






'''simple docstring'''


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 	__magic_name__( _A						,      _A						,      _A						,      _A="attention"					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      _A						,      _A						,      _A=False					):
						'''simple docstring'''





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

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







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





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







def 	__magic_name__( _A						,      *, _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = traverse_util.flatten_dict(variables["""target"""]					)
						UpperCamelCase__         = {'/'.join(SCREAMING_SNAKE_CASE_					): v for k, v in old.items()}

						# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
						UpperCamelCase__         = 'encoder/layers_0/mlp/wi_0/kernel' in old
						print("""Split MLP:"""						,      SCREAMING_SNAKE_CASE_					)

						UpperCamelCase__         = collections.OrderedDict()

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

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

												# Block i, layer 1 (MLP).
												UpperCamelCase__         = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      """encoder"""						,      """pre_mlp_layer_norm"""					)
												UpperCamelCase__         = tax_mlp_lookup(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      """encoder"""						,      SCREAMING_SNAKE_CASE_					)
												UpperCamelCase__         = layer_norm
												if split_mlp_wi:
																		UpperCamelCase__         = wi[0].T
																		UpperCamelCase__         = wi[1].T
												else:
																		UpperCamelCase__         = wi.T
												UpperCamelCase__         = wo.T

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

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

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

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

												UpperCamelCase__         = old['decoder/decoder_norm/scale']
												UpperCamelCase__         = 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:
																		UpperCamelCase__         = old['decoder/logits_dense/kernel'].T

						return new







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = 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:
												UpperCamelCase__         = state_dict['shared.weight']

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

						return state_dict







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_					)
						UpperCamelCase__         = convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_						,      num_layers=config.num_layers						,      is_encoder_only=SCREAMING_SNAKE_CASE_					)
						UpperCamelCase__         = make_state_dict(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_					)
						model.load_state_dict(SCREAMING_SNAKE_CASE_						,      strict=SCREAMING_SNAKE_CASE_					)







def 	__magic_name__( _A						,      _A						,      _A						,      _A = False					):
						'''simple docstring'''





						UpperCamelCase__         = TaConfig.from_json_file(SCREAMING_SNAKE_CASE_					)
						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:
												UpperCamelCase__         = TaEncoderModel(SCREAMING_SNAKE_CASE_					)
						else:
												UpperCamelCase__         = TaForConditionalGeneration(SCREAMING_SNAKE_CASE_					)

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

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

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


if __name__ == "__main__":
					lowerCamelCase_				: Tuple    	=							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
					)
					lowerCamelCase_				: Dict    	=							parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)








'''simple docstring'''


from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowerCamelCase_				: List[Any]    	=							{
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
					from .config import (
					    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
					    OnnxConfig,
					    OnnxConfigWithPast,
					    OnnxSeqaSeqConfigWithPast,
					    PatchingSpec,
					)
					from .convert import export, validate_model_outputs
					from .features import FeaturesManager
					from .utils import ParameterFormat, compute_serialized_parameters_size

else:
					import sys

					lowerCamelCase_				: List[Any]    	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
