


'''simple docstring'''



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
_lowercase      :      str            =					{
    """/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 				lowerCamelCase__				(	A       :      int					):




						'''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


_lowercase      :      Optional[Any]            =					{
    """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 				lowerCamelCase__				(	A       :      Optional[int]				,       A       :      List[Any]					):




						'''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 				lowerCamelCase__				(	A       :      Tuple				,       A       :      List[str]				,       A       :      Any=None				,       A       :      List[str]="./"				,       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__":
	_lowercase      :      Union[str, Any]            =					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""")
	_lowercase      :      str            =					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,
	)









'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowercase      :      str            =					logging.get_logger(__name__)

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

_lowercase      :      Tuple            =					{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_lowercase      :      Union[str, Any]            =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	VOCAB_FILES_NAMES
				__magic_name__    :				str  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				List[Any]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				List[Any]  		=	GPTaTokenizer




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Tuple="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Optional[int]=False				,   **lowerCAmelCase      :			Tuple				,   )->					int:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     kwargs.pop('''add_bos_token'''				,   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




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Union[str, Any]				,   **lowerCAmelCase      :			Dict				)->					BatchEncoding:

										"""simple docstring"""




										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 					a__(		self      :			List[str]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			Tuple				)->					BatchEncoding:

										"""simple docstring"""




										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 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				) + [self.eos_token_id]				)

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





