


'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE				:							List[Any]		       =		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE				:							str		       =		{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						_UpperCAmelCase		:					str      	="plbart"
						_UpperCAmelCase		:					Optional[Any]      	=["past_key_values"]
						_UpperCAmelCase		:					List[Any]      	={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






						def __init__(     self       :			str   ,     lowerCAmelCase       :			str=5_00_05   ,     lowerCAmelCase       :			List[str]=10_24   ,     lowerCAmelCase       :			Any=6   ,     lowerCAmelCase       :			List[str]=30_72   ,     lowerCAmelCase       :			Union[str, Any]=12   ,     lowerCAmelCase       :			Any=6   ,     lowerCAmelCase       :			Union[str, Any]=30_72   ,     lowerCAmelCase       :			Optional[int]=12   ,     lowerCAmelCase       :			Tuple=0.0   ,     lowerCAmelCase       :			Any=0.0   ,     lowerCAmelCase       :			Optional[Any]=True   ,     lowerCAmelCase       :			Any=True   ,     lowerCAmelCase       :			str="gelu"   ,     lowerCAmelCase       :			str=7_68   ,     lowerCAmelCase       :			List[str]=0.1   ,     lowerCAmelCase       :			Any=0.1   ,     lowerCAmelCase       :			Optional[Any]=0.0   ,     lowerCAmelCase       :			int=0.0_2   ,     lowerCAmelCase       :			Tuple=0.0   ,     lowerCAmelCase       :			str=True   ,     lowerCAmelCase       :			Tuple=1   ,     lowerCAmelCase       :			Dict=0   ,     lowerCAmelCase       :			List[Any]=2   ,     lowerCAmelCase       :			Dict=2   ,     **lowerCAmelCase       :			Union[str, Any]   ,     ):
													A_ 						=		vocab_size
													A_ 						=		max_position_embeddings
													A_ 						=		d_model
													A_ 						=		encoder_ffn_dim
													A_ 						=		encoder_layers
													A_ 						=		encoder_attention_heads
													A_ 						=		decoder_ffn_dim
													A_ 						=		decoder_layers
													A_ 						=		decoder_attention_heads
													A_ 						=		dropout
													A_ 						=		attention_dropout
													A_ 						=		activation_dropout
													A_ 						=		activation_function
													A_ 						=		init_std
													A_ 						=		encoder_layerdrop
													A_ 						=		decoder_layerdrop
													A_ 						=		classifier_dropout
													A_ 						=		use_cache
													A_ 						=		encoder_layers
													A_ 						=		scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=lowerCAmelCase   ,     bos_token_id=lowerCAmelCase   ,     eos_token_id=lowerCAmelCase   ,     is_encoder_decoder=lowerCAmelCase   ,     forced_eos_token_id=lowerCAmelCase   ,     **lowerCAmelCase   ,     )






class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						@property
						def 							_UpperCAmelCase					(     self       :			Dict  ):
													return OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "sequence"}),
													        ("attention_mask", {0: "batch", 1: "sequence"}),
													    ]  )






						@property
						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													if self.use_past:
																				return OrderedDict(
																				    [
																				        ("last_hidden_state", {0: "batch", 1: "sequence"}),
																				        ("past_keys", {0: "batch", 2: "sequence"}),
																				        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
																				    ]  )
													else:
																				return OrderedDict(
																				    [
																				        ("last_hidden_state", {0: "batch", 1: "sequence"}),
																				        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
																				    ]  )









'''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_							(  UpperCamelCase_   ):
							A_ 						=		"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 			a_							(  ):
							A_ 						=		VivitConfig()

							A_ 						=		4_0_0
							A_ 						=		"huggingface/label-files"
							A_ 						=		"kinetics400-id2label.json"

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







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







def 			a_							(  UpperCamelCase_   ):
							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 			a_							(  UpperCamelCase_   ):
							return current.flatten()







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







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







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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False   ):
							A_ 						=		get_n_layers(UpperCamelCase_   )

							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(UpperCamelCase_   ):
														new_state.update(transform_state_encoder_block(UpperCamelCase_	,     UpperCamelCase_   )   )

							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(UpperCamelCase_   ) for k, v in new_state.items()}







def 			a_							(  ):
							A_ 						=		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 			a_							(  UpperCamelCase_   ):
							A_ 						=		"checkpoint"

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

							A_ 						=		restore_checkpoint(UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		transform_state(UpperCamelCase_	,     classification_head=UpperCamelCase_   )

							A_ 						=		get_vivit_config()

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

							A_ 						=		VivitForVideoClassification(UpperCamelCase_   )
							model.load_state_dict(UpperCamelCase_   )
							model.eval()

							A_ 						=		get_processor()

							A_ 						=		prepare_video()
							A_ 						=		extractor(UpperCamelCase_	,     return_tensors="pt"   )

							A_ 						=		model(**UpperCamelCase_   )

							A_ 						=		torch.Size([1, 4_0_0]   )
							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]	,     UpperCamelCase_	,     atol=1e-4   ), outputs.logits[0, :5]

							model.save_pretrained(UpperCamelCase_   )
							extractor.save_pretrained(UpperCamelCase_   )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		argparse.ArgumentParser()

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

							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		parser.parse_args()
							convert(args.output_model_name)





