






"""simple docstring"""



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()
_lowerCAmelCase  :  List[Any]   =			logging.get_logger(__name__)

_lowerCAmelCase  :  Union[str, Any]   =			[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

_lowerCAmelCase  :  List[Any]   =			{
    """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,
}

_lowerCAmelCase  :  Dict   =			list("""ARNDCQEGHILKMFPSTWYV""")

_lowerCAmelCase  :  str   =			restypes + ["""X"""]
_lowerCAmelCase  :  Union[str, Any]   =			restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]


def   SCREAMING_SNAKE_CASE__					(     )->		Optional[int]:






     '''simple docstring'''



     with TemporaryDirectory() as tempdir:
          UpperCAmelCase__	:						Dict       =       "\n".join(snake_case							)
          UpperCAmelCase__	:						Optional[int]       =       Path(snake_case							) / "vocab.txt"
          vocab_file.write_text(snake_case							)
          UpperCAmelCase__	:						Any       =       EsmTokenizer(vocab_file=str(snake_case							)							)
     UpperCAmelCase__	:						int       =       0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[Any]       =       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   SCREAMING_SNAKE_CASE__					(     snake_case					:   str ,				snake_case					:   str ,				snake_case					:   bool ,				snake_case					:   str ,				snake_case					:   str							)->		List[Any]:






     '''simple docstring'''



     if model.startswith("esmfold"							):
          UpperCAmelCase__	:						List[str]       =       MODEL_MAPPING[model]()
     else:
          UpperCAmelCase__       ,							UpperCAmelCase__	:						Optional[int]       =       MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

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

     if is_folding_model:
          UpperCAmelCase__	:						Any       =       esm.esm.alphabet
     UpperCAmelCase__	:						List[str]       =       tuple(alphabet.all_toks							)
     UpperCAmelCase__	:						Optional[Any]       =       alphabet.mask_idx
     UpperCAmelCase__	:						Union[str, Any]       =       alphabet.padding_idx

     if is_folding_model:
          UpperCAmelCase__	:						str       =       esm.esm
     else:
          UpperCAmelCase__	:						Optional[Any]       =       esm

     UpperCAmelCase__	:						Union[str, Any]       =       EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings ,				mask_token_id=snake_case ,				hidden_size=snake_case ,				num_hidden_layers=snake_case ,				num_attention_heads=snake_case ,				intermediate_size=snake_case ,				max_position_embeddings=1026 ,				layer_norm_eps=1E-5 ,				attention_probs_dropout_prob=0.0 ,				hidden_dropout_prob=0.0 ,				pad_token_id=snake_case ,				emb_layer_norm_before=snake_case ,				token_dropout=snake_case ,				position_embedding_type=snake_case ,				is_folding_model=snake_case ,				esmfold_config=snake_case ,				vocab_list=snake_case ,				)
     if classification_head:
          UpperCAmelCase__	:						Any       =       esm.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our ESM config:" ,				snake_case							)

     if model.startswith("esmfold"							):
          UpperCAmelCase__	:						Optional[int]       =       EsmForProteinFolding
     elif classification_head:
          UpperCAmelCase__	:						int       =       EsmForSequenceClassification
     else:
          UpperCAmelCase__	:						Optional[int]       =       EsmForMaskedLM
     UpperCAmelCase__	:						List[Any]       =       model_class(snake_case							)
     model.eval()

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

     if config.emb_layer_norm_before:
          UpperCAmelCase__	:						Optional[int]       =       original_esm_model.emb_layer_norm_before.weight
          UpperCAmelCase__	:						str       =       original_esm_model.emb_layer_norm_before.bias

     UpperCAmelCase__	:						Any       =       original_esm_model.emb_layer_norm_after.weight
     UpperCAmelCase__	:						Optional[int]       =       original_esm_model.emb_layer_norm_after.bias

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

          # self attention
          UpperCAmelCase__	:						EsmSelfAttention       =       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)							)
          )

          UpperCAmelCase__	:						List[str]       =       esm_layer.self_attn.q_proj.weight
          UpperCAmelCase__	:						int       =       esm_layer.self_attn.q_proj.bias
          UpperCAmelCase__	:						int       =       esm_layer.self_attn.k_proj.weight
          UpperCAmelCase__	:						Optional[Any]       =       esm_layer.self_attn.k_proj.bias
          UpperCAmelCase__	:						List[str]       =       esm_layer.self_attn.v_proj.weight
          UpperCAmelCase__	:						Union[str, Any]       =       esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn ,				"rot_emb" ,				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.
               UpperCAmelCase__	:						Optional[Any]       =       esm_layer.self_attn.rot_emb.inv_freq

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

          # self-attention output
          UpperCAmelCase__	:						EsmSelfOutput       =       layer.attention.output
          assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase__	:						Tuple       =       esm_layer.self_attn.out_proj.weight
          UpperCAmelCase__	:						Any       =       esm_layer.self_attn.out_proj.bias

          # intermediate
          UpperCAmelCase__	:						EsmIntermediate       =       layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          UpperCAmelCase__	:						str       =       esm_layer.fca.weight
          UpperCAmelCase__	:						int       =       esm_layer.fca.bias

          # output
          UpperCAmelCase__	:						EsmOutput       =       layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          UpperCAmelCase__	:						List[str]       =       esm_layer.fca.weight
          UpperCAmelCase__	:						List[Any]       =       esm_layer.fca.bias
          # end of layer

     if is_folding_model:
          UpperCAmelCase__	:						List[Any]       =       esm.esm_s_combine.data
          UpperCAmelCase__	:						int       =       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:
          UpperCAmelCase__	:						Tuple       =       esm.esm.classification_heads["mnli"].dense.weight
          UpperCAmelCase__	:						Tuple       =       esm.classification_heads["mnli"].dense.bias
          UpperCAmelCase__	:						List[str]       =       esm.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase__	:						Optional[Any]       =       esm.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase__	:						Any       =       esm.lm_head.dense.weight
          UpperCAmelCase__	:						int       =       esm.lm_head.dense.bias
          UpperCAmelCase__	:						Union[str, Any]       =       esm.lm_head.layer_norm.weight
          UpperCAmelCase__	:						int       =       esm.lm_head.layer_norm.bias
          UpperCAmelCase__	:						Union[str, Any]       =       esm.lm_head.weight
          UpperCAmelCase__	:						Optional[Any]       =       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.
          UpperCAmelCase__	:						Optional[Any]       =       SAMPLE_DATA[:2]
     else:
          UpperCAmelCase__	:						Any       =       SAMPLE_DATA

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

          UpperCAmelCase__	:						Optional[int]       =       hf_tokenizer([row[1] for row in sample_data] ,				return_tensors="pt" ,				padding=snake_case							)
          UpperCAmelCase__	:						Optional[int]       =       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.
               UpperCAmelCase__	:						Union[str, Any]       =       esm.cuda().infer([row[1] for row in sample_data]							)
               UpperCAmelCase__	:						Dict       =       model.cuda()(
                   input_ids=hf_tokens["input_ids"].cuda() ,				attention_mask=hf_tokens["attention_mask"].cuda()							)
          else:
               UpperCAmelCase__	:						List[Any]       =       model(**snake_case ,				output_hidden_states=snake_case							)
               UpperCAmelCase__	:						int       =       our_output["logits"]
               if classification_head:
                    UpperCAmelCase__	:						List[Any]       =       esm.model.classification_heads["mnli"](esm.extract_features(snake_case							)							)
               else:
                    UpperCAmelCase__	:						Optional[int]       =       esm(hf_tokens["input_ids"] ,				repr_layers=list(range(999							)							)							)
                    UpperCAmelCase__	:						Any       =       their_output["logits"]

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

          del esm  # Free up some memory before continuing

     print(f'Saving tokenizer to {pytorch_dump_folder_path}'							)
     hf_tokenizer.save_pretrained(snake_case							)

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


if __name__ == "__main__":
 _lowerCAmelCase  :  Tuple   =			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.""")
 _lowerCAmelCase  :  Any   =			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
 )










"""simple docstring"""



import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_lowerCAmelCase  :  List[str]   =			"""src/diffusers"""
_lowerCAmelCase  :  Union[str, Any]   =			"""docs/source/en"""
_lowerCAmelCase  :  List[str]   =			"""."""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		str:






     '''simple docstring'''



     with open(snake_case ,				"r" ,				encoding="utf-8" ,				newline="\n"							) as f:
          UpperCAmelCase__	:						Any       =       f.readlines()
     # Find the start prompt.
     UpperCAmelCase__	:						str       =       0
     while not lines[start_index].startswith(snake_case							):
          start_index += 1
     start_index += 1

     UpperCAmelCase__	:						Union[str, Any]       =       start_index
     while not lines[end_index].startswith(snake_case							):
          end_index += 1
     end_index -= 1

     while len(lines[start_index]							) <= 1:
          start_index += 1
     while len(lines[end_index]							) <= 1:
          end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_lowerCAmelCase  :  str   =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase  :  List[Any]   =			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCAmelCase  :  Optional[int]   =			re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowerCAmelCase  :  str   =			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_lowerCAmelCase  :  Any   =			importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_lowerCAmelCase  :  int   =			spec.loader.load_module()


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,				snake_case							)
     return [m.group(0							) for m in matches]


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       2 if text == "✅" or text == "❌" else len(snake_case							)
     UpperCAmelCase__	:						int       =       (width - text_length) // 2
     UpperCAmelCase__	:						Any       =       width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


def   SCREAMING_SNAKE_CASE__					(     )->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__	:						Any       =       {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__	:						Optional[int]       =       {name: config.replace("ConfigMixin" ,				""							) for name, config in model_name_to_config.items()}

     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     UpperCAmelCase__	:						Optional[Any]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Dict       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						List[str]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Tuple       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Any       =       collections.defaultdict(snake_case							)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(snake_case							):
          UpperCAmelCase__	:						Optional[int]       =       None
          if attr_name.endswith("Tokenizer"							):
               UpperCAmelCase__	:						Optional[Any]       =       slow_tokenizers
               UpperCAmelCase__	:						Optional[Any]       =       attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"							):
               UpperCAmelCase__	:						Union[str, Any]       =       fast_tokenizers
               UpperCAmelCase__	:						str       =       attr_name[:-13]
          elif _re_tf_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						List[str]       =       tf_models
               UpperCAmelCase__	:						List[str]       =       _re_tf_models.match(snake_case							).groups()[0]
          elif _re_flax_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						str       =       flax_models
               UpperCAmelCase__	:						Union[str, Any]       =       _re_flax_models.match(snake_case							).groups()[0]
          elif _re_pt_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						int       =       pt_models
               UpperCAmelCase__	:						Optional[Any]       =       _re_pt_models.match(snake_case							).groups()[0]

          if lookup_dict is not None:
               while len(snake_case							) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__	:						int       =       True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__	:						List[str]       =       "".join(camel_case_split(snake_case							)[:-1]							)

    # Let's build that table!
     UpperCAmelCase__	:						int       =       list(model_name_to_config.keys()							)
     model_names.sort(key=str.lower							)
     UpperCAmelCase__	:						Dict       =       ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     UpperCAmelCase__	:						List[str]       =       [len(snake_case							) + 2 for c in columns]
     UpperCAmelCase__	:						Tuple       =       max([len(snake_case							) for name in model_names]							) + 2

     # Build the table per se
     UpperCAmelCase__	:						Any       =       "|" + "|".join([_center_text(snake_case ,				snake_case							) for c, w in zip(snake_case ,				snake_case							)]							) + "|\n"
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]							) + "|\n"

     UpperCAmelCase__	:						str       =       {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__	:						str       =       model_name_to_prefix[name]
          UpperCAmelCase__	:						Dict       =       [
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(snake_case ,				snake_case							) for l, w in zip(snake_case ,				snake_case							)]							) + "|\n"
     return table


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]=False							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       _find_text_in_file(
         filename=os.path.join(snake_case ,				"index.mdx"							) ,				start_prompt="<!--This table is updated automatically from the auto modules" ,				end_prompt="<!-- End table-->" ,				)
     UpperCAmelCase__	:						Union[str, Any]       =       get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(snake_case ,				"index.mdx"							) ,				"w" ,				encoding="utf-8" ,				newline="\n"							) as f:
                    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
          else:
               raise ValueError(
                   "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."							)


if __name__ == "__main__":
 _lowerCAmelCase  :  Union[str, Any]   =			argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 _lowerCAmelCase  :  Dict   =			parser.parse_args()

 check_model_table(args.fix_and_overwrite)


