






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase="attention" ):

					'''simple docstring'''







					_A								= params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					_A								= params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					_A								= params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					_A								= params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=False ):

					'''simple docstring'''







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

					_A								= params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
					return wi, wo




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]




def      __A					(     _lowercase					,			*, _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= traverse_util.flatten_dict(variables['''target'''] )
					_A								= {'''/'''.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								= '''encoder/layers_0/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''					,			_lowercase )

					_A								= collections.OrderedDict()

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

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

										# Block i, layer 1 (MLP).
										_A								= tax_layer_norm_lookup(_lowercase					,			_lowercase					,			'''encoder'''					,			'''pre_mlp_layer_norm''' )
										_A							,_A								= tax_mlp_lookup(_lowercase					,			_lowercase					,			'''encoder'''					,			_lowercase )
										_A								= layer_norm
										if split_mlp_wi:
															_A								= wi[0].T
															_A								= wi[1].T
										else:
															_A								= wi.T
										_A								= wo.T

					_A								= old[
					    '''encoder/relpos_bias/rel_embedding'''
					].T
					_A								= old['''encoder/encoder_norm/scale''']

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

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

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

										_A								= old['''decoder/decoder_norm/scale''']
										_A								= old[
										    '''decoder/relpos_bias/rel_embedding'''
										].T

										# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
										if "decoder/logits_dense/kernel" in old:
															_A								= old['''decoder/logits_dense/kernel'''].T

					return new




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= 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								= state_dict['''shared.weight''']

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

					return state_dict




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= checkpoints.load_tax_checkpoint(_lowercase )
					_A								= convert_tax_to_pytorch(_lowercase					,			num_layers=config.num_layers					,			is_encoder_only=_lowercase )
					_A								= make_state_dict(_lowercase					,			_lowercase )
					model.load_state_dict(_lowercase					,			strict=_lowercase )




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase = False ):

					'''simple docstring'''







					_A								= TaConfig.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								= TaEncoderModel(_lowercase )
					else:
										_A								= TaForConditionalGeneration(_lowercase )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(_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      =				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
				)
				__A      =				parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)











import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__A      =				logging.get_logger(__name__)





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   int		,     __A:   Optional[int]		,     __A:   Optional[Any]   )    ->				str:
												_A								= question_encoder
												_A								= generator
												_A								= self.question_encoder

							def       __A (  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Dict:
												if os.path.isfile(__A   ):
																	raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
												os.makedirs(__A		,     exist_ok=__A   )
												_A								= os.path.join(__A		,     '''question_encoder_tokenizer'''   )
												_A								= os.path.join(__A		,     '''generator_tokenizer'''   )
												self.question_encoder.save_pretrained(__A   )
												self.generator.save_pretrained(__A   )

							@classmethod
							def       __A (  cls:   Optional[Any]		,     __A:   List[str]		,     **__A:   int   )    ->				Any:
												# dynamically import AutoTokenizer
												from ..auto.tokenization_auto import AutoTokenizer

												_A								= kwargs.pop('''config'''		,     __A   )

												if config is None:
																	_A								= RagConfig.from_pretrained(__A   )

												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.question_encoder		,     subfolder='''question_encoder_tokenizer'''   )
												_A								= AutoTokenizer.from_pretrained(
												    __A		,     config=config.generator		,     subfolder='''generator_tokenizer'''   )
												return cls(question_encoder=__A		,     generator=__A   )

							def __call__(  self:   int		,     *__A:   Optional[int]		,     **__A:   List[str]   )    ->				int:
												return self.current_tokenizer(*__A		,     **__A   )

							def       __A (  self:   Dict		,     *__A:   List[str]		,     **__A:   List[str]   )    ->				Dict:
												return self.generator.batch_decode(*__A		,     **__A   )

							def       __A (  self:   Union[str, Any]		,     *__A:   Tuple		,     **__A:   List[str]   )    ->				Tuple:
												return self.generator.decode(*__A		,     **__A   )

							def       __A (  self:   Dict   )    ->				List[str]:
												_A								= self.question_encoder

							def       __A (  self:   Union[str, Any]   )    ->				int:
												_A								= self.generator



							def       __A (  self:   Dict		,     __A:   List[str]		,     __A:   Optional[List[str]] = None		,     __A:   Optional[int] = None		,     __A:   Optional[int] = None		,     __A:   str = "longest"		,     __A:   str = None		,     __A:   bool = True		,     **__A:   Tuple		,     )    ->				BatchEncoding:
												warnings.warn(
												    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
												    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
												    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
												    '''details'''		,     __A		,     )
												if max_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    __A		,     add_special_tokens=__A		,     return_tensors=__A		,     max_length=__A		,     padding=__A		,     truncation=__A		,     **__A		,     )
												if tgt_texts is None:
																	return model_inputs
												# Process tgt_texts
												if max_target_length is None:
																	_A								= self.current_tokenizer.model_max_length
												_A								= self(
												    text_target=__A		,     add_special_tokens=__A		,     return_tensors=__A		,     padding=__A		,     max_length=__A		,     truncation=__A		,     **__A		,     )
												_A								= labels['''input_ids''']
												return model_inputs



