

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__					: Optional[Any]								=      logging.get_logger(__name__)

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

UpperCamelCase__					: Tuple								=      {
    '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__					: Any								=      list("ARNDCQEGHILKMFPSTWYV")

UpperCamelCase__					: Optional[int]								=      restypes + ['X']
UpperCamelCase__					: List[str]								=      restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']


def    _UpperCAmelCase    (					):





				"""simple docstring"""


				with TemporaryDirectory() as tempdir:
								SCREAMING_SNAKE_CASE_													=  '\n'.join(_SCREAMING_SNAKE_CASE    )
								SCREAMING_SNAKE_CASE_													=  Path(_SCREAMING_SNAKE_CASE    ) / 'vocab.txt'
								vocab_file.write_text(_SCREAMING_SNAKE_CASE    )
								SCREAMING_SNAKE_CASE_													=  EsmTokenizer(vocab_file=str(_SCREAMING_SNAKE_CASE    )    )
				SCREAMING_SNAKE_CASE_													=  0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Optional[Any]	,				_SCREAMING_SNAKE_CASE   :						Optional[int]    ):





				"""simple docstring"""


				SCREAMING_SNAKE_CASE_													=  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    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Dict	,				_SCREAMING_SNAKE_CASE   :						List[Any]	,				_SCREAMING_SNAKE_CASE   :						Tuple	,				_SCREAMING_SNAKE_CASE   :						str	,				_SCREAMING_SNAKE_CASE   :						List[str]    ):





				"""simple docstring"""


				if model.startswith('esmfold'    ):
								SCREAMING_SNAKE_CASE_													=  MODEL_MAPPING[model]()
				else:
								SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

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

				if is_folding_model:
								SCREAMING_SNAKE_CASE_													=  esm.esm.alphabet
				SCREAMING_SNAKE_CASE_													=  tuple(alphabet.all_toks    )
				SCREAMING_SNAKE_CASE_													=  alphabet.mask_idx
				SCREAMING_SNAKE_CASE_													=  alphabet.padding_idx

				if is_folding_model:
								SCREAMING_SNAKE_CASE_													=  esm.esm
				else:
								SCREAMING_SNAKE_CASE_													=  esm

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

				if model.startswith('esmfold'    ):
								SCREAMING_SNAKE_CASE_													=  EsmForProteinFolding
				elif classification_head:
								SCREAMING_SNAKE_CASE_													=  EsmForSequenceClassification
				else:
								SCREAMING_SNAKE_CASE_													=  EsmForMaskedLM
				SCREAMING_SNAKE_CASE_													=  model_class(_SCREAMING_SNAKE_CASE    )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				SCREAMING_SNAKE_CASE_													=  original_esm_model.embed_tokens.weight
				if position_embedding_type == "absolute":
								SCREAMING_SNAKE_CASE_													=  original_esm_model.embed_positions.weight

				if config.emb_layer_norm_before:
								SCREAMING_SNAKE_CASE_													=  original_esm_model.emb_layer_norm_before.weight
								SCREAMING_SNAKE_CASE_													=  original_esm_model.emb_layer_norm_before.bias

				SCREAMING_SNAKE_CASE_													=  original_esm_model.emb_layer_norm_after.weight
				SCREAMING_SNAKE_CASE_													=  original_esm_model.emb_layer_norm_after.bias

				for i in range(config.num_hidden_layers    ):
								# Encoder: start of layer
								SCREAMING_SNAKE_CASE_													=  model.esm.encoder.layer[i]
								# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
								SCREAMING_SNAKE_CASE_													=  original_esm_model.layers[i]

								# self attention
								SCREAMING_SNAKE_CASE_													=  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)    )
								)

								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.q_proj.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.q_proj.bias
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.k_proj.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.k_proj.bias
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.v_proj.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn	,				'rot_emb'	,				_SCREAMING_SNAKE_CASE    ) 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.
												SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn_layer_norm.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn_layer_norm.bias
								SCREAMING_SNAKE_CASE_													=  esm_layer.final_layer_norm.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.final_layer_norm.bias

								# self-attention output
								SCREAMING_SNAKE_CASE_													=  layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.out_proj.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.self_attn.out_proj.bias

								# intermediate
								SCREAMING_SNAKE_CASE_													=  layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								SCREAMING_SNAKE_CASE_													=  esm_layer.fca.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.fca.bias

								# output
								SCREAMING_SNAKE_CASE_													=  layer.output
								assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
								SCREAMING_SNAKE_CASE_													=  esm_layer.fca.weight
								SCREAMING_SNAKE_CASE_													=  esm_layer.fca.bias
								# end of layer

				if is_folding_model:
								SCREAMING_SNAKE_CASE_													=  esm.esm_s_combine.data
								SCREAMING_SNAKE_CASE_													=  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:
								SCREAMING_SNAKE_CASE_													=  esm.esm.classification_heads['mnli'].dense.weight
								SCREAMING_SNAKE_CASE_													=  esm.classification_heads['mnli'].dense.bias
								SCREAMING_SNAKE_CASE_													=  esm.classification_heads['mnli'].out_proj.weight
								SCREAMING_SNAKE_CASE_													=  esm.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								SCREAMING_SNAKE_CASE_													=  esm.lm_head.dense.weight
								SCREAMING_SNAKE_CASE_													=  esm.lm_head.dense.bias
								SCREAMING_SNAKE_CASE_													=  esm.lm_head.layer_norm.weight
								SCREAMING_SNAKE_CASE_													=  esm.lm_head.layer_norm.bias
								SCREAMING_SNAKE_CASE_													=  esm.lm_head.weight
								SCREAMING_SNAKE_CASE_													=  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.
								SCREAMING_SNAKE_CASE_													=  SAMPLE_DATA[:2]
				else:
								SCREAMING_SNAKE_CASE_													=  SAMPLE_DATA

				if is_folding_model:
								SCREAMING_SNAKE_CASE_													=  get_esmfold_tokenizer()
								SCREAMING_SNAKE_CASE_													=  hf_tokenizer(
								    [row[1] for row in sample_data]	,				return_tensors='pt'	,				padding=_SCREAMING_SNAKE_CASE	,				add_special_tokens=_SCREAMING_SNAKE_CASE    )
								SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  esmfold_encode_sequences([row[1] for row in sample_data]    )
								SCREAMING_SNAKE_CASE_													=  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.
								SCREAMING_SNAKE_CASE_													=  alphabet.get_batch_converter()
								SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_													=  batch_converter(_SCREAMING_SNAKE_CASE    )
								# Prepare tokenizer and make sure it matches
								with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE_													=  '\n'.join(alphabet.all_toks    )
												SCREAMING_SNAKE_CASE_													=  Path(_SCREAMING_SNAKE_CASE    ) / 'vocab.txt'
												vocab_file.write_text(_SCREAMING_SNAKE_CASE    )
												SCREAMING_SNAKE_CASE_													=  EsmTokenizer(vocab_file=str(_SCREAMING_SNAKE_CASE    )    )

								SCREAMING_SNAKE_CASE_													=  hf_tokenizer([row[1] for row in sample_data]	,				return_tensors='pt'	,				padding=_SCREAMING_SNAKE_CASE    )
								SCREAMING_SNAKE_CASE_													=  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.
												SCREAMING_SNAKE_CASE_													=  esm.cuda().infer([row[1] for row in sample_data]    )
												SCREAMING_SNAKE_CASE_													=  model.cuda()(
												    input_ids=hf_tokens['input_ids'].cuda()	,				attention_mask=hf_tokens['attention_mask'].cuda()    )
								else:
												SCREAMING_SNAKE_CASE_													=  model(**_SCREAMING_SNAKE_CASE	,				output_hidden_states=_SCREAMING_SNAKE_CASE    )
												SCREAMING_SNAKE_CASE_													=  our_output['logits']
												if classification_head:
																SCREAMING_SNAKE_CASE_													=  esm.model.classification_heads['mnli'](esm.extract_features(_SCREAMING_SNAKE_CASE    )    )
												else:
																SCREAMING_SNAKE_CASE_													=  esm(hf_tokens['input_ids']	,				repr_layers=list(range(999    )    )    )
																SCREAMING_SNAKE_CASE_													=  their_output['logits']

								if is_folding_model:
												SCREAMING_SNAKE_CASE_													=  torch.max(torch.abs(our_output['positions'] - their_output['positions']    )    ).item()
												SCREAMING_SNAKE_CASE_													=  torch.allclose(our_output['positions']	,				their_output['positions']	,				atol=1E-5    )
								else:
												SCREAMING_SNAKE_CASE_													=  torch.max(torch.abs(our_output - their_output    )    ).item()
												SCREAMING_SNAKE_CASE_													=  torch.allclose(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				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
												SCREAMING_SNAKE_CASE_													=  model.predict_contacts(hf_tokens['input_ids']	,				hf_tokens['attention_mask']    )
												SCREAMING_SNAKE_CASE_													=  esm.predict_contacts(hf_tokens['input_ids']    )
												SCREAMING_SNAKE_CASE_													=  torch.max(torch.abs(our_output - their_output    )    ).item()
												SCREAMING_SNAKE_CASE_													=  torch.allclose(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				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(_SCREAMING_SNAKE_CASE    ).mkdir(parents=_SCREAMING_SNAKE_CASE	,				exist_ok=_SCREAMING_SNAKE_CASE    )
								print(f"""Saving model to {pytorch_dump_folder_path}"""    )
								model.save_pretrained(_SCREAMING_SNAKE_CASE    )

								del esm  # Free up some memory before continuing

				print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""    )
				hf_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE    )

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


if __name__ == "__main__":
							UpperCamelCase__					: Union[str, Any]								=      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__					: int								=      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
							)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						decoder_start_token_id=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


