

'''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				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					List[str]   )      -> Dict:
							return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					str		,lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Dict="attention"   )      -> str:
							SCREAMING_SNAKE_CASE_				=				SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				k_tmp.reshape(k_tmp.shape[0]		,k_tmp.shape[1] * k_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,o_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				q_tmp.reshape(q_tmp.shape[0]		,q_tmp.shape[1] * q_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				v_tmp.reshape(v_tmp.shape[0]		,v_tmp.shape[1] * v_tmp.shape[2]   )
							return k, o, q, v




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					List[Any]=False   )      -> Optional[Any]:
							if split_mlp_wi:
														SCREAMING_SNAKE_CASE_				=				params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
														SCREAMING_SNAKE_CASE_				=				params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
														SCREAMING_SNAKE_CASE_				=				(wi_a, wi_a)
							else:
														SCREAMING_SNAKE_CASE_				=				params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

							SCREAMING_SNAKE_CASE_				=				params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
							return wi, wo




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Any   )      -> Optional[Any]:
							return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]




def 							_UpperCamelCase				(   lowerCAmelCase__:					dict		,*, lowerCAmelCase__:					int		,lowerCAmelCase__:					bool		,lowerCAmelCase__:					bool = False   )      -> List[Any]:
							SCREAMING_SNAKE_CASE_				=				traverse_util.flatten_dict(variables['target']   )
							SCREAMING_SNAKE_CASE_				=				{'/'.join(lowerCAmelCase__   ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							SCREAMING_SNAKE_CASE_				=				'encoder/encoder/mlp/wi_0/kernel' in old
							print('Split MLP:'		,lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				collections.OrderedDict()

							# Shared embeddings.
							SCREAMING_SNAKE_CASE_				=				old['token_embedder/embedding']

							# Encoder.
							for i in range(lowerCAmelCase__   ):
														# Block i, layer 0 (Self Attention).
														SCREAMING_SNAKE_CASE_				=				tax_layer_norm_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'encoder'		,'pre_attention_layer_norm'   )
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				tax_attention_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'encoder'		,'attention'   )
														SCREAMING_SNAKE_CASE_				=				layer_norm
														SCREAMING_SNAKE_CASE_				=				k.T
														SCREAMING_SNAKE_CASE_				=				o.T
														SCREAMING_SNAKE_CASE_				=				q.T
														SCREAMING_SNAKE_CASE_				=				v.T

														# Block i, layer 1 (MLP).
														SCREAMING_SNAKE_CASE_				=				tax_layer_norm_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'encoder'		,'pre_mlp_layer_norm'   )
														SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				tax_mlp_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'encoder'		,lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				layer_norm
														if split_mlp_wi:
																					SCREAMING_SNAKE_CASE_				=				wi[0].T
																					SCREAMING_SNAKE_CASE_				=				wi[1].T
														else:
																					SCREAMING_SNAKE_CASE_				=				wi.T
														SCREAMING_SNAKE_CASE_				=				wo.T
														if scalable_attention:
																					# convert the rel_embedding of each layer
																					SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(
																					    lowerCAmelCase__		,lowerCAmelCase__		,'encoder'   ).T

							SCREAMING_SNAKE_CASE_				=				old['encoder/encoder_norm/scale']

							if not scalable_attention:
														SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(
														    lowerCAmelCase__		,0		,'encoder'   ).T
														SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(
														    lowerCAmelCase__		,0		,'decoder'   ).T

							if not is_encoder_only:
														# Decoder.
														for i in range(lowerCAmelCase__   ):
																					# Block i, layer 0 (Self Attention).
																					SCREAMING_SNAKE_CASE_				=				tax_layer_norm_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,'pre_self_attention_layer_norm'   )
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				tax_attention_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,'self_attention'   )
																					SCREAMING_SNAKE_CASE_				=				layer_norm
																					SCREAMING_SNAKE_CASE_				=				k.T
																					SCREAMING_SNAKE_CASE_				=				o.T
																					SCREAMING_SNAKE_CASE_				=				q.T
																					SCREAMING_SNAKE_CASE_				=				v.T

																					# Block i, layer 1 (Cross Attention).
																					SCREAMING_SNAKE_CASE_				=				tax_layer_norm_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,'pre_cross_attention_layer_norm'   )
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				tax_attention_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,'encoder_decoder_attention'   )
																					SCREAMING_SNAKE_CASE_				=				layer_norm
																					SCREAMING_SNAKE_CASE_				=				k.T
																					SCREAMING_SNAKE_CASE_				=				o.T
																					SCREAMING_SNAKE_CASE_				=				q.T
																					SCREAMING_SNAKE_CASE_				=				v.T

																					# Block i, layer 2 (MLP).
																					SCREAMING_SNAKE_CASE_				=				tax_layer_norm_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,'pre_mlp_layer_norm'   )
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				tax_mlp_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'		,lowerCAmelCase__   )
																					SCREAMING_SNAKE_CASE_				=				layer_norm
																					if split_mlp_wi:
																												SCREAMING_SNAKE_CASE_				=				wi[0].T
																												SCREAMING_SNAKE_CASE_				=				wi[1].T
																					else:
																												SCREAMING_SNAKE_CASE_				=				wi.T
																					SCREAMING_SNAKE_CASE_				=				wo.T

																					if scalable_attention:
																												# convert the rel_embedding of each layer
																												SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(lowerCAmelCase__		,lowerCAmelCase__		,'decoder'   ).T

														SCREAMING_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:
																					SCREAMING_SNAKE_CASE_				=				old['decoder/logits_dense/kernel'].T

							return new




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					bool   )      -> Tuple:
							SCREAMING_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:
														SCREAMING_SNAKE_CASE_				=				state_dict['shared.weight']

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

							return state_dict




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					str   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				checkpoints.load_tax_checkpoint(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				convert_tax_to_pytorch(
							    lowerCAmelCase__		,num_layers=config.num_layers		,is_encoder_only=lowerCAmelCase__		,scalable_attention=lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				make_state_dict(lowerCAmelCase__		,lowerCAmelCase__   )
							model.load_state_dict(lowerCAmelCase__		,strict=lowerCAmelCase__   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					str		,lowerCAmelCase__:					bool = False		,lowerCAmelCase__:					bool = False		,)      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				MTaConfig.from_json_file(lowerCAmelCase__   )
							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:
														SCREAMING_SNAKE_CASE_				=				UMTaEncoderModel(lowerCAmelCase__   )
							else:
														SCREAMING_SNAKE_CASE_				=				UMTaForConditionalGeneration(lowerCAmelCase__   )

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(lowerCAmelCase__   )
							print('Done'   )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				int     	=			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							:				int     	=			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,
							)








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							:				List[Any]     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Tuple     	=			{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """roberta"""



				def __init__(    self,     _lowercase=50265,     _lowercase=768,     _lowercase=12,     _lowercase=12,     _lowercase=3072,     _lowercase="gelu",     _lowercase=0.1,     _lowercase=0.1,     _lowercase=512,     _lowercase=2,     _lowercase=0.02,     _lowercase=1E-12,     _lowercase=1,     _lowercase=0,     _lowercase=2,     _lowercase="absolute",     _lowercase=True,     _lowercase=None,     **_lowercase,     )     ->							Dict:
											super().__init__(pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     **_lowercase       )

											SCREAMING_SNAKE_CASE_				=				vocab_size
											SCREAMING_SNAKE_CASE_				=				hidden_size
											SCREAMING_SNAKE_CASE_				=				num_hidden_layers
											SCREAMING_SNAKE_CASE_				=				num_attention_heads
											SCREAMING_SNAKE_CASE_				=				hidden_act
											SCREAMING_SNAKE_CASE_				=				intermediate_size
											SCREAMING_SNAKE_CASE_				=				hidden_dropout_prob
											SCREAMING_SNAKE_CASE_				=				attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_				=				max_position_embeddings
											SCREAMING_SNAKE_CASE_				=				type_vocab_size
											SCREAMING_SNAKE_CASE_				=				initializer_range
											SCREAMING_SNAKE_CASE_				=				layer_norm_eps
											SCREAMING_SNAKE_CASE_				=				position_embedding_type
											SCREAMING_SNAKE_CASE_				=				use_cache
											SCREAMING_SNAKE_CASE_				=				classifier_dropout






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		SCREAMING_SNAKE_CASE_				=				{0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )





