'''simple docstring'''


import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__lowerCAmelCase	:       Tuple						       =	re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
__lowerCAmelCase	:       Any						       =	10
__lowerCAmelCase	:       Dict						       =	256





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	if len(UpperCamelCase__       ) < MIN_NUM_TOKENS:
		return None
	__UpperCAmelCase							=			MinHash(num_perm=UpperCamelCase__       )
	for token in set(UpperCamelCase__       ):
		min_hash.update(token.encode()       )
	return min_hash





def       lowerCAmelCase					(		UpperCamelCase__				:   str       ):


	"""simple docstring"""
	return {t for t in NON_ALPHA.split(UpperCamelCase__       ) if len(t.strip()       ) > 0}






class 						A   :





							def __init__(      self				:							List[Any]     ,		*,
							    __a				:							float = 0.8_5     ,		)							->	Optional[int]:
								__UpperCAmelCase							=			duplication_jaccard_threshold
								__UpperCAmelCase							=			NUM_PERM
								__UpperCAmelCase							=			MinHashLSH(threshold=self._duplication_jaccard_threshold     ,		num_perm=self._num_perm	)

								__UpperCAmelCase							=			defaultdict(__a	)





							def       snake_case__						(      self				:							Dict     ,		__a				:							Tuple     ,		__a				:							MinHash	)							->	None:
								__UpperCAmelCase							=			self._index.query(__a	)
								if code_key in self._index.keys:
									print(f"""Duplicate key {code_key}"""	)
									return

								self._index.insert(__a     ,		__a	)
								if len(__a	) > 0:
									for base_duplicate in close_duplicates:
										if base_duplicate in self._duplicate_clusters:
											self._duplicate_clusters[base_duplicate].add(__a	)
											break
									else:
										self._duplicate_clusters[close_duplicates[0]].add(__a	)





							def       snake_case__						(      self				:							str	)							->	List[List[Dict]]:
								__UpperCAmelCase							=			[]
								for base, duplicates in self._duplicate_clusters.items():
									__UpperCAmelCase							=			[base] + list(__a	)
									# reformat the cluster to be a list of dict
									__UpperCAmelCase							=			[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
									duplicate_clusters.append(__a	)
								return duplicate_clusters






							def       snake_case__						(      self				:							int     ,		__a				:							str	)							->	None:
								__UpperCAmelCase							=			self.get_duplicate_clusters()
								with open(__a     ,		'''w'''	) as f:
									json.dump(__a     ,		__a	)





def       lowerCAmelCase					(		UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase							=			element
	__UpperCAmelCase							=			get_min_hash([t for t in NON_ALPHA.split(data['''content''']       ) if len(t.strip()       ) > 0]       )
	if min_hash is not None:
		return (index, data["repo_name"], data["path"]), min_hash





def       lowerCAmelCase					(		UpperCamelCase__				:   Type[Dataset]       ):


	"""simple docstring"""
	with mp.Pool() as pool:
		for data in pool.imap_unordered(
		    _compute_min_hash    ,					ThreadedIterator(UpperCamelCase__    ,					max_queue_size=1_0_0_0_0       )    ,					chunksize=1_0_0    ,					):
			if data is not None:
				yield data





def       lowerCAmelCase					(		UpperCamelCase__				:   Type[Dataset]    ,					UpperCamelCase__				:   float       ):


	"""simple docstring"""
	__UpperCAmelCase							=			DuplicationIndex(duplication_jaccard_threshold=UpperCamelCase__       )

	for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(UpperCamelCase__       )       )    ,					max_queue_size=1_0_0       )       ):
		di.add(UpperCamelCase__    ,					UpperCamelCase__       )

	# Returns a List[Cluster] where Cluster is List[str] with the filenames.
	return di.get_duplicate_clusters()





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			get_tokens(UpperCamelCase__       )
	__UpperCAmelCase							=			get_tokens(UpperCamelCase__       )
	return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


__lowerCAmelCase	:       str						       =	None





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for elementa in cluster:
		__UpperCAmelCase							=			_shared_dataset[elementa['''base_index''']]['''content''']
		for elementa in extremes:
			__UpperCAmelCase							=			_shared_dataset[elementa['''base_index''']]['''content''']
			if jaccard_similarity(UpperCamelCase__    ,					UpperCamelCase__       ) >= jaccard_threshold:
				elementa["copies"] += 1
				break
		else:
			__UpperCAmelCase							=			1
			extremes.append(UpperCamelCase__       )
	return extremes





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	global _shared_dataset
	__UpperCAmelCase							=			dataset
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			partial(_find_cluster_extremes_shared    ,					jaccard_threshold=UpperCamelCase__       )
	with mp.Pool() as pool:
		for extremes in tqdm(
		    pool.imap_unordered(
		        UpperCamelCase__    ,					UpperCamelCase__    ,					)    ,					total=len(UpperCamelCase__       )    ,					):
			extremes_list.append(UpperCamelCase__       )
	return extremes_list





def       lowerCAmelCase					(		UpperCamelCase__				:   Type[Dataset]    ,					UpperCamelCase__				:   float = 0.85       ):


	"""simple docstring"""
	__UpperCAmelCase							=			make_duplicate_clusters(UpperCamelCase__    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
	__UpperCAmelCase							=			{}
	__UpperCAmelCase							=			find_extremes(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	for extremes in extremes_clusters:
		for element in extremes:
			__UpperCAmelCase							=			element
	__UpperCAmelCase							=			duplicate_indices - set(extreme_dict.keys()       )
	__UpperCAmelCase							=			dataset.filter(lambda UpperCamelCase__    ,					UpperCamelCase__       : idx not in remove_indices    ,					with_indices=UpperCamelCase__       )

	# update duplicate_clusters
	for cluster in duplicate_clusters:
		for element in cluster:
			__UpperCAmelCase							=			element['''base_index'''] in extreme_dict
			if element["is_extreme"]:
				__UpperCAmelCase							=			extreme_dict[element['''base_index''']]['''copies''']

	print(f"""Original dataset size: {len(UpperCamelCase__       )}"""       )
	print(f"""Number of duplicate clusters: {len(UpperCamelCase__       )}"""       )
	print(f"""Files in duplicate cluster: {len(UpperCamelCase__       )}"""       )
	print(f"""Unique files in duplicate cluster: {len(UpperCamelCase__       )}"""       )
	print(f"""Filtered dataset size: {len(UpperCamelCase__       )}"""       )

	return ds_filter, duplicate_clusters

'''simple docstring'''


import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	for i in range(encoder_config.num_hidden_layers       ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.norm1.weight""", f"""encoder.encoder.layer.{i}.layernorm_before.weight""")       )
		rename_keys.append((f"""encoder.deit.blocks.{i}.norm1.bias""", f"""encoder.encoder.layer.{i}.layernorm_before.bias""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.attn.proj.weight""", f"""encoder.encoder.layer.{i}.attention.output.dense.weight""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.attn.proj.bias""", f"""encoder.encoder.layer.{i}.attention.output.dense.bias""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.norm2.weight""", f"""encoder.encoder.layer.{i}.layernorm_after.weight""")       )
		rename_keys.append((f"""encoder.deit.blocks.{i}.norm2.bias""", f"""encoder.encoder.layer.{i}.layernorm_after.bias""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc1.weight""", f"""encoder.encoder.layer.{i}.intermediate.dense.weight""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc1.bias""", f"""encoder.encoder.layer.{i}.intermediate.dense.bias""")       )
		rename_keys.append(
		    (f"""encoder.deit.blocks.{i}.mlp.fc2.weight""", f"""encoder.encoder.layer.{i}.output.dense.weight""")       )
		rename_keys.append((f"""encoder.deit.blocks.{i}.mlp.fc2.bias""", f"""encoder.encoder.layer.{i}.output.dense.bias""")       )

	# cls token, position embeddings and patch embeddings of encoder
	rename_keys.extend(
	    [
	        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
	        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
	        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
	        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
	        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
	        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
	    ]       )

	return rename_keys





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	for i in range(encoder_config.num_hidden_layers       ):
		# queries, keys and values (only weights, no biases)
		__UpperCAmelCase							=			state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""       )

		__UpperCAmelCase							=			in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__UpperCAmelCase							=			in_proj_weight[
		    -encoder_config.hidden_size :, :
		]





def       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	if "handwritten" in checkpoint_url:
		__UpperCAmelCase							=			'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
		# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
	elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
		__UpperCAmelCase							=			'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
	__UpperCAmelCase							=			Image.open(requests.get(UpperCamelCase__    ,					stream=UpperCamelCase__       ).raw       ).convert('''RGB'''       )
	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__UpperCAmelCase							=			7_6_8
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			4_0_9_6
		__UpperCAmelCase							=			2_4
		__UpperCAmelCase							=			1_6
		__UpperCAmelCase							=			1_0_2_4
	else:
		raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''       )

	# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
	if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			'''relu'''
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			True
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			False

	# load HuggingFace model
	__UpperCAmelCase							=			ViTModel(UpperCamelCase__    ,					add_pooling_layer=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRForCausalLM(UpperCamelCase__       )
	__UpperCAmelCase							=			VisionEncoderDecoderModel(encoder=UpperCamelCase__    ,					decoder=UpperCamelCase__       )
	model.eval()

	# load state_dict of original model, rename some keys
	__UpperCAmelCase							=			torch.hub.load_state_dict_from_url(UpperCamelCase__    ,					map_location='''cpu'''    ,					check_hash=UpperCamelCase__       )['''model''']

	__UpperCAmelCase							=			create_rename_keys(UpperCamelCase__    ,					UpperCamelCase__       )
	for src, dest in rename_keys:
		rename_key(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )
	read_in_q_k_v(UpperCamelCase__    ,					UpperCamelCase__       )

	# remove parameters we don't need
	del state_dict["encoder.deit.head.weight"]
	del state_dict["encoder.deit.head.bias"]
	del state_dict["decoder.version"]

	# add prefix to decoder keys
	for key, val in state_dict.copy().items():
		__UpperCAmelCase							=			state_dict.pop(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

	# Check outputs on an image
	__UpperCAmelCase							=			ViTImageProcessor(size=encoder_config.image_size       )
	__UpperCAmelCase							=			RobertaTokenizer.from_pretrained('''roberta-large'''       )
	__UpperCAmelCase							=			TrOCRProcessor(UpperCamelCase__    ,					UpperCamelCase__       )

	__UpperCAmelCase							=			processor(images=prepare_img(UpperCamelCase__       )    ,					return_tensors='''pt'''       ).pixel_values

	# verify logits
	__UpperCAmelCase							=			torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
	__UpperCAmelCase							=			model(pixel_values=UpperCamelCase__    ,					decoder_input_ids=UpperCamelCase__       )
	__UpperCAmelCase							=			outputs.logits

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]       )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]       )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]       )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase							=			torch.tensor(
		    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]       )

	if "stage1" not in checkpoint_url:
		assert logits.shape == expected_shape, "Shape of logits not as expected"
		assert torch.allclose(logits[0, 0, :1_0]    ,					UpperCamelCase__    ,					atol=1E-3       ), "First elements 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 processor to {pytorch_dump_folder_path}"""       )
	processor.save_pretrained(UpperCamelCase__       )


if __name__ == "__main__":
					__lowerCAmelCase	:       Any						       =	argparse.ArgumentParser()

					parser.add_argument(
					    "--checkpoint_url",
					    default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
					    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."
					)
					__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
