



import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)

def      _A				(UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		model.config

			lowerCamelCase__ :      List[Any]								=		DonutSwinConfig(
			    image_size=original_config.input_size      ,			patch_size=4      ,			depths=original_config.encoder_layer      ,			num_heads=[4, 8, 16, 32]      ,			window_size=original_config.window_size      ,			embed_dim=128      ,			)
			lowerCamelCase__ :      List[Any]								=		MBartConfig(
			    is_decoder=UpperCamelCase      ,			is_encoder_decoder=UpperCamelCase      ,			add_cross_attention=UpperCamelCase      ,			decoder_layers=original_config.decoder_layer      ,			max_position_embeddings=original_config.max_position_embeddings      ,			vocab_size=len(
			        model.decoder.tokenizer )      ,			scale_embedding=UpperCamelCase      ,			add_final_layer_norm=UpperCamelCase      ,			)

			return encoder_config, decoder_config

def      _A				(UpperCamelCase		:      Optional[int] )  ->Union[str, Any]:



			'''simple docstring'''







			if "encoder.model" in name:
						lowerCamelCase__ :      int								=		name.replace("""encoder.model"""      ,			"""encoder""" )
			if "decoder.model" in name:
						lowerCamelCase__ :      Dict								=		name.replace("""decoder.model"""      ,			"""decoder""" )
			if "patch_embed.proj" in name:
						lowerCamelCase__ :      int								=		name.replace("""patch_embed.proj"""      ,			"""embeddings.patch_embeddings.projection""" )
			if "patch_embed.norm" in name:
						lowerCamelCase__ :      Tuple								=		name.replace("""patch_embed.norm"""      ,			"""embeddings.norm""" )
			if name.startswith("""encoder""" ):
						if "layers" in name:
									lowerCamelCase__ :      Any								=		"""encoder.""" + name
						if "attn.proj" in name:
									lowerCamelCase__ :      Any								=		name.replace("""attn.proj"""      ,			"""attention.output.dense""" )
						if "attn" in name and "mask" not in name:
									lowerCamelCase__ :      Tuple								=		name.replace("""attn"""      ,			"""attention.self""" )
						if "norm1" in name:
									lowerCamelCase__ :      Dict								=		name.replace("""norm1"""      ,			"""layernorm_before""" )
						if "norm2" in name:
									lowerCamelCase__ :      Union[str, Any]								=		name.replace("""norm2"""      ,			"""layernorm_after""" )
						if "mlp.fc1" in name:
									lowerCamelCase__ :      List[Any]								=		name.replace("""mlp.fc1"""      ,			"""intermediate.dense""" )
						if "mlp.fc2" in name:
									lowerCamelCase__ :      Dict								=		name.replace("""mlp.fc2"""      ,			"""output.dense""" )

						if name == "encoder.norm.weight":
									lowerCamelCase__ :      str								=		"""encoder.layernorm.weight"""
						if name == "encoder.norm.bias":
									lowerCamelCase__ :      Dict								=		"""encoder.layernorm.bias"""

			return name

def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      int )  ->Optional[Any]:



			'''simple docstring'''







			for key in orig_state_dict.copy().keys():
						lowerCamelCase__ :      List[str]								=		orig_state_dict.pop(UpperCamelCase )

						if "qkv" in key:
									lowerCamelCase__ :      int								=		key.split(""".""" )
									lowerCamelCase__ :      List[str]								=		int(key_split[3] )
									lowerCamelCase__ :      Optional[Any]								=		int(key_split[5] )
									lowerCamelCase__ :      str								=		model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												lowerCamelCase__ :      Union[str, Any]								=		val[:dim, :]
												lowerCamelCase__ :      Optional[int]								=		val[dim : dim * 2, :]
												lowerCamelCase__ :      Union[str, Any]								=		val[-dim:, :]
									else:
												lowerCamelCase__ :      List[Any]								=		val[:dim]
												lowerCamelCase__ :      Optional[Any]								=		val[dim : dim * 2]
												lowerCamelCase__ :      Dict								=		val[-dim:]
						elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
									# HuggingFace implementation doesn't use attn_mask buffer
									# and model doesn't use final LayerNorms for the encoder
									pass
						else:
									lowerCamelCase__ :      List[str]								=		val

			return orig_state_dict

def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      List[Any]=None      ,			UpperCamelCase		:      Dict=False )  ->List[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Dict								=		DonutModel.from_pretrained(UpperCamelCase ).eval()

			# load HuggingFace model
			lowerCamelCase__ :      List[str]								=		get_configs(UpperCamelCase )
			lowerCamelCase__ :      Union[str, Any]								=		DonutSwinModel(UpperCamelCase )
			lowerCamelCase__ :      Union[str, Any]								=		MBartForCausalLM(UpperCamelCase )
			lowerCamelCase__ :      Any								=		VisionEncoderDecoderModel(encoder=UpperCamelCase      ,			decoder=UpperCamelCase )
			model.eval()

			lowerCamelCase__ :      List[Any]								=		original_model.state_dict()
			lowerCamelCase__ :      List[Any]								=		convert_state_dict(UpperCamelCase      ,			UpperCamelCase )
			model.load_state_dict(UpperCamelCase )

			# verify results on scanned document
			lowerCamelCase__ :      str								=		load_dataset("""hf-internal-testing/example-documents""" )
			lowerCamelCase__ :      List[Any]								=		dataset["""test"""][0]["""image"""].convert("""RGB""" )

			lowerCamelCase__ :      List[Any]								=		XLMRobertaTokenizerFast.from_pretrained(UpperCamelCase      ,			from_slow=UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis      ,			size=original_model.config.input_size[::-1] )
			lowerCamelCase__ :      Optional[int]								=		DonutProcessor(UpperCamelCase      ,			UpperCamelCase )
			lowerCamelCase__ :      Tuple								=		processor(UpperCamelCase      ,			return_tensors="""pt""" ).pixel_values

			if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
						lowerCamelCase__ :      List[str]								=		"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
						lowerCamelCase__ :      Union[str, Any]								=		"""When is the coffee break?"""
						lowerCamelCase__ :      List[str]								=		task_prompt.replace("""{user_input}"""      ,			UpperCamelCase )
			elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
						lowerCamelCase__ :      str								=		"""<s_rvlcdip>"""
			elif model_name in [
			    "naver-clova-ix/donut-base-finetuned-cord-v1",
			    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
			]:
						lowerCamelCase__ :      str								=		"""<s_cord>"""
			elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
						lowerCamelCase__ :      Any								=		"""s_cord-v2>"""
			elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
						lowerCamelCase__ :      List[str]								=		"""<s_zhtrainticket>"""
			elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
						# use a random prompt
						lowerCamelCase__ :      int								=		"""hello world"""
			else:
						raise ValueError("""Model name not supported""" )
			lowerCamelCase__ :      List[Any]								=		original_model.decoder.tokenizer(UpperCamelCase      ,			add_special_tokens=UpperCamelCase      ,			return_tensors="""pt""" )[
			    """input_ids"""
			]

			lowerCamelCase__ :      Tuple								=		original_model.encoder.model.patch_embed(UpperCamelCase )
			lowerCamelCase__ :      str								=		model.encoder.embeddings(UpperCamelCase )
			assert torch.allclose(UpperCamelCase      ,			UpperCamelCase      ,			atol=1E-3 )

			# verify encoder hidden states
			lowerCamelCase__ :      Any								=		original_model.encoder(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		model.encoder(UpperCamelCase ).last_hidden_state
			assert torch.allclose(UpperCamelCase      ,			UpperCamelCase      ,			atol=1E-2 )

			# verify decoder hidden states
			lowerCamelCase__ :      int								=		original_model(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase ).logits
			lowerCamelCase__ :      Dict								=		model(UpperCamelCase      ,			decoder_input_ids=UpperCamelCase ).logits
			assert torch.allclose(UpperCamelCase      ,			UpperCamelCase      ,			atol=1E-3 )
			print("""Looks ok!""" )

			if pytorch_dump_folder_path is not None:
						print(f"Saving model and processor to {pytorch_dump_folder_path}" )
						model.save_pretrained(UpperCamelCase )
						processor.save_pretrained(UpperCamelCase )

			if push_to_hub:
						model.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1]      ,			commit_message="""Update model""" )
						processor.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1]      ,			commit_message="""Update model""" )


if __name__ == "__main__":
				_lowercase     							=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''naver-clova-ix/donut-base-finetuned-docvqa''',
				    required=False,
				    type=str,
				    help='''Name of the original model you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default=None,
				    required=False,
				    type=str,
				    help='''Path to the output PyTorch model directory.''',
				)
				parser.add_argument(
				    '''--push_to_hub''',
				    action='''store_true''',
				    help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
				)

				_lowercase     							=						parser.parse_args()
				convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



