





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCamelCase__           =			{
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}





def      _UpperCamelCase  (a__ :Union[str, Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      list(s_dict.keys()							)
							for key in keys:
														UpperCamelCase__					  =      r""".*/layers_(\d+)"""
														UpperCamelCase__					  =      key
														if re.match(a__ ,      a__							):
																					UpperCamelCase__					  =      re.sub(r"""layers_(\d+)""" ,      r"""block/\1/layer""" ,      a__							)

														UpperCamelCase__					  =      r"""(encoder|decoder)\/"""

														if re.match(a__ ,      a__							):
																					UpperCamelCase__					  =      re.match(a__ ,      a__							).groups()
																					if groups[0] == "encoder":
																												UpperCamelCase__					  =      re.sub(r"""/mlp/""" ,      r"""/1/mlp/""" ,      a__							)
																												UpperCamelCase__					  =      re.sub(r"""/pre_mlp_layer_norm/""" ,      r"""/1/layer_norm/""" ,      a__							)

																					elif groups[0] == "decoder":
																												UpperCamelCase__					  =      re.sub(r"""/mlp/""" ,      r"""/2/mlp/""" ,      a__							)
																												UpperCamelCase__					  =      re.sub(r"""/pre_mlp_layer_norm/""" ,      r"""/2/layer_norm/""" ,      a__							)

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												UpperCamelCase__					  =      new_key.replace(a__ ,      a__							)

														print(f"""{key} -> {new_key}"""							)
														UpperCamelCase__					  =      s_dict.pop(a__							)

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														UpperCamelCase__					  =      s_dict[
														    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														UpperCamelCase__					  =      s_dict[
														    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()							):
														if "expert" in key:
																					UpperCamelCase__					  =      s_dict[key].shape[0]
																					UpperCamelCase__					  =      s_dict[key]
																					for idx in range(a__							):
																												UpperCamelCase__					  =      expert_weihts[idx]
																												print(f"""{key} -> {key.replace("expert/" ,      "nested fstring"							)}"""							)

																					s_dict.pop(a__							)

							return s_dict


UpperCamelCase__           =			{
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :List[str]							):






							"""simple docstring"""





							import regex as re

							with open(a__ ,      """r"""							) as f:
														UpperCamelCase__					  =      f.read()

							UpperCamelCase__					  =      re.findall(r"""(.*) = ([0-9.]*)""" ,      a__							)
							UpperCamelCase__					  =      {}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					UpperCamelCase__					  =      float(a__							) if """.""" in value else int(a__							)

							UpperCamelCase__					  =      re.findall(r"""(.*activations) = \(\'(.*)\',\)""" ,      a__							)[0]
							UpperCamelCase__					  =      str(activation[1]							)

							UpperCamelCase__					  =      num_experts
							UpperCamelCase__					  =      SwitchTransformersConfig(**a__							)
							return config





def      _UpperCamelCase  (a__ :Tuple ,      a__ :List[str] ,      a__ :Optional[Any]=None ,      a__ :Optional[int]="./" ,      a__ :str=8							):






							"""simple docstring"""





							print(f"""Loading flax weights from : {flax_checkpoint_path}"""							)
							UpperCamelCase__					  =      checkpoints.load_tax_checkpoint(a__							)

							if gin_file is not None:
														UpperCamelCase__					  =      convert_gin_to_config(a__ ,      a__							)
							else:
														UpperCamelCase__					  =      SwitchTransformersConfig.from_pretrained(a__							)

							UpperCamelCase__					  =      SwitchTransformersForConditionalGeneration(a__							)

							UpperCamelCase__					  =      flax_params["""target"""]
							UpperCamelCase__					  =      flatten_dict(a__ ,      sep="""/"""							)
							UpperCamelCase__					  =      rename_keys(a__							)
							UpperCamelCase__					  =      unflatten_dict(a__ ,      sep="""/"""							)

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(a__ ,      a__							)

							print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
							pt_model.save_pretrained(a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--switch_t5x_checkpoint_path",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
					        " model architecture. If not provided, a `gin_file` has to be provided."
					    ),
					)
					parser.add_argument(
					    "--gin_file",
					    default=None,
					    type=str,
					    required=False,
					    help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
					)
					parser.add_argument(
					    "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
					)
					parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
					UpperCamelCase__           =			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(
					    args.switch_tax_checkpoint_path,
					    args.config_name,
					    args.gin_file,
					    args.pytorch_dump_folder_path,
					    args.num_experts,
					)











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__           =			{
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__           =			{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							VOCAB_FILES_NAMES
							snake_case  :   Optional[Any]      =							PRETRAINED_VOCAB_FILES_MAP
							snake_case  :   Any      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case  :   Dict      =							["""input_ids""", """attention_mask"""]
							snake_case  :   List[Any]      =							RobertaTokenizer






							def __init__(    self	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase="replace"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="</s>"	,			__lowerCAmelCase="<s>"	,			__lowerCAmelCase="<unk>"	,			__lowerCAmelCase="<pad>"	,			__lowerCAmelCase="<mask>"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(
														    __lowerCAmelCase	,			__lowerCAmelCase	,			tokenizer_file=__lowerCAmelCase	,			errors=__lowerCAmelCase	,			bos_token=__lowerCAmelCase	,			eos_token=__lowerCAmelCase	,			sep_token=__lowerCAmelCase	,			cls_token=__lowerCAmelCase	,			unk_token=__lowerCAmelCase	,			pad_token=__lowerCAmelCase	,			mask_token=__lowerCAmelCase	,			add_prefix_space=__lowerCAmelCase	,			trim_offsets=__lowerCAmelCase	,			**__lowerCAmelCase	,			)

														UpperCamelCase__					  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
														if pre_tok_state.get("""add_prefix_space"""	,			__lowerCAmelCase	) != add_prefix_space:
																					UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			pre_tok_state.pop("""type"""	)	)
																					UpperCamelCase__					  =      add_prefix_space
																					UpperCamelCase__					  =      pre_tok_class(**__lowerCAmelCase	)

														UpperCamelCase__					  =      add_prefix_space

														UpperCamelCase__					  =      """post_processor"""
														UpperCamelCase__					  =      getattr(self.backend_tokenizer	,			__lowerCAmelCase	,			__lowerCAmelCase	)
														if tokenizer_component_instance:
																					UpperCamelCase__					  =      json.loads(tokenizer_component_instance.__getstate__()	)

																					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																					if "sep" in state:
																												UpperCamelCase__					  =      tuple(state["""sep"""]	)
																					if "cls" in state:
																												UpperCamelCase__					  =      tuple(state["""cls"""]	)

																					UpperCamelCase__					  =      False

																					if state.get("""add_prefix_space"""	,			__lowerCAmelCase	) != add_prefix_space:
																												UpperCamelCase__					  =      add_prefix_space
																												UpperCamelCase__					  =      True

																					if state.get("""trim_offsets"""	,			__lowerCAmelCase	) != trim_offsets:
																												UpperCamelCase__					  =      trim_offsets
																												UpperCamelCase__					  =      True

																					if changes_to_apply:
																												UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			state.pop("""type"""	)	)
																												UpperCamelCase__					  =      component_class(**__lowerCAmelCase	)
																												setattr(self.backend_tokenizer	,			__lowerCAmelCase	,			__lowerCAmelCase	)






							@property
							def 					_lowerCamelCase		(    self	):
														if self._mask_token is None:
																					if self.verbose:
																												logger.error("""Using mask_token, but it is not set yet."""	)
																					return None
														return str(self._mask_token	)






							@mask_token.setter
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      AddedToken(__lowerCAmelCase	,			lstrip=__lowerCAmelCase	,			rstrip=__lowerCAmelCase	) if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	) else value
														UpperCamelCase__					  =      value






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      kwargs.get("""is_split_into_words"""	,			__lowerCAmelCase	)
														assert self.add_prefix_space or not is_split_into_words, (
														    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    "to use it with pretokenized inputs."
														)

														return super()._batch_encode_plus(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      kwargs.get("""is_split_into_words"""	,			__lowerCAmelCase	)

														assert self.add_prefix_space or not is_split_into_words, (
														    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
														    "to use it with pretokenized inputs."
														)

														return super()._encode_plus(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      self._tokenizer.model.save(__lowerCAmelCase	,			name=__lowerCAmelCase	)
														return tuple(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	):
														UpperCamelCase__					  =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
														if token_ids_a is None:
																					return output

														return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase = None	):
														UpperCamelCase__					  =      [self.sep_token_id]
														UpperCamelCase__					  =      [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]




