

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: str    ,      lowercase_: Dict					)     ->      Optional[int]:
	return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: List[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: str="attention"					)     ->      Optional[int]:
	A__							:    Optional[int]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]					)
	A__							:    Union[str, Any]        =		k_tmp.reshape(k_tmp.shape[0]    ,      k_tmp.shape[1] * k_tmp.shape[2]					)
	A__							:    List[Any]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]					)
	A__							:    Tuple        =		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]    ,      o_tmp.shape[2]					)
	A__							:    Optional[int]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]					)
	A__							:    Any        =		q_tmp.reshape(q_tmp.shape[0]    ,      q_tmp.shape[1] * q_tmp.shape[2]					)
	A__							:    List[Any]        =		np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]					)
	A__							:    List[Any]        =		v_tmp.reshape(v_tmp.shape[0]    ,      v_tmp.shape[1] * v_tmp.shape[2]					)
	return k, o, q, v

def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: str    ,      lowercase_: Dict    ,      lowercase_: Optional[Any]=False					)     ->      Tuple:
	if split_mlp_wi:
		A__							:    Union[str, Any]        =		params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		A__							:    Any        =		params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		A__							:    Optional[int]        =		(wi_a, wi_a)
	else:
		A__							:    Union[str, Any]        =		params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	A__							:    Optional[int]        =		params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: Optional[int]    ,      lowercase_: str    ,      lowercase_: List[Any]					)     ->      str:
	return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]

def 						UpperCamelCase					(lowercase_: dict    ,      *, lowercase_: int    ,      lowercase_: bool    ,      lowercase_: bool = False					)     ->      Optional[Any]:
	A__							:    List[Any]        =		traverse_util.flatten_dict(variables["""target"""]					)
	A__							:    Optional[Any]        =		{"""/""".join(lowercase_					): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	A__							:    Union[str, Any]        =		"""encoder/encoder/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""    ,      lowercase_					)

	A__							:    Optional[int]        =		collections.OrderedDict()

	# Shared embeddings.
	A__							:    int        =		old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(lowercase_					):
		# Block i, layer 0 (Self Attention).
		A__							:    Union[str, Any]        =		tax_layer_norm_lookup(lowercase_    ,      lowercase_    ,      """encoder"""    ,      """pre_attention_layer_norm"""					)
		A__   ,   A__   ,   A__   ,   A__							:    Optional[Any]        =		tax_attention_lookup(lowercase_    ,      lowercase_    ,      """encoder"""    ,      """attention"""					)
		A__							:    Optional[Any]        =		layer_norm
		A__							:    Optional[int]        =		k.T
		A__							:    Dict        =		o.T
		A__							:    Optional[int]        =		q.T
		A__							:    List[Any]        =		v.T

		# Block i, layer 1 (MLP).
		A__							:    List[Any]        =		tax_layer_norm_lookup(lowercase_    ,      lowercase_    ,      """encoder"""    ,      """pre_mlp_layer_norm"""					)
		A__   ,   A__							:    List[Any]        =		tax_mlp_lookup(lowercase_    ,      lowercase_    ,      """encoder"""    ,      lowercase_					)
		A__							:    Optional[Any]        =		layer_norm
		if split_mlp_wi:
			A__							:    int        =		wi[0].T
			A__							:    List[str]        =		wi[1].T
		else:
			A__							:    Union[str, Any]        =		wi.T
		A__							:    List[str]        =		wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			A__							:    Optional[int]        =		tax_relpos_bias_lookup(
			    lowercase_    ,      lowercase_    ,      """encoder"""					).T

	A__							:    int        =		old["""encoder/encoder_norm/scale"""]

	if not scalable_attention:
		A__							:    str        =		tax_relpos_bias_lookup(
		    lowercase_    ,      0    ,      """encoder"""					).T
		A__							:    Tuple        =		tax_relpos_bias_lookup(
		    lowercase_    ,      0    ,      """decoder"""					).T

	if not is_encoder_only:
		# Decoder.
		for i in range(lowercase_					):
			# Block i, layer 0 (Self Attention).
			A__							:    List[str]        =		tax_layer_norm_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      """pre_self_attention_layer_norm"""					)
			A__   ,   A__   ,   A__   ,   A__							:    Any        =		tax_attention_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      """self_attention"""					)
			A__							:    List[str]        =		layer_norm
			A__							:    Optional[int]        =		k.T
			A__							:    str        =		o.T
			A__							:    Optional[Any]        =		q.T
			A__							:    Union[str, Any]        =		v.T

			# Block i, layer 1 (Cross Attention).
			A__							:    Optional[Any]        =		tax_layer_norm_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      """pre_cross_attention_layer_norm"""					)
			A__   ,   A__   ,   A__   ,   A__							:    Union[str, Any]        =		tax_attention_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      """encoder_decoder_attention"""					)
			A__							:    Union[str, Any]        =		layer_norm
			A__							:    Union[str, Any]        =		k.T
			A__							:    List[str]        =		o.T
			A__							:    str        =		q.T
			A__							:    List[Any]        =		v.T

			# Block i, layer 2 (MLP).
			A__							:    List[Any]        =		tax_layer_norm_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      """pre_mlp_layer_norm"""					)
			A__   ,   A__							:    Union[str, Any]        =		tax_mlp_lookup(lowercase_    ,      lowercase_    ,      """decoder"""    ,      lowercase_					)
			A__							:    List[Any]        =		layer_norm
			if split_mlp_wi:
				A__							:    str        =		wi[0].T
				A__							:    str        =		wi[1].T
			else:
				A__							:    Optional[Any]        =		wi.T
			A__							:    List[str]        =		wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				A__							:    int        =		tax_relpos_bias_lookup(lowercase_    ,      lowercase_    ,      """decoder"""					).T

		A__							:    str        =		old["""decoder/decoder_norm/scale"""]

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			A__							:    Tuple        =		old["""decoder/logits_dense/kernel"""].T

	return new

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: bool					)     ->      List[str]:
	A__							:    Any        =		collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		A__							:    Optional[int]        =		state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			A__							:    List[Any]        =		state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""					)
			A__							:    Dict        =		state_dict["""shared.weight"""]

	return state_dict

def 						UpperCamelCase					(lowercase_: Optional[Any]    ,      lowercase_: int    ,      lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	A__							:    Optional[Any]        =		checkpoints.load_tax_checkpoint(lowercase_					)
	A__							:    List[Any]        =		convert_tax_to_pytorch(
	    lowercase_    ,      num_layers=config.num_layers    ,      is_encoder_only=lowercase_    ,      scalable_attention=lowercase_					)
	A__							:    str        =		make_state_dict(lowercase_    ,      lowercase_					)
	model.load_state_dict(lowercase_    ,      strict=lowercase_					)

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: List[Any]    ,      lowercase_: Tuple    ,      lowercase_: bool = False    ,      lowercase_: bool = False    ,      )     ->      Tuple:
	A__							:    str        =		MTaConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		A__							:    Dict        =		UMTaEncoderModel(lowercase_					)
	else:
		A__							:    Optional[int]        =		UMTaForConditionalGeneration(lowercase_					)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	model.save_pretrained(lowercase_					)

	# Verify that we can load the checkpoint.
	model.from_pretrained(lowercase_					)
	print("""Done"""					)


if __name__ == "__main__":
	A_       : List[Any]         =			argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
	# Required parameters
	parser.add_argument(
	    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
	)
	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
	)
	parser.add_argument(
	    '--scalable_attention',
	    action='store_true',
	    help='Whether the model uses scaled attention (umt5 model)',
	    default=False,
	)
	A_       : Dict         =			parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



