




'''simple docstring'''





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_(	snake_case							:       Optional[int]							,				snake_case							:       str							,				snake_case							:       Dict     ):

							'''simple docstring'''






							return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       List[Any]							,				snake_case							:       Union[str, Any]							,				snake_case							:       str="attention"     ):

							'''simple docstring'''






							snake_case_						=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]     )
							snake_case_						=      k_tmp.reshape(k_tmp.shape[0]							,				k_tmp.shape[1] * k_tmp.shape[2]     )
							snake_case_						=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]     )
							snake_case_						=      o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,				o_tmp.shape[2]     )
							snake_case_						=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]     )
							snake_case_						=      q_tmp.reshape(q_tmp.shape[0]							,				q_tmp.shape[1] * q_tmp.shape[2]     )
							snake_case_						=      np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]     )
							snake_case_						=      v_tmp.reshape(v_tmp.shape[0]							,				v_tmp.shape[1] * v_tmp.shape[2]     )
							return k, o, q, v


def      UpperCamelCase_(	snake_case							:       Dict							,				snake_case							:       str							,				snake_case							:       Dict							,				snake_case							:       Optional[Any]=False     ):

							'''simple docstring'''






							if split_mlp_wi:
														snake_case_						=      params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
														snake_case_						=      params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
														snake_case_						=      (wi_a, wi_a)
							else:
														snake_case_						=      params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

							snake_case_						=      params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
							return wi, wo


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       Optional[int]							,				snake_case							:       str							,				snake_case							:       List[Any]     ):

							'''simple docstring'''






							return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]


def      UpperCamelCase_(	snake_case							:       dict							,				*, snake_case							:       int							,				snake_case							:       bool							,				snake_case							:       bool = False     ):

							'''simple docstring'''






							snake_case_						=      traverse_util.flatten_dict(variables["target"]     )
							snake_case_						=      {"""/""".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
							snake_case_						=      """encoder/encoder/mlp/wi_0/kernel""" in old
							print("Split MLP:"							,				lowercase_     )

							snake_case_						=      collections.OrderedDict()

							# Shared embeddings.
							snake_case_						=      old["""token_embedder/embedding"""]

							# Encoder.
							for i in range(lowercase_     ):
														# Block i, layer 0 (Self Attention).
														snake_case_						=      tax_layer_norm_lookup(lowercase_							,				lowercase_							,				"encoder"							,				"pre_attention_layer_norm"     )
														snake_case_						=      tax_attention_lookup(lowercase_							,				lowercase_							,				"encoder"							,				"attention"     )
														snake_case_						=      layer_norm
														snake_case_						=      k.T
														snake_case_						=      o.T
														snake_case_						=      q.T
														snake_case_						=      v.T

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

							snake_case_						=      old["""encoder/encoder_norm/scale"""]

							if not scalable_attention:
														snake_case_						=      tax_relpos_bias_lookup(
														    lowercase_							,				0							,				"encoder"     ).T
														snake_case_						=      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).
																					snake_case_						=      tax_layer_norm_lookup(lowercase_							,				lowercase_							,				"decoder"							,				"pre_self_attention_layer_norm"     )
																					snake_case_						=      tax_attention_lookup(lowercase_							,				lowercase_							,				"decoder"							,				"self_attention"     )
																					snake_case_						=      layer_norm
																					snake_case_						=      k.T
																					snake_case_						=      o.T
																					snake_case_						=      q.T
																					snake_case_						=      v.T

																					# Block i, layer 1 (Cross Attention).
																					snake_case_						=      tax_layer_norm_lookup(lowercase_							,				lowercase_							,				"decoder"							,				"pre_cross_attention_layer_norm"     )
																					snake_case_						=      tax_attention_lookup(lowercase_							,				lowercase_							,				"decoder"							,				"encoder_decoder_attention"     )
																					snake_case_						=      layer_norm
																					snake_case_						=      k.T
																					snake_case_						=      o.T
																					snake_case_						=      q.T
																					snake_case_						=      v.T

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

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

														snake_case_						=      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:
																					snake_case_						=      old["""decoder/logits_dense/kernel"""].T

							return new


def      UpperCamelCase_(	snake_case							:       Any							,				snake_case							:       bool     ):

							'''simple docstring'''






							snake_case_						=      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:
														snake_case_						=      state_dict["""shared.weight"""]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					snake_case_						=      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."     )
																					snake_case_						=      state_dict["""shared.weight"""]

							return state_dict


def      UpperCamelCase_(	snake_case							:       Optional[Any]							,				snake_case							:       int							,				snake_case							:       Optional[Any]							,				snake_case							:       Union[str, Any]							,				snake_case							:       Optional[Any]     ):

							'''simple docstring'''






							snake_case_						=      checkpoints.load_tax_checkpoint(lowercase_     )
							snake_case_						=      convert_tax_to_pytorch(
							    lowercase_							,				num_layers=config.num_layers							,				is_encoder_only=lowercase_							,				scalable_attention=lowercase_     )
							snake_case_						=      make_state_dict(lowercase_							,				lowercase_     )
							model.load_state_dict(lowercase_							,				strict=lowercase_     )


def      UpperCamelCase_(	snake_case							:       int							,				snake_case							:       List[Any]							,				snake_case							:       Tuple							,				snake_case							:       bool = False							,				snake_case							:       bool = False							,				):

							'''simple docstring'''






							snake_case_						=      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:
														snake_case_						=      UMTaEncoderModel(lowercase_     )
							else:
														snake_case_						=      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__":
		_SCREAMING_SNAKE_CASE							:   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,
		)
		_SCREAMING_SNAKE_CASE							:   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,
		)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_       : int         =			logging.get_logger(__name__)

A_       : Optional[Any]         =			{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       '''mgp-str'''






					def __init__( self    ,       A__=[32, 128]    ,       A__=4    ,       A__=3    ,       A__=27    ,       A__=38    ,       A__=5_0257    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=4.0    ,       A__=True    ,       A__=False    ,       A__=1e-5    ,       A__=0.0    ,       A__=0.0    ,       A__=0.0    ,       A__=False    ,       A__=0.0_2    ,       **A__    ,       ):
						super().__init__(**A__			)

						A__							:    Dict        =		image_size
						A__							:    int        =		patch_size
						A__							:    Dict        =		num_channels
						A__							:    List[Any]        =		max_token_length
						A__							:    str        =		num_character_labels
						A__							:    Tuple        =		num_bpe_labels
						A__							:    Optional[Any]        =		num_wordpiece_labels
						A__							:    Optional[int]        =		hidden_size
						A__							:    Tuple        =		num_hidden_layers
						A__							:    Any        =		num_attention_heads
						A__							:    List[Any]        =		mlp_ratio
						A__							:    Tuple        =		distilled
						A__							:    Union[str, Any]        =		layer_norm_eps
						A__							:    Tuple        =		drop_rate
						A__							:    List[str]        =		qkv_bias
						A__							:    Optional[Any]        =		attn_drop_rate
						A__							:    Union[str, Any]        =		drop_path_rate
						A__							:    Optional[Any]        =		output_aa_attentions
						A__							:    Optional[int]        =		initializer_range



