'''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()
_lowercase						: str						=						logging.get_logger(__name__)




def 							lowerCamelCase	(      UpperCAmelCase__       :					Tuple      ,      UpperCAmelCase__       :					str=False      ,      UpperCAmelCase__       :					int=False )			->      Optional[Any]:
							lowercase_					:			Tuple										=       """backbone.""" if is_semantic else """"""

							lowercase_					:			Tuple										=       []
							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	(      UpperCAmelCase__       :					Tuple      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Dict=False      ,      UpperCAmelCase__       :					Dict=False )			->      Optional[Any]:
							for i in range(config.num_hidden_layers ):
														lowercase_					:			Union[str, Any]										=       """backbone.""" if is_semantic else """"""
														# queries, keys and values
														lowercase_					:			Tuple										=       state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight''' )
														lowercase_					:			Optional[int]										=       state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias''' )
														lowercase_					:			Optional[Any]										=       state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias''' )

														lowercase_					:			Tuple										=       in_proj_weight[
														    : config.hidden_size, :
														]
														lowercase_					:			Union[str, Any]										=       q_bias
														lowercase_					:			List[Any]										=       in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														lowercase_					:			int										=       in_proj_weight[
														    -config.hidden_size :, :
														]
														lowercase_					:			Union[str, Any]										=       v_bias

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

														lowercase_					:			Union[str, Any]										=       gamma_a
														lowercase_					:			Optional[int]										=       gamma_a




def 							lowerCamelCase	(      UpperCAmelCase__       :					List[Any]      ,      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					List[Any] )			->      Tuple:
							lowercase_					:			List[str]										=       dct.pop(UpperCAmelCase__ )
							lowercase_					:			Union[str, Any]										=       val




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			List[str]										=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowercase_					:			List[str]										=       Image.open(requests.get(UpperCAmelCase__      ,      stream=UpperCAmelCase__ ).raw )
							return im




@torch.no_grad()
def 							lowerCamelCase	(      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					Tuple=False )			->      Dict:
							lowercase_					:			Any										=       False if """rvlcdip""" in checkpoint_url else True
							lowercase_					:			Optional[Any]										=       BeitConfig(use_absolute_position_embeddings=UpperCAmelCase__      ,      use_mask_token=UpperCAmelCase__ )

							# size of the architecture
							if "large" in checkpoint_url or "dit-l" in checkpoint_url:
														lowercase_					:			int										=       1024
														lowercase_					:			Union[str, Any]										=       4096
														lowercase_					:			str										=       24
														lowercase_					:			Union[str, Any]										=       16

							# labels
							if "rvlcdip" in checkpoint_url:
														lowercase_					:			List[str]										=       16
														lowercase_					:			Any										=       """huggingface/label-files"""
														lowercase_					:			Dict										=       """rvlcdip-id2label.json"""
														lowercase_					:			Dict										=       json.load(open(hf_hub_download(UpperCAmelCase__      ,      UpperCAmelCase__      ,      repo_type="""dataset""" )      ,      """r""" ) )
														lowercase_					:			List[str]										=       {int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
														lowercase_					:			List[str]										=       idalabel
														lowercase_					:			Optional[int]										=       {v: k for k, v in idalabel.items()}

							# load state_dict of original model, remove and rename some keys
							lowercase_					:			Union[str, Any]										=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      ,      map_location="""cpu""" )["""model"""]

							lowercase_					:			Any										=       create_rename_keys(UpperCAmelCase__      ,      has_lm_head=UpperCAmelCase__ )
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )
							read_in_q_k_v(UpperCAmelCase__      ,      UpperCAmelCase__      ,      has_lm_head=UpperCAmelCase__ )

							# load HuggingFace model
							lowercase_					:			Dict										=       BeitForMaskedImageModeling(UpperCAmelCase__ ) if has_lm_head else BeitForImageClassification(UpperCAmelCase__ )
							model.eval()
							model.load_state_dict(UpperCAmelCase__ )

							# Check outputs on an image
							lowercase_					:			Tuple										=       BeitImageProcessor(
							    size=config.image_size      ,      resample=PILImageResampling.BILINEAR      ,      do_center_crop=UpperCAmelCase__ )
							lowercase_					:			Dict										=       prepare_img()

							lowercase_					:			Any										=       image_processor(images=UpperCAmelCase__      ,      return_tensors="""pt""" )
							lowercase_					:			str										=       encoding["""pixel_values"""]

							lowercase_					:			Tuple										=       model(UpperCAmelCase__ )
							lowercase_					:			Tuple										=       outputs.logits

							# verify logits
							lowercase_					:			str										=       [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
							assert logits.shape == torch.Size(UpperCAmelCase__ ), "Shape of logits not as expected"

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

							if push_to_hub:
														if has_lm_head:
																					lowercase_					:			Union[str, Any]										=       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					lowercase_					:			Optional[int]										=       """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(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      organization="""nielsr"""      ,      commit_message="""Add image processor"""      ,      use_temp_dir=UpperCAmelCase__      ,      )
														model.push_to_hub(
														    repo_path_or_name=Path(UpperCAmelCase__      ,      UpperCAmelCase__ )      ,      organization="""nielsr"""      ,      commit_message="""Add model"""      ,      use_temp_dir=UpperCAmelCase__      ,      )


if __name__ == "__main__":
						_lowercase						: str						=						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",
						)
						_lowercase						: Any						=						parser.parse_args()
						convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


_lowercase						: Optional[int]						=						"\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase						: List[Any]						=						"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase						: List[Any]						=						"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,     _KWARGS_DESCRIPTION)
class     __magic_name__						(    datasets.Metric):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""https://github.com/krishnap25/mauve"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""				,			id="""sequence"""    ),
										            """references""": datasets.Value("""string"""				,			id="""sequence"""    ),
										        }    )				,			codebase_urls=["""https://github.com/krishnap25/mauve"""]				,			reference_urls=[
										        """https://arxiv.org/abs/2102.01454""",
										        """https://github.com/krishnap25/mauve""",
										    ]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Any				,			lowercase_				:  Any=None				,			lowercase_				:  str=None				,			lowercase_				:  Dict=None				,			lowercase_				:  Any=None				,			lowercase_				:  int="auto"				,			lowercase_				:  Tuple=-1				,			lowercase_				:  str=0.9				,			lowercase_				:  Union[str, Any]=5				,			lowercase_				:  List[str]=500				,			lowercase_				:  Union[str, Any]="gpt2-large"				,			lowercase_				:  List[Any]=-1				,			lowercase_				:  str=1024				,			lowercase_				:  List[str]=25				,			lowercase_				:  str=5				,			lowercase_				:  List[Any]=True				,			lowercase_				:  Tuple=25				,			):
										lowercase_					:			List[str]										=       compute_mauve(
										    p_text=lowercase_				,			q_text=lowercase_				,			p_features=lowercase_				,			q_features=lowercase_				,			p_tokens=lowercase_				,			q_tokens=lowercase_				,			num_buckets=lowercase_				,			pca_max_data=lowercase_				,			kmeans_explained_var=lowercase_				,			kmeans_num_redo=lowercase_				,			kmeans_max_iter=lowercase_				,			featurize_model_name=lowercase_				,			device_id=lowercase_				,			max_text_length=lowercase_				,			divergence_curve_discretization_size=lowercase_				,			mauve_scaling_factor=lowercase_				,			verbose=lowercase_				,			seed=lowercase_				,			)
										return out





