import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


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

UpperCamelCase      			=     [
    (
        'protein1',
        'MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA',
    ),
    ('protein2', 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'),
    ('protein3', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'),
    ('protein4', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'),
]

UpperCamelCase      			=     {
    'esm1b_t33_650M_UR50S': esm_module.pretrained.esm1b_t33_650M_UR50S,
    'esm1v_t33_650M_UR90S_1': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    'esm1v_t33_650M_UR90S_2': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    'esm1v_t33_650M_UR90S_3': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    'esm1v_t33_650M_UR90S_4': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    'esm1v_t33_650M_UR90S_5': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    'esm2_t48_15B_UR50D': esm_module.pretrained.esm2_t48_15B_UR50D,
    'esm2_t36_3B_UR50D': esm_module.pretrained.esm2_t36_3B_UR50D,
    'esm2_t33_650M_UR50D': esm_module.pretrained.esm2_t33_650M_UR50D,
    'esm2_t30_150M_UR50D': esm_module.pretrained.esm2_t30_150M_UR50D,
    'esm2_t12_35M_UR50D': esm_module.pretrained.esm2_t12_35M_UR50D,
    'esm2_t6_8M_UR50D': esm_module.pretrained.esm2_t6_8M_UR50D,
    'esmfold_v1': esmfold_va,
}

UpperCamelCase      			=     list('ARNDCQEGHILKMFPSTWYV')

UpperCamelCase      			=     restypes + ['X']
UpperCamelCase      			=     restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']



def    lowerCamelCase_   (  )							->	Tuple:
		with TemporaryDirectory() as tempdir:
				__A							:   Dict	  =				"\n".join(_lowercase    )
				__A							:   List[Any]	  =				Path(_lowercase    ) / "vocab.txt"
				vocab_file.write_text(_lowercase    )
				__A							:   List[Any]	  =				EsmTokenizer(vocab_file=str(_lowercase    )    )
		__A							:   List[Any]	  =				0  # Overlaps with 'A' but that seems to be what they want
		return hf_tokenizer



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	List[Any]:
		__A							:   List[str]	  =				our_module.load_state_dict(original_module.state_dict()    )
		if status.missing_keys:
				raise ValueError(F"Missing keys: {status.missing_keys}"    )
		if status.unexpected_keys:
				raise ValueError(F"Unexpected keys: {status.unexpected_keys}"    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase    )							->	str:
		if model.startswith("esmfold"    ):
				__A							:   Dict	  =				MODEL_MAPPING[model]()
		else:
				__A  ,       __A							:   Union[str, Any]	  =				MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

		if model.startswith("esmfold"    ):
				__A							:   List[str]	  =				esm.esm.embed_dim
				__A							:   Dict	  =				esm.esm.num_layers
				__A							:   str	  =				esm.esm.attention_heads
				__A							:   Union[str, Any]	  =				4 * embed_dim
				__A							:   Tuple	  =				esm.esm.token_dropout
				__A							:   Optional[int]	  =				False  # This code path does not exist in ESM-2
				__A							:   str	  =				"rotary"
				__A							:   Tuple	  =				True
				__A							:   Any	  =				EsmFoldConfig()
				for key, val in esm.cfg.items():
						if hasattr(_lowercase ,     _lowercase    ) and key != "trunk":
								setattr(_lowercase ,     _lowercase ,     _lowercase    )
				for key, val in esm.cfg.trunk.items():
						if hasattr(esmfold_config.trunk ,     _lowercase    ) and key != "structure_module":
								setattr(esmfold_config.trunk ,     _lowercase ,     _lowercase    )
				for key, val in esm.cfg.trunk.structure_module.items():
						if hasattr(esmfold_config.trunk.structure_module ,     _lowercase    ):
								setattr(esmfold_config.trunk.structure_module ,     _lowercase ,     _lowercase    )
		elif hasattr(_lowercase ,     "args"    ):
				# Indicates an ESM-1b or ESM-1v model
				__A							:   Dict	  =				esm.args.embed_dim
				__A							:   Optional[int]	  =				esm.args.layers
				__A							:   List[str]	  =				esm.args.attention_heads
				__A							:   List[Any]	  =				esm.args.ffn_embed_dim
				__A							:   str	  =				esm.args.token_dropout
				__A							:   Tuple	  =				True if esm.emb_layer_norm_before else False
				__A							:   int	  =				"absolute"
				__A							:   Dict	  =				False
				__A							:   List[str]	  =				None
		else:
				# Indicates an ESM-2 model
				__A							:   Any	  =				esm.embed_dim
				__A							:   Optional[int]	  =				esm.num_layers
				__A							:   int	  =				esm.attention_heads
				__A							:   List[Any]	  =				4 * embed_dim  # This is hardcoded in ESM-2
				__A							:   Any	  =				esm.token_dropout
				__A							:   Optional[Any]	  =				False  # This code path does not exist in ESM-2
				__A							:   List[str]	  =				"rotary"
				__A							:   Optional[Any]	  =				False
				__A							:   int	  =				None

		if is_folding_model:
				__A							:   int	  =				esm.esm.alphabet
		__A							:   List[Any]	  =				tuple(alphabet.all_toks    )
		__A							:   List[Any]	  =				alphabet.mask_idx
		__A							:   Any	  =				alphabet.padding_idx

		if is_folding_model:
				__A							:   Any	  =				esm.esm
		else:
				__A							:   Dict	  =				esm

		__A							:   Optional[int]	  =				EsmConfig(
		    vocab_size=original_esm_model.embed_tokens.num_embeddings ,     mask_token_id=_lowercase ,     hidden_size=_lowercase ,     num_hidden_layers=_lowercase ,     num_attention_heads=_lowercase ,     intermediate_size=_lowercase ,     max_position_embeddings=1_026 ,     layer_norm_eps=1E-5 ,     attention_probs_dropout_prob=0.0 ,     hidden_dropout_prob=0.0 ,     pad_token_id=_lowercase ,     emb_layer_norm_before=_lowercase ,     token_dropout=_lowercase ,     position_embedding_type=_lowercase ,     is_folding_model=_lowercase ,     esmfold_config=_lowercase ,     vocab_list=_lowercase ,     )
		if classification_head:
				__A							:   Optional[int]	  =				esm.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our ESM config:" ,     _lowercase    )

		if model.startswith("esmfold"    ):
				__A							:   str	  =				EsmForProteinFolding
		elif classification_head:
				__A							:   Dict	  =				EsmForSequenceClassification
		else:
				__A							:   Dict	  =				EsmForMaskedLM
		__A							:   Tuple	  =				model_class(_lowercase    )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		__A							:   Optional[int]	  =				original_esm_model.embed_tokens.weight
		if position_embedding_type == "absolute":
				__A							:   str	  =				original_esm_model.embed_positions.weight

		if config.emb_layer_norm_before:
				__A							:   List[Any]	  =				original_esm_model.emb_layer_norm_before.weight
				__A							:   int	  =				original_esm_model.emb_layer_norm_before.bias

		__A							:   Optional[Any]	  =				original_esm_model.emb_layer_norm_after.weight
		__A							:   Dict	  =				original_esm_model.emb_layer_norm_after.bias

		for i in range(config.num_hidden_layers    ):
				# Encoder: start of layer
				__A							:   List[str]	  =				model.esm.encoder.layer[i]
				# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
				__A							:   Union[str, Any]	  =				original_esm_model.layers[i]

				# self attention
				__A							:   int	  =				layer.attention.self
				assert (
				    esm_layer.self_attn.k_proj.weight.data.shape
				    == esm_layer.self_attn.q_proj.weight.data.shape
				    == esm_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)    )
				)

				__A							:   Optional[int]	  =				esm_layer.self_attn.q_proj.weight
				__A							:   List[Any]	  =				esm_layer.self_attn.q_proj.bias
				__A							:   Any	  =				esm_layer.self_attn.k_proj.weight
				__A							:   List[str]	  =				esm_layer.self_attn.k_proj.bias
				__A							:   Tuple	  =				esm_layer.self_attn.v_proj.weight
				__A							:   Dict	  =				esm_layer.self_attn.v_proj.bias

				if getattr(esm_layer.self_attn ,     "rot_emb" ,     _lowercase    ) is not None:
						# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
						# During the training of ESM-2 the model was converted to float16 precision, which also converts
						# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
						# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
						# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
						# we make sure the new model copies the data from the old inv_freq.
						__A							:   int	  =				esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				__A							:   List[Any]	  =				esm_layer.self_attn_layer_norm.weight
				__A							:   str	  =				esm_layer.self_attn_layer_norm.bias
				__A							:   Dict	  =				esm_layer.final_layer_norm.weight
				__A							:   Optional[int]	  =				esm_layer.final_layer_norm.bias

				# self-attention output
				__A							:   Optional[int]	  =				layer.attention.output
				assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
				__A							:   int	  =				esm_layer.self_attn.out_proj.weight
				__A							:   str	  =				esm_layer.self_attn.out_proj.bias

				# intermediate
				__A							:   Optional[int]	  =				layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				__A							:   Optional[Any]	  =				esm_layer.fca.weight
				__A							:   Optional[Any]	  =				esm_layer.fca.bias

				# output
				__A							:   str	  =				layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				__A							:   Optional[int]	  =				esm_layer.fca.weight
				__A							:   Optional[int]	  =				esm_layer.fca.bias
				# end of layer

		if is_folding_model:
				__A							:   Any	  =				esm.esm_s_combine.data
				__A							:   List[str]	  =				esm.afa_to_esm.data
				transfer_and_check_weights(esm.embedding ,     model.embedding    )
				transfer_and_check_weights(esm.esm_s_mlp ,     model.esm_s_mlp    )
				transfer_and_check_weights(esm.trunk ,     model.trunk    )
				transfer_and_check_weights(esm.distogram_head ,     model.distogram_head    )
				transfer_and_check_weights(esm.ptm_head ,     model.ptm_head    )
				transfer_and_check_weights(esm.lm_head ,     model.lm_head    )
				transfer_and_check_weights(esm.lddt_head ,     model.lddt_head    )

		elif classification_head:
				__A							:   str	  =				esm.esm.classification_heads["mnli"].dense.weight
				__A							:   Optional[Any]	  =				esm.classification_heads["mnli"].dense.bias
				__A							:   List[str]	  =				esm.classification_heads["mnli"].out_proj.weight
				__A							:   Tuple	  =				esm.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				__A							:   Dict	  =				esm.lm_head.dense.weight
				__A							:   List[str]	  =				esm.lm_head.dense.bias
				__A							:   List[Any]	  =				esm.lm_head.layer_norm.weight
				__A							:   List[Any]	  =				esm.lm_head.layer_norm.bias
				__A							:   List[Any]	  =				esm.lm_head.weight
				__A							:   Dict	  =				esm.lm_head.bias

		# Contact prediction head
		transfer_and_check_weights(esm.contact_head ,     model.esm.contact_head    )

		# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
		if is_folding_model:
				# Folding models aren't trained on masked inputs and don't like mask tokens.
				__A							:   str	  =				SAMPLE_DATA[:2]
		else:
				__A							:   Tuple	  =				SAMPLE_DATA

		if is_folding_model:
				__A							:   List[Any]	  =				get_esmfold_tokenizer()
				__A							:   Optional[Any]	  =				hf_tokenizer(
				    [row[1] for row in sample_data] ,     return_tensors="pt" ,     padding=_lowercase ,     add_special_tokens=_lowercase    )
				__A  ,       __A  ,       __A  ,       __A  ,       __A							:   Tuple	  =				esmfold_encode_sequences([row[1] for row in sample_data]    )
				__A							:   Optional[Any]	  =				torch.all(hf_tokens["input_ids"] == esmfold_aas    ) and torch.all(
				    hf_tokens["attention_mask"] == esmfold_mask    )
		else:
				# Let's check that we get the same results.
				__A							:   Any	  =				alphabet.get_batch_converter()
				__A  ,       __A  ,       __A							:   List[str]	  =				batch_converter(_lowercase    )
				# Prepare tokenizer and make sure it matches
				with TemporaryDirectory() as tempdir:
						__A							:   Any	  =				"\n".join(alphabet.all_toks    )
						__A							:   List[Any]	  =				Path(_lowercase    ) / "vocab.txt"
						vocab_file.write_text(_lowercase    )
						__A							:   Any	  =				EsmTokenizer(vocab_file=str(_lowercase    )    )

				__A							:   Union[str, Any]	  =				hf_tokenizer([row[1] for row in sample_data] ,     return_tensors="pt" ,     padding=_lowercase    )
				__A							:   Tuple	  =				torch.all(hf_tokens["input_ids"] == batch_tokens    )

		print("Do both models tokenizers output the same tokens?" ,     "🔥" if success else "💩"    )
		if not success:
				raise Exception("Tokenization does not match!"    )

		with torch.no_grad():
				if is_folding_model:
						# Let's test the model in parts
						# ESMFold always converts the ESM stem to float16, which requires float16 ops
						# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
						# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
						# original and the converted model on the GPU at the same time.
						__A							:   Tuple	  =				esm.cuda().infer([row[1] for row in sample_data]    )
						__A							:   str	  =				model.cuda()(
						    input_ids=hf_tokens["input_ids"].cuda() ,     attention_mask=hf_tokens["attention_mask"].cuda()    )
				else:
						__A							:   str	  =				model(**_lowercase ,     output_hidden_states=_lowercase    )
						__A							:   List[Any]	  =				our_output["logits"]
						if classification_head:
								__A							:   int	  =				esm.model.classification_heads["mnli"](esm.extract_features(_lowercase    )    )
						else:
								__A							:   str	  =				esm(hf_tokens["input_ids"] ,     repr_layers=list(range(999    )    )    )
								__A							:   int	  =				their_output["logits"]

				if is_folding_model:
						__A							:   Any	  =				torch.max(torch.abs(our_output["positions"] - their_output["positions"]    )    ).item()
						__A							:   str	  =				torch.allclose(our_output["positions"] ,     their_output["positions"] ,     atol=1E-5    )
				else:
						__A							:   str	  =				torch.max(torch.abs(our_output - their_output    )    ).item()
						__A							:   int	  =				torch.allclose(_lowercase ,     _lowercase ,     atol=1E-5    )

				print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-5
				print("Do both models output the same tensors?" ,     "🔥" if success else "💩"    )

				if not success:
						raise Exception("Something went wRoNg"    )

				if not is_folding_model:
						# Let's check contact prediction too
						__A							:   Any	  =				model.predict_contacts(hf_tokens["input_ids"] ,     hf_tokens["attention_mask"]    )
						__A							:   List[Any]	  =				esm.predict_contacts(hf_tokens["input_ids"]    )
						__A							:   List[Any]	  =				torch.max(torch.abs(our_output - their_output    )    ).item()
						__A							:   Optional[int]	  =				torch.allclose(_lowercase ,     _lowercase ,     atol=1E-5    )

						print("Contact prediction testing:"    )
						print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-5
						print("Do both models output the same tensors?" ,     "🔥" if success else "💩"    )

						if not success:
								raise Exception("Something went wRoNg"    )

				pathlib.Path(_lowercase    ).mkdir(parents=_lowercase ,     exist_ok=_lowercase    )
				print(F"Saving model to {pytorch_dump_folder_path}"    )
				model.save_pretrained(_lowercase    )

				del esm  # Free up some memory before continuing

		print(F"Saving tokenizer to {pytorch_dump_folder_path}"    )
		hf_tokenizer.save_pretrained(_lowercase    )

		if push_to_repo:
				model.push_to_hub(repo_id=_lowercase ,     use_auth_token=_lowercase    )
				hf_tokenizer.push_to_hub(repo_id=_lowercase ,     use_auth_token=_lowercase    )


if __name__ == "__main__":
					UpperCamelCase      			=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_dump_folder_path', type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
					)
					parser.add_argument('--model', default=None, type=str, required=True, help='Name of model to convert.')
					parser.add_argument('--push_to_repo', type=str, help='Repo to upload to (including username!).')
					parser.add_argument('--auth_token', type=str, help='HuggingFace auth token.')
					UpperCamelCase      			=     parser.parse_args()
					convert_esm_checkpoint_to_pytorch(
					    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
					)






import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase      			=     128
UpperCamelCase      			=     256



def    lowerCamelCase_   (  _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				message.encode("ascii"    )

		__A							:   str	  =				[]
		for block_start in range(0 ,     len(_lowercase    ) ,     _lowercase    ):
				__A							:   str	  =				0
				for i in range(_lowercase ,     min(block_start + block_size ,     len(_lowercase    )    )    ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(_lowercase    )
		return block_ints



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	str:
		__A							:   list[str]	  =				[]
		for block_int in block_ints:
				__A							:   list[str]	  =				[]
				for i in range(block_size - 1 ,     -1 ,     -1    ):
						if len(_lowercase    ) + i < message_length:
								__A							:   Optional[Any]	  =				block_int // (BYTE_SIZE**i)
								__A							:   Union[str, Any]	  =				block_int % (BYTE_SIZE**i)
								block_message.insert(0 ,     chr(_lowercase    )    )
				message.extend(_lowercase    )
		return "".join(_lowercase    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE    )							->	list[int]:
		__A							:   List[str]	  =				[]
		__A  ,       __A							:   List[Any]	  =				key

		for block in get_blocks_from_text(_lowercase ,     _lowercase    ):
				encrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return encrypted_blocks



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A							:   Optional[Any]	  =				[]
		__A  ,       __A							:   Dict	  =				key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowercase ,     _lowercase ,     _lowercase    )    )
		return get_text_from_blocks(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	tuple[int, int, int]:
		with open(_lowercase    ) as fo:
				__A							:   Optional[int]	  =				fo.read()
		__A  ,       __A  ,       __A							:   Optional[int]	  =				content.split(","    )
		return (int(_lowercase    ), int(_lowercase    ), int(_lowercase    ))



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = DEFAULT_BLOCK_SIZE ,     )							->	str:
		__A  ,       __A  ,       __A							:   Optional[int]	  =				read_key_file(_lowercase    )
		if key_size < block_size * 8:
				sys.exit(
				    "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
				    "requires the block size to be equal to or greater than the key size. "
				    "Either decrease the block size or use different keys.".format(
				        block_size * 8 ,     _lowercase    )    )

		__A							:   Optional[int]	  =				[str(_lowercase    ) for i in encrypt_message(_lowercase ,     (n, e) ,     _lowercase    )]

		__A							:   int	  =				",".join(_lowercase    )
		__A							:   List[Any]	  =				F"{len(_lowercase    )}_{block_size}_{encrypted_content}"
		with open(_lowercase ,     "w"    ) as fo:
				fo.write(_lowercase    )
		return encrypted_content



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	str:
		__A  ,       __A  ,       __A							:   List[str]	  =				read_key_file(_lowercase    )
		with open(_lowercase    ) as fo:
				__A							:   Optional[Any]	  =				fo.read()
		__A  ,       __A  ,       __A							:   int	  =				content.split("_"    )
		__A							:   Tuple	  =				int(_lowercase    )
		__A							:   Any	  =				int(_lowercase    )

		if key_size < block_size * 8:
				sys.exit(
				    "ERROR: Block size is {} bits and key size is {} bits. The RSA cipher "
				    "requires the block size to be equal to or greater than the key size. "
				    "Did you specify the correct key file and encrypted file?".format(
				        block_size * 8 ,     _lowercase    )    )

		__A							:   Dict	  =				[]
		for block in encrypted_message.split(","    ):
				encrypted_blocks.append(int(_lowercase    )    )

		return decrypt_message(_lowercase ,     _lowercase ,     (n, d) ,     _lowercase    )



def    lowerCamelCase_   (  )							->	None:
		__A							:   int	  =				"encrypted_file.txt"
		__A							:   int	  =				input(r"Encrypt\Decrypt [e\d]: "    )

		if response.lower().startswith("e"    ):
				__A							:   str	  =				"encrypt"
		elif response.lower().startswith("d"    ):
				__A							:   str	  =				"decrypt"

		if mode == "encrypt":
				if not os.path.exists("rsa_pubkey.txt"    ):
						rkg.make_key_files("rsa" ,     1_024    )

				__A							:   Any	  =				input("\nEnter message: "    )
				__A							:   Any	  =				"rsa_pubkey.txt"
				print(F"Encrypting and writing to {filename}..."    )
				__A							:   List[str]	  =				encrypt_and_write_to_file(_lowercase ,     _lowercase ,     _lowercase    )

				print("\nEncrypted text:"    )
				print(_lowercase    )

		elif mode == "decrypt":
				__A							:   Dict	  =				"rsa_privkey.txt"
				print(F"Reading from {filename} and decrypting..."    )
				__A							:   Dict	  =				read_from_file_and_decrypt(_lowercase ,     _lowercase    )
				print("writing decryption to rsa_decryption.txt..."    )
				with open("rsa_decryption.txt" ,     "w"    ) as dec:
						dec.write(_lowercase    )

				print("\nDecryption:"    )
				print(_lowercase    )


if __name__ == "__main__":
					main()





