
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__a         =   logging.get_logger(__name__)


__a         =   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
__a         =   {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__a         =   {
    'allenai/led-base-16384': 1_6_3_8_4,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   (
				    list(range(ord('''!'''   )		,  ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   )		,  ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   )		,  ord('''ÿ'''   ) + 1   )   )
				)
				lowercase_	    =   bs[:]
				lowercase_	    =   0
				for b in range(2**8   ):
								if b not in bs:
												bs.append(snake_case__   )
												cs.append(2**8 + n   )
												n += 1
				lowercase_	    =   [chr(snake_case__   ) for n in cs]
				return dict(zip(snake_case__		,  snake_case__   )   )




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				lowercase_	    =   set()
				lowercase_	    =   word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)   )
								lowercase_	    =   char
				return pairs


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      VOCAB_FILES_NAMES
	a     :List[str]       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Optional[int]       	=      ['input_ids', 'attention_mask']
	def __init__(			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="replace"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="</s>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="</s>"			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     str="<unk>"			,				SCREAMING_SNAKE_CASE_    :     Dict="<pad>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<mask>"			,				SCREAMING_SNAKE_CASE_    :     str=False			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	str:
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else bos_token
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else eos_token
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else sep_token
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else cls_token
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else unk_token
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    errors=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				add_prefix_space=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as vocab_handle:
									lowercase_	    =   json.load(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {v: k for k, v in self.encoder.items()}
					lowercase_	    =   errors  # how to handle errors in decoding
					lowercase_	    =   bytes_to_unicode()
					lowercase_	    =   {v: k for k, v in self.byte_encoder.items()}
					with open(SCREAMING_SNAKE_CASE_			,				encoding='''utf-8'''			) as merges_handle:
									lowercase_	    =   merges_handle.read().split('''\n'''			)[1:-1]
					lowercase_	    =   [tuple(merge.split()			) for merge in bpe_merges]
					lowercase_	    =   dict(zip(SCREAMING_SNAKE_CASE_			,				range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
					lowercase_	    =   {}
					lowercase_	    =   add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					lowercase_	    =   re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''			)
	@property
	# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Any:
					return len(self.encoder			)
	def 		_lowercase    (			self    :     int			)			->	Dict:
					return dict(self.encoder			,				**self.added_tokens_encoder			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[Any]:
					if token in self.cache:
									return self.cache[token]
					lowercase_	    =   tuple(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)

					if not pairs:
									return token

					while True:
									lowercase_	    =   min(SCREAMING_SNAKE_CASE_			,				key=lambda SCREAMING_SNAKE_CASE_			: self.bpe_ranks.get(SCREAMING_SNAKE_CASE_			,				float('''inf'''			)			)			)
									if bigram not in self.bpe_ranks:
													break
									lowercase_    ,      lowercase_	    =   bigram
									lowercase_	    =   []
									lowercase_	    =   0
									while i < len(SCREAMING_SNAKE_CASE_			):
													try:
																	lowercase_	    =   word.index(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													except ValueError:
																	new_word.extend(word[i:]			)
																	break
													else:
																	new_word.extend(word[i:j]			)
																	lowercase_	    =   j

													if word[i] == first and i < len(SCREAMING_SNAKE_CASE_			) - 1 and word[i + 1] == second:
																	new_word.append(first + second			)
																	i += 2
													else:
																	new_word.append(word[i]			)
																	i += 1
									lowercase_	    =   tuple(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   new_word
									if len(SCREAMING_SNAKE_CASE_			) == 1:
													break
									else:
													lowercase_	    =   get_pairs(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   ''' '''.join(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   word
					return word
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	str:
					lowercase_	    =   []
					for token in re.findall(self.pat			,				SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   ''''''.join(
									    self.byte_encoder[b] for b in token.encode('''utf-8'''			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_			).split(''' '''			)			)
					return bpe_tokens
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	str:
					return self.encoder.get(SCREAMING_SNAKE_CASE_			,				self.encoder.get(self.unk_token			)			)
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	List[str]:
					return self.decoder.get(SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Dict:
					lowercase_	    =   ''''''.join(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8'''			,				errors=self.errors			)
					return text
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as f:
									f.write(json.dumps(self.encoder			,				indent=2			,				sort_keys=SCREAMING_SNAKE_CASE_			,				ensure_ascii=SCREAMING_SNAKE_CASE_			) + '''\n'''			)

					lowercase_	    =   0
					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as writer:
									writer.write('''#version: 0.2\n'''			)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,				key=lambda SCREAMING_SNAKE_CASE_			: kv[1]			):
													if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																	    ''' Please check that the tokenizer is not corrupted!'''			)
																	lowercase_	    =   token_index
													writer.write(''' '''.join(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
													index += 1

					return vocab_file, merge_file
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			)			->	List[int]:
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=SCREAMING_SNAKE_CASE_			,				token_ids_a=SCREAMING_SNAKE_CASE_			,				already_has_special_tokens=SCREAMING_SNAKE_CASE_			)

					if token_ids_a is None:
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep			) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					lowercase_	    =   kwargs.pop('''add_prefix_space'''			,				self.add_prefix_space			)
					if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_			) > 0 and not text[0].isspace()):
									lowercase_	    =   ''' ''' + text
					return (text, kwargs)




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[Dict[str, EncodedInput], BatchEncoding]			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     PaddingStrategy = PaddingStrategy.DO_NOT_PAD			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[bool] = None			,				)			->	dict:
					lowercase_	    =   super()._pad(
					    encoded_inputs=SCREAMING_SNAKE_CASE_			,				max_length=SCREAMING_SNAKE_CASE_			,				padding_strategy=SCREAMING_SNAKE_CASE_			,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_			,				return_attention_mask=SCREAMING_SNAKE_CASE_			,				)

					# Load from model defaults
					if return_attention_mask is None:
									lowercase_	    =   '''attention_mask''' in self.model_input_names

					if return_attention_mask and "global_attention_mask" in encoded_inputs:
									lowercase_	    =   encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									lowercase_	    =   len(encoded_inputs['''global_attention_mask''']			) != len(SCREAMING_SNAKE_CASE_			)

									if needs_to_be_padded:
													lowercase_	    =   len(SCREAMING_SNAKE_CASE_			) - len(encoded_inputs['''global_attention_mask''']			)

													if self.padding_side == "right":
																	# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																	lowercase_	    =   (
																	    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																	)
													elif self.padding_side == "left":
																	lowercase_	    =   [-1] * difference + encoded_inputs[
																	    '''global_attention_mask'''
																	]
													else:
																	raise ValueError('''Invalid padding strategy:''' + str(self.padding_side			)			)

					return encoded_inputs








'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







       lowercase__      =					os.path.join(args.tf_model_dir    ,					'''parameters.json'''			)
       lowercase__      =					json.loads(open(A			).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'''			):
              lowercase__      =					args.output + '''.pt'''
       lowercase__      =					OrderedDict()
       with tf.device('''/CPU:0'''			):
              lowercase__      =					tf.train.load_checkpoint(args.tf_model_dir			)
              lowercase__      =					reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     lowercase__      =					reader.get_tensor(A			).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'''			):
                                   lowercase__      =					int(key_name[9]			)
                            elif key_name.startswith('''pasts/out'''			):
                                   lowercase__      =					8
                            lowercase__      =					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/moe'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/switch_gating/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/softmlp/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/wo/kernel'''			) or key_name.endswith('''/wi/kernel'''			):
                                   lowercase__      =					key_name[-9:-7]
                                   for i in range(16			):
                                          lowercase__      =					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                          lowercase__      =					(
                                              vnp[i].transpose([1, 0]			).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/mlp'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/p1/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p1/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/ln'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/att'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/qkv/kernel'''			):
                                   lowercase__      =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   lowercase__      =					state[:, 0, :, :]
                                   lowercase__      =					state[:, 1, :, :]
                                   lowercase__      =					state[:, 2, :, :]
                                   lowercase__      =					(
                                       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
                                   lowercase__      =					(
                                       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
                                   lowercase__      =					(
                                       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
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/o/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                                   lowercase__      =					(
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/an'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif (
                         key_name.startswith('''model/wte'''			)
                         or key_name.startswith('''model/wpe'''			)
                         or key_name.startswith('''model/ete'''			)
                     ):
                            lowercase__      =					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                                key_name[-3:]
                            ]
                            lowercase__      =					'''model.%s.weight''' % nlayer
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					torch.tensor(A			)
                            if key_name.startswith('''model/wte'''			):
                                   lowercase__      =					'''lm_head.weight'''
                                   lowercase__      =					vnp.copy()  # same in embedded
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/wob'''			):
                            lowercase__      =					'''final_logits_bias'''
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					state.reshape((1, -1)			)
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense/kernel":
                            lowercase__      =					'''model.last_project.weight'''
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense_1/bias":
                            lowercase__      =					'''model.last_project.bias'''
                            lowercase__      =					vnp.copy()  # same because it is one dimensional
                            lowercase__      =					torch.tensor(A			)
       torch.save(A    ,					args.output			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[int]    		=      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)




