
"""simple docstring"""


import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_lowercase											=			logging.get_logger(__name__)  # pylint: disable=invalid-name


_lowercase											=			'''lfs-multipart-upload'''







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : ArgumentParser )       ->				Optional[int]:
							A  				=				parser.add_parser(
							    'lfs-enable-largefiles'					,help=(
							        'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
							    )					,)
							enable_parser.add_argument('path'					,type=A_					,help='Local path to repository you want to configure.' )
							enable_parser.set_defaults(func=lambda A_ : LfsEnableCommand(A_ ) )

							A  				=				parser.add_parser(
							    A_					,help=(
							        'Deprecated: use `huggingface-cli` instead. '
							        'Command will get called by git-lfs, do not call it directly.'
							    )					,)
							upload_parser.set_defaults(func=lambda A_ : LfsUploadCommand(A_ ) )







class 			lowerCAmelCase_			:



						'''simple docstring'''


						def __init__(	self       : Dict					,A_       : Any )       ->				List[str]:
							A  				=				args




						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[str]:
							warnings.warn(
							    'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.' )
							A  				=				os.path.abspath(self.args.path )
							if not os.path.isdir(A_ ):
								print('This does not look like a valid git repo.' )
								exit(1 )
							subprocess.run(
							    'git config lfs.customtransfer.multipart.path transformers-cli'.split()					,check=A_					,cwd=A_ )
							subprocess.run(
							    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()					,check=A_					,cwd=A_					,)
							print('Local repo set up for largefiles' )






def  _snake_case			(						snake_case__				:       Dict       ):
	A  				=				json.dumps(snake_case__       ) + '\n'
	sys.stdout.write(snake_case__       )
	sys.stdout.flush()






def  _snake_case			(						):
	A  				=				json.loads(sys.stdin.readline().strip()       )

	if "terminate" in (msg.get('type'       ), msg.get('event'       )):
		# terminate message received
		return None

	if msg.get('event'       ) not in ("download", "upload"):
		logger.critical('Received unexpected message'       )
		sys.exit(1       )

	return msg







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						def __init__(	self       : List[Any]					,A_       : str					,A_       : int					,A_       : int )       ->				List[str]:
							A  				=				filepath
							A  				=				seek_from
							A  				=				read_limit
							A  				=				0


						def __enter__(	self       : List[str] )       ->				Dict:
							A  				=				open(self.filepath					,'rb' )
							self.f.seek(self.seek_from )
							return self


						def __len__(	self       : Union[str, Any] )       ->				List[Any]:
							A  				=				os.fstat(self.f.fileno() ).st_size
							return min(self.read_limit					,total_length - self.seek_from )


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : int=-1 )       ->				List[Any]:
							if self.n_seen >= self.read_limit:
								return b""
							A  				=				self.read_limit - self.n_seen
							A  				=				self.f.read(remaining_amount if n < 0 else min(A_					,A_ ) )
							self.n_seen += len(A_ )
							return data


						def __iter__(	self       : str )       ->				str:
							yield self.read(n=4 * 1024 * 1024 )




						def __exit__(	self       : str					,*A_       : Union[str, Any] )       ->				List[str]:
							self.f.close()







class 			lowerCAmelCase_			:



						'''simple docstring'''


						def __init__(	self       : List[str]					,A_       : List[str] )       ->				List[Any]:
							A  				=				args




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				int:
							# Immediately after invoking a custom transfer process, git-lfs
							# sends initiation data to the process over stdin.
							# This tells the process useful information about the configuration.
							A  				=				json.loads(sys.stdin.readline().strip() )
							if not (init_msg.get('event' ) == "init" and init_msg.get('operation' ) == "upload"):
								write_msg({'error': {'code': 32, 'message': 'Wrong lfs init operation'}} )
								sys.exit(1 )

							# The transfer process should use the information it needs from the
							# initiation structure, and also perform any one-off setup tasks it
							# needs to do. It should then respond on stdout with a simple empty
							# confirmation structure, as follows:
							write_msg({} )

							# After the initiation exchange, git-lfs will send any number of
							# transfer requests to the stdin of the transfer process, in a serial sequence.
							while True:
								A  				=				read_msg()
								if msg is None:
									# When all transfers have been processed, git-lfs will send
									# a terminate event to the stdin of the transfer process.
									# On receiving this message the transfer process should
									# clean up and terminate. No response is expected.
									sys.exit(0 )

								A  				=				msg['oid']
								A  				=				msg['path']
								A  				=				msg['action']['href']
								A  				=				msg['action']['header']
								A  				=				int(header.pop('chunk_size' ) )
								A  				=				list(header.values() )

								A  				=				[]
								for i, presigned_url in enumerate(A_ ):
									with FileSlice(A_					,seek_from=i * chunk_size					,read_limit=A_ ) as data:
										A  				=				requests.put(A_					,data=A_ )
										r.raise_for_status()
										parts.append(
										    {
										        'etag': r.headers.get('etag' ),
										        'partNumber': i + 1,
										    } )
										# In order to support progress reporting while data is uploading / downloading,
										# the transfer process should post messages to stdout
										write_msg(
										    {
										        'event': 'progress',
										        'oid': oid,
										        'bytesSoFar': (i + 1) * chunk_size,
										        'bytesSinceLast': chunk_size,
										    } )
										# Not precise but that's ok.

								A  				=				requests.post(
								    A_					,json={
								        'oid': oid,
								        'parts': parts,
								    }					,)
								r.raise_for_status()

								write_msg({'event': 'complete', 'oid': oid} )

"""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			(						snake_case__				:       Tuple       ):
	A  				=				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

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






def  _snake_case			(						):
	A  				=				VivitConfig()

	A  				=				400
	A  				=				'huggingface/label-files'
	A  				=				'kinetics400-id2label.json'

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






def  _snake_case			(						):
	A  				=				hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'		, filename='eating_spaghetti_32_frames.npy'		, repo_type='dataset'       )
	A  				=				np.load(snake_case__       )
	return list(snake_case__       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	if np.ndim(snake_case__       ) == 2:
		return transform_attention_bias(snake_case__       )

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

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






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return current.flatten()






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0], current.shape[1] * current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0] * current.shape[1], current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       List[str]       ):
	A  				=				state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

	A  				=				F'encoder.layer.{i}.'
	A  				=				{
	    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			(						snake_case__				:       Dict       ):
	return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False       ):
	A  				=				get_n_layers(snake_case__       )

	A  				=				OrderedDict()

	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

	A  				=				state_dict['optimizer']['target']['cls']
	A  				=				state_dict['optimizer']['target']['Transformer']['posembed_input'][
	    'pos_embedding'
	]

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

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

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






def  _snake_case			(						):
	A  				=				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  _snake_case			(						snake_case__				:       str       ):
	A  				=				'checkpoint'

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

	A  				=				restore_checkpoint(snake_case__		, snake_case__       )
	A  				=				transform_state(snake_case__		, classification_head=snake_case__       )

	A  				=				get_vivit_config()

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

	A  				=				VivitForVideoClassification(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	A  				=				get_processor()

	A  				=				prepare_video()
	A  				=				extractor(snake_case__		, return_tensors='pt'       )

	A  				=				model(**snake_case__       )

	A  				=				torch.Size([1, 400]       )
	A  				=				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]		, snake_case__		, atol=1e-4       ), outputs.logits[0, :5]

	model.save_pretrained(snake_case__       )
	extractor.save_pretrained(snake_case__       )


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

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

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