
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a=False,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			'''backbone.''' if is_semantic else ''''''

		SCREAMING_SNAKE_CASE_    			=			[]
		for i in range(config.num_hidden_layers     ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((F'{prefix}blocks.{i}.norm1.weight', F'beit.encoder.layer.{i}.layernorm_before.weight')     )
				rename_keys.append((F'{prefix}blocks.{i}.norm1.bias', F'beit.encoder.layer.{i}.layernorm_before.bias')     )
				rename_keys.append(
				    (F'{prefix}blocks.{i}.attn.proj.weight', F'beit.encoder.layer.{i}.attention.output.dense.weight')     )
				rename_keys.append(
				    (F'{prefix}blocks.{i}.attn.proj.bias', F'beit.encoder.layer.{i}.attention.output.dense.bias')     )
				rename_keys.append((F'{prefix}blocks.{i}.norm2.weight', F'beit.encoder.layer.{i}.layernorm_after.weight')     )
				rename_keys.append((F'{prefix}blocks.{i}.norm2.bias', F'beit.encoder.layer.{i}.layernorm_after.bias')     )
				rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.weight', F'beit.encoder.layer.{i}.intermediate.dense.weight')     )
				rename_keys.append((F'{prefix}blocks.{i}.mlp.fc1.bias', F'beit.encoder.layer.{i}.intermediate.dense.bias')     )
				rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.weight', F'beit.encoder.layer.{i}.output.dense.weight')     )
				rename_keys.append((F'{prefix}blocks.{i}.mlp.fc2.bias', F'beit.encoder.layer.{i}.output.dense.bias')     )

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        (F'{prefix}cls_token', '''beit.embeddings.cls_token'''),
		        (F'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
		        (F'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
		        (F'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
		    ]     )

		if has_lm_head:
				# mask token + layernorm
				rename_keys.extend(
				    [
				        ('''mask_token''', '''beit.embeddings.mask_token'''),
				        ('''norm.weight''', '''layernorm.weight'''),
				        ('''norm.bias''', '''layernorm.bias'''),
				    ]     )
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
				        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
				        ('''head.weight''', '''classifier.weight'''),
				        ('''head.bias''', '''classifier.bias'''),
				    ]     )

		return rename_keys



def 							_lowerCamelCase			(			__a,				__a,				__a=False,				__a=False     ):
		for i in range(config.num_hidden_layers     ):
				SCREAMING_SNAKE_CASE_    			=			'''backbone.''' if is_semantic else ''''''
				# queries, keys and values
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'     )
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'     )
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'     )

				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE_    			=			q_bias
				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE_    			=			in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE_    			=			v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'{prefix}blocks.{i}.gamma_1'     )
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(F'{prefix}blocks.{i}.gamma_2'     )

				SCREAMING_SNAKE_CASE_    			=			gamma_a
				SCREAMING_SNAKE_CASE_    			=			gamma_a



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			dct.pop(__a     )
		SCREAMING_SNAKE_CASE_    			=			val



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			False if '''rvlcdip''' in checkpoint_url else True
		SCREAMING_SNAKE_CASE_    			=			BeitConfig(use_absolute_position_embeddings=__a,				use_mask_token=__a     )

		# size of the architecture
		if "large" in checkpoint_url or "dit-l" in checkpoint_url:
				SCREAMING_SNAKE_CASE_    			=			1_024
				SCREAMING_SNAKE_CASE_    			=			4_096
				SCREAMING_SNAKE_CASE_    			=			24
				SCREAMING_SNAKE_CASE_    			=			16

		# labels
		if "rvlcdip" in checkpoint_url:
				SCREAMING_SNAKE_CASE_    			=			16
				SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
				SCREAMING_SNAKE_CASE_    			=			'''rvlcdip-id2label.json'''
				SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
				SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE_    			=			idalabel
				SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		# load state_dict of original model, remove and rename some keys
		SCREAMING_SNAKE_CASE_    			=			torch.hub.load_state_dict_from_url(__a,				map_location='''cpu'''     )['''model''']

		SCREAMING_SNAKE_CASE_    			=			create_rename_keys(__a,				has_lm_head=__a     )
		for src, dest in rename_keys:
				rename_key(__a,				__a,				__a     )
		read_in_q_k_v(__a,				__a,				has_lm_head=__a     )

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BeitForMaskedImageModeling(__a     ) if has_lm_head else BeitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

		# Check outputs on an image
		SCREAMING_SNAKE_CASE_    			=			BeitImageProcessor(
		    size=config.image_size,				resample=PILImageResampling.BILINEAR,				do_center_crop=__a     )
		SCREAMING_SNAKE_CASE_    			=			prepare_img()

		SCREAMING_SNAKE_CASE_    			=			image_processor(images=__a,				return_tensors='''pt'''     )
		SCREAMING_SNAKE_CASE_    			=			encoding['''pixel_values''']

		SCREAMING_SNAKE_CASE_    			=			model(__a     )
		SCREAMING_SNAKE_CASE_    			=			outputs.logits

		# verify logits
		SCREAMING_SNAKE_CASE_    			=			[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8_192]
		assert logits.shape == torch.Size(__a     ), "Shape of logits not as expected"

		Path(__a     ).mkdir(exist_ok=__a     )
		print(F'Saving model to {pytorch_dump_folder_path}'     )
		model.save_pretrained(__a     )
		print(F'Saving image processor to {pytorch_dump_folder_path}'     )
		image_processor.save_pretrained(__a     )

		if push_to_hub:
				if has_lm_head:
						SCREAMING_SNAKE_CASE_    			=			'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
				else:
						SCREAMING_SNAKE_CASE_    			=			'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
				image_processor.push_to_hub(
				    repo_path_or_name=Path(__a,				__a     ),				organization='''nielsr''',				commit_message='''Add image processor''',				use_temp_dir=__a,				)
				model.push_to_hub(
				    repo_path_or_name=Path(__a,				__a     ),				organization='''nielsr''',				commit_message='''Add model''',				use_temp_dir=__a,				)


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

						parser.add_argument(
						    '--checkpoint_url',
						    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
						    type=str,
						    help='URL to the original PyTorch checkpoint (.pth file).',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						)
						lowerCAmelCase__				=					parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCAmelCase__				=					r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'





@add_start_docstrings(__lowercase )
class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''rag'''
							UpperCAmelCase__      =   True

							def __init__(self							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=" / "							,							SCREAMING_SNAKE_CASE_=" // "							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=3_00							,							SCREAMING_SNAKE_CASE_=7_68							,							SCREAMING_SNAKE_CASE_=8							,							SCREAMING_SNAKE_CASE_="wiki_dpr"							,							SCREAMING_SNAKE_CASE_="train"							,							SCREAMING_SNAKE_CASE_="compressed"							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0.0							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=None							,							**SCREAMING_SNAKE_CASE_							,							):

									"""simple docstring"""







									super().__init__(
									    bos_token_id=SCREAMING_SNAKE_CASE_							,							pad_token_id=SCREAMING_SNAKE_CASE_							,							eos_token_id=SCREAMING_SNAKE_CASE_							,							decoder_start_token_id=SCREAMING_SNAKE_CASE_							,							forced_eos_token_id=SCREAMING_SNAKE_CASE_							,							is_encoder_decoder=SCREAMING_SNAKE_CASE_							,							prefix=SCREAMING_SNAKE_CASE_							,							vocab_size=SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_							,							)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''question_encoder'''     )
									SCREAMING_SNAKE_CASE_    			=			question_encoder_config.pop('''model_type'''     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''generator'''     )
									SCREAMING_SNAKE_CASE_    			=			decoder_config.pop('''model_type'''     )

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			AutoConfig.for_model(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			reduce_loss
									SCREAMING_SNAKE_CASE_    			=			label_smoothing
									SCREAMING_SNAKE_CASE_    			=			exclude_bos_score
									SCREAMING_SNAKE_CASE_    			=			do_marginalize

									SCREAMING_SNAKE_CASE_    			=			title_sep
									SCREAMING_SNAKE_CASE_    			=			doc_sep
									SCREAMING_SNAKE_CASE_    			=			n_docs
									SCREAMING_SNAKE_CASE_    			=			max_combined_length

									SCREAMING_SNAKE_CASE_    			=			dataset
									SCREAMING_SNAKE_CASE_    			=			dataset_split
									SCREAMING_SNAKE_CASE_    			=			index_name

									SCREAMING_SNAKE_CASE_    			=			retrieval_vector_size
									SCREAMING_SNAKE_CASE_    			=			retrieval_batch_size
									SCREAMING_SNAKE_CASE_    			=			passages_path
									SCREAMING_SNAKE_CASE_    			=			index_path
									SCREAMING_SNAKE_CASE_    			=			use_dummy_dataset

									SCREAMING_SNAKE_CASE_    			=			output_retrieved

									SCREAMING_SNAKE_CASE_    			=			do_deduplication

									SCREAMING_SNAKE_CASE_    			=			use_cache

									if self.forced_eos_token_id is None:
											SCREAMING_SNAKE_CASE_    			=			getattr(self.generator							,							'''forced_eos_token_id'''							,							SCREAMING_SNAKE_CASE_     )

							@classmethod
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return cls(question_encoder=question_encoder_config.to_dict()							,							generator=generator_config.to_dict()							,							**SCREAMING_SNAKE_CASE_     )







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			copy.deepcopy(self.__dict__     )
									SCREAMING_SNAKE_CASE_    			=			self.question_encoder.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.generator.to_dict()
									SCREAMING_SNAKE_CASE_    			=			self.__class__.model_type
									return output