
						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 				lowercase      (_lowerCAmelCase		):
						return current.flatten()

def 				lowercase      (_lowerCAmelCase		):
						return np.reshape(_lowerCAmelCase				,  (current.shape[0], current.shape[1] * current.shape[2])		).T

def 				lowercase      (_lowerCAmelCase		):
						return np.reshape(_lowerCAmelCase				,  (current.shape[0] * current.shape[1], current.shape[2])		).T

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       state_dict["""optimizer"""]["""target"""]["""Transformer"""][f"""encoderblock_{i}"""]

						__lowerCAmelCase        =       f"""encoder.layer.{i}."""
						__lowerCAmelCase        =       {
						    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 				lowercase      (_lowerCAmelCase		):
						return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]		)

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase=False		):
						__lowerCAmelCase        =       get_n_layers(_lowerCAmelCase		)

						__lowerCAmelCase        =       OrderedDict()

						__lowerCAmelCase        =       state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
						__lowerCAmelCase        =       state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

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

						__lowerCAmelCase        =       state_dict["""optimizer"""]["""target"""]["""cls"""]
						__lowerCAmelCase        =       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:
												__lowerCAmelCase        =       {"""vivit.""" + k: v for k, v in new_state.items()}
												__lowerCAmelCase        =       np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""kernel"""]		)
												__lowerCAmelCase        =       np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""bias"""]		)

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

def 				lowercase      ():
						__lowerCAmelCase        =       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 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       """checkpoint"""

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

						__lowerCAmelCase        =       restore_checkpoint(_lowerCAmelCase				,  _lowerCAmelCase		)
						__lowerCAmelCase        =       transform_state(_lowerCAmelCase				,  classification_head=_lowerCAmelCase		)

						__lowerCAmelCase        =       get_vivit_config()

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

						__lowerCAmelCase        =       VivitForVideoClassification(_lowerCAmelCase		)
						model.load_state_dict(_lowerCAmelCase		)
						model.eval()

						__lowerCAmelCase        =       get_processor()

						__lowerCAmelCase        =       prepare_video()
						__lowerCAmelCase        =       extractor(_lowerCAmelCase				,  return_tensors="""pt"""		)

						__lowerCAmelCase        =       model(**_lowerCAmelCase		)

						__lowerCAmelCase        =       torch.Size([1, 400]		)
						__lowerCAmelCase        =       torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]		)

						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__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

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

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


"""simple docstring"""
#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class      lowerCAmelCase_						:



							'''simple docstring'''





							_snake_case		     =						'''block_{0}.dat'''




							def __init__(				self  ,							snake_case_							)   ->       int:
													__lowerCAmelCase        =       filename
													__lowerCAmelCase        =       []




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Tuple:
													__lowerCAmelCase        =       self.BLOCK_FILENAME_FORMAT.format(snake_case_							)
													with open(snake_case_  ,							"""w"""							) as file:
																			file.write(snake_case_							)
													self.block_filenames.append(snake_case_							)




							def  A__		(				self							)   ->       Union[str, Any]:
													return self.block_filenames




							def  A__		(				self  ,							snake_case_  ,							snake_case_=None							)   ->       Optional[Any]:
													__lowerCAmelCase        =       0
													with open(self.filename							) as file:
																			while True:
																									__lowerCAmelCase        =       file.readlines(snake_case_							)

																									if lines == []:
																															break

																									if sort_key is None:
																															lines.sort()
																									else:
																															lines.sort(key=snake_case_							)

																									self.write_block("""""".join(snake_case_							)  ,							snake_case_							)
																									i += 1




							def  A__		(				self							)   ->       Optional[Any]:
													map(os.remove  ,							self.block_filenames							)
class      lowerCAmelCase_						:



							'''simple docstring'''





							def  A__		(				self  ,							snake_case_							)   ->       List[str]:
													__lowerCAmelCase        =       -1
													__lowerCAmelCase        =       None

													for i in range(len(snake_case_							)							):
																			if min_str is None or choices[i] < min_str:
																									__lowerCAmelCase        =       i

													return min_index
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       Any:
													__lowerCAmelCase        =       files
													__lowerCAmelCase        =       set()
													__lowerCAmelCase        =       len(snake_case_							)
													__lowerCAmelCase        =       {i: None for i in range(self.num_buffers							)}




							def  A__		(				self							)   ->       str:
													return {
													    i: self.buffers[i] for i in range(self.num_buffers							) if i not in self.empty
													}




							def  A__		(				self							)   ->       Optional[Any]:
													for i in range(self.num_buffers							):
																			if self.buffers[i] is None and i not in self.empty:
																									__lowerCAmelCase        =       self.files[i].readline()

																									if self.buffers[i] == "":
																															self.empty.add(snake_case_							)
																															self.files[i].close()

													if len(self.empty							) == self.num_buffers:
																			return False

													return True




							def  A__		(				self  ,							snake_case_							)   ->       Optional[Any]:
													__lowerCAmelCase        =       self.buffers[index]
													__lowerCAmelCase        =       None

													return value
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       merge_strategy




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       FilesArray(self.get_file_handles(snake_case_  ,							snake_case_							)							)
													with open(snake_case_  ,							"""w"""  ,							snake_case_							) as outfile:
																			while buffers.refresh():
																									__lowerCAmelCase        =       self.merge_strategy.select(buffers.get_dict()							)
																									outfile.write(buffers.unshift(snake_case_							)							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       Optional[int]:
													__lowerCAmelCase        =       {}

													for i in range(len(snake_case_							)							):
																			__lowerCAmelCase        =       open(filenames[i]  ,							"""r"""  ,							snake_case_							)  # noqa: UP015

													return files
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       block_size




							def  A__		(				self  ,							snake_case_  ,							snake_case_=None							)   ->       Any:
													__lowerCAmelCase        =       self.get_number_blocks(snake_case_  ,							self.block_size							)
													__lowerCAmelCase        =       FileSplitter(snake_case_							)
													splitter.split(self.block_size  ,							snake_case_							)

													__lowerCAmelCase        =       FileMerger(NWayMerge()							)
													__lowerCAmelCase        =       self.block_size / (num_blocks + 1)
													merger.merge(splitter.get_block_filenames()  ,							filename + """.out"""  ,							snake_case_							)

													splitter.cleanup()




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       int:
													return (os.stat(snake_case_							).st_size / block_size) + 1

def 				lowercase      (_lowerCAmelCase		):
						if string[-1].lower() == "k":
												return int(string[:-1]		) * 1024
						elif string[-1].lower() == "m":
												return int(string[:-1]		) * 1024 * 1024
						elif string[-1].lower() == "g":
												return int(string[:-1]		) * 1024 * 1024 * 1024
						else:
												return int(_lowerCAmelCase		)

def 				lowercase      ():
						__lowerCAmelCase        =       argparse.ArgumentParser()
						parser.add_argument(
						    """-m"""				,  """--mem"""				,  help="""amount of memory to use for sorting"""				,  default="""100M"""		)
						parser.add_argument(
						    """filename"""				,  metavar="""<filename>"""				,  nargs=1				,  help="""name of file to sort"""		)
						__lowerCAmelCase        =       parser.parse_args()

						__lowerCAmelCase        =       ExternalSort(parse_memory(args.mem		)		)
						sorter.sort(args.filename[0]		)


if __name__ == "__main__":
			main()
