





'''simple docstring'''






import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
	import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME




class    _A   (				__lowercase		):

		def __init__(      self				:      Union[str, Any] )				->						Optional[Any]:


					"""simple docstring"""

					self.register_to_config()
					super().__init__()

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      int     ,			__magic_name__				:      Optional[Any] )				->						Tuple:


					"""simple docstring"""

					if dicta == dicta:
								return True
					else:
								__snake_case			,    __snake_case				:			Union[str, Any]       			=    self._remove_meta_keys(__magic_name__ )
								__snake_case			,    __snake_case				:			Dict       			=    self._remove_meta_keys(__magic_name__ )
								if configa == configa:
											print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.''' )
											return True
					return False

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      Dict )				->						Union[str, Any]:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    []
					__snake_case				:			int       			=    config_dict.copy()
					for key in config_dict.keys():
								if key.startswith("""_""" ):
											temp_dict.pop(__magic_name__ )
											meta_keys.append(__magic_name__ )
					return (temp_dict, meta_keys)

		@torch.no_grad()
		def   lowercase__    (      self				:      int     ,			__magic_name__				:      List[Union[str, os.PathLike]]     ,			**__magic_name__				:      str )				->						Union[str, Any]:


					"""simple docstring"""

					__snake_case				:			str       			=    kwargs.pop("""cache_dir"""     ,			__magic_name__ )
					__snake_case				:			Tuple       			=    kwargs.pop("""resume_download"""     ,			__magic_name__ )
					__snake_case				:			int       			=    kwargs.pop("""force_download"""     ,			__magic_name__ )
					__snake_case				:			str       			=    kwargs.pop("""proxies"""     ,			__magic_name__ )
					__snake_case				:			List[Any]       			=    kwargs.pop("""local_files_only"""     ,			__magic_name__ )
					__snake_case				:			int       			=    kwargs.pop("""use_auth_token"""     ,			__magic_name__ )
					__snake_case				:			Any       			=    kwargs.pop("""revision"""     ,			__magic_name__ )
					__snake_case				:			Any       			=    kwargs.pop("""torch_dtype"""     ,			__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    kwargs.pop("""device_map"""     ,			__magic_name__ )

					__snake_case				:			List[Any]       			=    kwargs.pop("""alpha"""     ,			0.5 )
					__snake_case				:			Optional[int]       			=    kwargs.pop("""interp"""     ,			__magic_name__ )

					print("""Received list"""     ,			__magic_name__ )
					print(f'''Combining with alpha={alpha}, interpolation mode={interp}''' )

					__snake_case				:			int       			=    len(__magic_name__ )
					# Ignore result from model_index_json comparision of the two checkpoints
					__snake_case				:			Any       			=    kwargs.pop("""force"""     ,			__magic_name__ )

					# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
					if checkpoint_count > 3 or checkpoint_count < 2:
								raise ValueError(
								    """Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"""
								    """ passed.""" )

					print("""Received the right number of checkpoints""" )
					# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
					# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

					# Validate that the checkpoints can be merged
					# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
					__snake_case				:			List[Any]       			=    []
					for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
								__snake_case				:			Any       			=    DiffusionPipeline.load_config(
								    __magic_name__     ,			cache_dir=__magic_name__     ,			resume_download=__magic_name__     ,			force_download=__magic_name__     ,			proxies=__magic_name__     ,			local_files_only=__magic_name__     ,			use_auth_token=__magic_name__     ,			revision=__magic_name__     ,			)
								config_dicts.append(__magic_name__ )

					__snake_case				:			Dict       			=    True
					for idx in range(1     ,			len(__magic_name__ ) ):
								comparison_result &= self._compare_model_configs(config_dicts[idx - 1]     ,			config_dicts[idx] )
								if not force and comparison_result is False:
											raise ValueError("""Incompatible checkpoints. Please check model_index.json for the models.""" )
											print(config_dicts[0]     ,			config_dicts[1] )
					print("""Compatible model_index.json files found""" )
					# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
					__snake_case				:			List[str]       			=    []
					for pretrained_model_name_or_path, config_dict in zip(__magic_name__     ,			__magic_name__ ):
								__snake_case				:			List[Any]       			=    [k for k in config_dict.keys() if not k.startswith("""_""" )]
								__snake_case				:			int       			=    [os.path.join(__magic_name__     ,			"""*""" ) for k in folder_names]
								allow_patterns += [
								    WEIGHTS_NAME,
								    SCHEDULER_CONFIG_NAME,
								    CONFIG_NAME,
								    ONNX_WEIGHTS_NAME,
								    DiffusionPipeline.config_name,
								]
								__snake_case				:			str       			=    config_dict.get("""_class_name""" )
								__snake_case				:			Dict       			=    {"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}

								__snake_case				:			List[str]       			=    (
								    pretrained_model_name_or_path
								    if os.path.isdir(__magic_name__ )
								    else snapshot_download(
								        __magic_name__     ,			cache_dir=__magic_name__     ,			resume_download=__magic_name__     ,			proxies=__magic_name__     ,			local_files_only=__magic_name__     ,			use_auth_token=__magic_name__     ,			revision=__magic_name__     ,			allow_patterns=__magic_name__     ,			user_agent=__magic_name__     ,			)
								)
								print("""Cached Folder"""     ,			__magic_name__ )
								cached_folders.append(__magic_name__ )

					# Step 3:-
					# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
					__snake_case				:			Optional[int]       			=    DiffusionPipeline.from_pretrained(
					    cached_folders[0]     ,			torch_dtype=__magic_name__     ,			device_map=__magic_name__ )
					final_pipe.to(self.device )

					__snake_case				:			Optional[Any]       			=    None
					if len(__magic_name__ ) > 2:
								__snake_case				:			Dict       			=    os.path.join(cached_folders[2] )

					if interp == "sigmoid":
								__snake_case				:			Dict       			=    CheckpointMergerPipeline.sigmoid
					elif interp == "inv_sigmoid":
								__snake_case				:			Optional[Any]       			=    CheckpointMergerPipeline.inv_sigmoid
					elif interp == "add_diff":
								__snake_case				:			Union[str, Any]       			=    CheckpointMergerPipeline.add_difference
					else:
								__snake_case				:			Dict       			=    CheckpointMergerPipeline.weighted_sum

					# Find each module's state dict.
					for attr in final_pipe.config.keys():
								if not attr.startswith("""_""" ):
											__snake_case				:			List[Any]       			=    os.path.join(cached_folders[1]     ,			__magic_name__ )
											if os.path.exists(__magic_name__ ):
														__snake_case				:			List[str]       			=    [
														    *glob.glob(os.path.join(__magic_name__     ,			"""*.safetensors""" ) ),
														    *glob.glob(os.path.join(__magic_name__     ,			"""*.bin""" ) ),
														]
														__snake_case				:			List[str]       			=    files[0] if len(__magic_name__ ) > 0 else None
											if len(__magic_name__ ) < 3:
														__snake_case				:			Optional[int]       			=    None
											else:
														__snake_case				:			Union[str, Any]       			=    os.path.join(cached_folders[2]     ,			__magic_name__ )
														if os.path.exists(__magic_name__ ):
																	__snake_case				:			Tuple       			=    [
																	    *glob.glob(os.path.join(__magic_name__     ,			"""*.safetensors""" ) ),
																	    *glob.glob(os.path.join(__magic_name__     ,			"""*.bin""" ) ),
																	]
																	__snake_case				:			List[Any]       			=    files[0] if len(__magic_name__ ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
											if checkpoint_path_a is None and checkpoint_path_a is None:
														print(f'''Skipping {attr}: not present in 2nd or 3d model''' )
														continue
											try:
														__snake_case				:			Dict       			=    getattr(__magic_name__     ,			__magic_name__ )
														if isinstance(__magic_name__     ,			__magic_name__ ):  # ignore requires_safety_checker boolean
																	continue
														__snake_case				:			List[str]       			=    getattr(__magic_name__     ,			"""state_dict""" )
														__snake_case				:			Tuple       			=    theta_a()

														__snake_case				:			Any       			=    getattr(__magic_name__     ,			"""load_state_dict""" )
														__snake_case				:			Optional[Any]       			=    (
														    safetensors.torch.load_file(__magic_name__ )
														    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors""" ))
														    else torch.load(__magic_name__     ,			map_location="""cpu""" )
														)
														__snake_case				:			Tuple       			=    None
														if checkpoint_path_a:
																	__snake_case				:			int       			=    (
																	    safetensors.torch.load_file(__magic_name__ )
																	    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors""" ))
																	    else torch.load(__magic_name__     ,			map_location="""cpu""" )
																	)

														if not theta_a.keys() == theta_a.keys():
																	print(f'''Skipping {attr}: key mismatch''' )
																	continue
														if theta_a and not theta_a.keys() == theta_a.keys():
																	print(f'''Skipping {attr}:y mismatch''' )
											except Exception as e:
														print(f'''Skipping {attr} do to an unexpected error: {str(__magic_name__ )}''' )
														continue
											print(f'''MERGING {attr}''' )

											for key in theta_a.keys():
														if theta_a:
																	__snake_case				:			Tuple       			=    theta_func(theta_a[key]     ,			theta_a[key]     ,			theta_a[key]     ,			__magic_name__ )
														else:
																	__snake_case				:			List[str]       			=    theta_func(theta_a[key]     ,			theta_a[key]     ,			__magic_name__     ,			__magic_name__ )

											del theta_a
											del theta_a
											update_theta_a(__magic_name__ )

											del theta_a
					return final_pipe

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[Any]     ,			__magic_name__				:      Union[str, Any]     ,			__magic_name__				:      Optional[int]     ,			__magic_name__				:      int )				->						List[str]:


					"""simple docstring"""

					return ((1 - alpha) * thetaa) + (alpha * thetaa)

		@staticmethod
		def   lowercase__    (      __magic_name__				:      str     ,			__magic_name__				:      List[Any]     ,			__magic_name__				:      Dict     ,			__magic_name__				:      List[str] )				->						int:


					"""simple docstring"""

					__snake_case				:			str       			=    alpha * alpha * (3 - (2 * alpha))
					return thetaa + ((thetaa - thetaa) * alpha)

		@staticmethod
		def   lowercase__    (      __magic_name__				:      str     ,			__magic_name__				:      Optional[int]     ,			__magic_name__				:      Union[str, Any]     ,			__magic_name__				:      Dict )				->						Optional[Any]:


					"""simple docstring"""

					import math

					__snake_case				:			int       			=    0.5 - math.sin(math.asin(1.0 - 2.0 * alpha ) / 3.0 )
					return thetaa + ((thetaa - thetaa) * alpha)

		@staticmethod
		def   lowercase__    (      __magic_name__				:      Tuple     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      List[Any]     ,			__magic_name__				:      List[Any] )				->						Tuple:


					"""simple docstring"""

					return thetaa + (thetaa - thetaa) * (1.0 - alpha)









'''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)


