



"""simple docstring"""





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple     ):


						'''simple docstring'''






						snake_case_ :			Any            =      os.path.join(args.tf_model_dir       ,			"""parameters.json"""     )
						snake_case_ :			List[str]            =      json.loads(open(__UpperCamelCase     ).read()     )
						if not params:
												raise ValueError(
												    F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'     )
						if not args.output.endswith(""".pt"""     ):
												snake_case_ :			Optional[Any]            =      args.output + """.pt"""
						snake_case_ :			List[Any]            =      OrderedDict()
						with tf.device("""/CPU:0"""     ):
												snake_case_ :			Any            =      tf.train.load_checkpoint(args.tf_model_dir     )
												snake_case_ :			Tuple            =      reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :			Union[str, Any]            =      reader.get_tensor(__UpperCamelCase     ).astype(np.floataa     )
																		if key_name.endswith("""/adam_m"""     ) or key_name.endswith("""/adam_v"""     ):
																								continue
																		if key_name.startswith("""pasts/"""     ):
																								if key_name.startswith("""pasts/mlp"""     ):
																														snake_case_ :			Any            =      int(key_name[9]     )
																								elif key_name.startswith("""pasts/out"""     ):
																														snake_case_ :			Union[str, Any]            =      8
																								snake_case_ :			Optional[Any]            =      """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :			str            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :			Dict            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/moe"""     ):
																								snake_case_ :			Optional[int]            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/switch_gating/kernel"""     ):
																														snake_case_ :			Dict            =      """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :			str            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/softmlp/kernel"""     ):
																														snake_case_ :			Tuple            =      """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :			Dict            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			List[str]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/wo/kernel"""     ) or key_name.endswith("""/wi/kernel"""     ):
																														snake_case_ :			List[Any]            =      key_name[-9:-7]
																														for i in range(1_6     ):
																																				snake_case_ :			int            =      """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :			Union[str, Any]            =      (
																																				    vnp[i].transpose([1, 0]     ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/mlp"""     ):
																								snake_case_ :			int            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/p1/kernel"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :			List[str]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Optional[int]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p1/bias"""     ):
																														snake_case_ :			str            =      """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :			Optional[int]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Any            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p2/kernel"""     ):
																														snake_case_ :			Any            =      """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :			Union[str, Any]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p2/bias"""     ):
																														snake_case_ :			List[Any]            =      """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :			List[str]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/ln"""     ):
																								snake_case_ :			Optional[Any]            =      int(key_name[8:].split("""/"""     )[0]     )
																								if key_name.endswith("""/b"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :			Tuple            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/g"""     ):
																														snake_case_ :			List[Any]            =      """model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :			Dict            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[int]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/att"""     ):
																								snake_case_ :			List[Any]            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/qkv/kernel"""     ):
																														snake_case_ :			Tuple            =      vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :			str            =      state[:, 0, :, :]
																														snake_case_ :			str            =      state[:, 1, :, :]
																														snake_case_ :			Optional[Any]            =      state[:, 2, :, :]
																														snake_case_ :			Any            =      (
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]     )
																														    .transpose([1, 0]     )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			str            =      (
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]     )
																														    .transpose([1, 0]     )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			List[str]            =      (
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]     )
																														    .transpose([1, 0]     )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			str            =      """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																														snake_case_ :			Dict            =      """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :			int            =      torch.tensor(__UpperCamelCase     )
																														snake_case_ :			Tuple            =      """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/o/kernel"""     ):
																														snake_case_ :			List[str]            =      """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :			Dict            =      (
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]     ).transpose([1, 0]     ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/an"""     ):
																								snake_case_ :			Optional[int]            =      int(key_name[8:].split("""/"""     )[0]     )
																								if key_name.endswith("""/b"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :			Optional[Any]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/g"""     ):
																														snake_case_ :			str            =      """model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :			str            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Dict            =      torch.tensor(__UpperCamelCase     )
																		elif (
																		    key_name.startswith("""model/wte"""     )
																		    or key_name.startswith("""model/wpe"""     )
																		    or key_name.startswith("""model/ete"""     )
																		):
																								snake_case_ :			Optional[int]            =      {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :			Union[str, Any]            =      """model.%s.weight""" % nlayer
																								snake_case_ :			Optional[Any]            =      vnp.copy()  # same in embedded
																								snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																								if key_name.startswith("""model/wte"""     ):
																														snake_case_ :			Optional[int]            =      """lm_head.weight"""
																														snake_case_ :			Tuple            =      vnp.copy()  # same in embedded
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/wob"""     ):
																								snake_case_ :			List[str]            =      """final_logits_bias"""
																								snake_case_ :			Optional[int]            =      vnp.copy()  # same in embedded
																								snake_case_ :			List[str]            =      state.reshape((1, -1)     )
																								snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																		elif key_name == "model/dense/kernel":
																								snake_case_ :			Tuple            =      """model.last_project.weight"""
																								snake_case_ :			List[str]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :			Tuple            =      """model.last_project.bias"""
																								snake_case_ :			Tuple            =      vnp.copy()  # same because it is one dimensional
																								snake_case_ :			int            =      torch.tensor(__UpperCamelCase     )
						torch.save(__UpperCamelCase       ,			args.output     )


if __name__ == "__main__":
					__lowerCAmelCase							:       Dict          =       argparse.ArgumentParser(
					    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
					)
					parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
					parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
					__lowerCAmelCase							:       Dict          =       parser.parse_args()
					convert_tf_gptsan_to_pt(args)



"""simple docstring"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = False							):
          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            "You should not supply a second sequence if the provided sequence of "
                            "ids is already formatted with special tokens for the model."							)

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


