
'''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 lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase="attention"				)     ->							Dict:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
					lowerCamelCase_					:      Any     =		params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
					lowerCamelCase_					:      str     =		params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
					lowerCamelCase_					:      int     =		params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
					return k, o, q, v




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=False				)     ->							int:
					'''simple docstring'''

					if split_mlp_wi:
										lowerCamelCase_					:      Optional[int]     =		params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
										lowerCamelCase_					:      Tuple     =		params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
										lowerCamelCase_					:      str     =		(wi_a, wi_a)
					else:
										lowerCamelCase_					:      List[Any]     =		params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

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




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							int:
					'''simple docstring'''

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




def lowercase_	(   _lowercase			,       *, _lowercase			,       _lowercase				)     ->							Dict:
					'''simple docstring'''

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

					lowerCamelCase_					:      Dict     =		collections.OrderedDict()

					# Shared embeddings.
					lowerCamelCase_					:      Optional[Any]     =		old['''token_embedder/embedding''']

					# Encoder.
					for i in range(_lowercase				):
										# Block i, layer 0 (Self Attention).
										lowerCamelCase_					:      List[str]     =		tax_layer_norm_lookup(_lowercase			,       _lowercase			,       '''encoder'''			,       '''pre_attention_layer_norm'''				)
										lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      List[Any]     =		tax_attention_lookup(_lowercase			,       _lowercase			,       '''encoder'''			,       '''attention'''				)
										lowerCamelCase_					:      Optional[Any]     =		layer_norm
										lowerCamelCase_					:      int     =		k.T
										lowerCamelCase_					:      Optional[Any]     =		o.T
										lowerCamelCase_					:      List[Any]     =		q.T
										lowerCamelCase_					:      List[str]     =		v.T

										# Block i, layer 1 (MLP).
										lowerCamelCase_					:      Optional[Any]     =		tax_layer_norm_lookup(_lowercase			,       _lowercase			,       '''encoder'''			,       '''pre_mlp_layer_norm'''				)
										lowerCamelCase_,      lowerCamelCase_					:      Union[str, Any]     =		tax_mlp_lookup(_lowercase			,       _lowercase			,       '''encoder'''			,       _lowercase				)
										lowerCamelCase_					:      Dict     =		layer_norm
										if split_mlp_wi:
															lowerCamelCase_					:      int     =		wi[0].T
															lowerCamelCase_					:      int     =		wi[1].T
										else:
															lowerCamelCase_					:      List[Any]     =		wi.T
										lowerCamelCase_					:      Optional[int]     =		wo.T

					lowerCamelCase_					:      int     =		old[
					    '''encoder/relpos_bias/rel_embedding'''
					].T
					lowerCamelCase_					:      Tuple     =		old['''encoder/encoder_norm/scale''']

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

															# Block i, layer 1 (Cross Attention).
															lowerCamelCase_					:      Dict     =		tax_layer_norm_lookup(_lowercase			,       _lowercase			,       '''decoder'''			,       '''pre_cross_attention_layer_norm'''				)
															lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_					:      Optional[int]     =		tax_attention_lookup(_lowercase			,       _lowercase			,       '''decoder'''			,       '''encoder_decoder_attention'''				)
															lowerCamelCase_					:      Optional[int]     =		layer_norm
															lowerCamelCase_					:      List[Any]     =		k.T
															lowerCamelCase_					:      Optional[Any]     =		o.T
															lowerCamelCase_					:      Optional[Any]     =		q.T
															lowerCamelCase_					:      Optional[int]     =		v.T

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

										lowerCamelCase_					:      Union[str, Any]     =		old['''decoder/decoder_norm/scale''']
										lowerCamelCase_					:      Union[str, Any]     =		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:
															lowerCamelCase_					:      int     =		old['''decoder/logits_dense/kernel'''].T

					return new




def lowercase_	(   _lowercase			,       _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		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:
										lowerCamelCase_					:      Tuple     =		state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															lowerCamelCase_					:      Optional[int]     =		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.'''				)
															lowerCamelCase_					:      Dict     =		state_dict['''shared.weight''']

					return state_dict




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_					:      List[Any]     =		checkpoints.load_tax_checkpoint(_lowercase				)
					lowerCamelCase_					:      Optional[int]     =		convert_tax_to_pytorch(_lowercase			,       num_layers=config.num_layers			,       is_encoder_only=_lowercase				)
					lowerCamelCase_					:      Union[str, Any]     =		make_state_dict(_lowercase			,       _lowercase				)
					model.load_state_dict(_lowercase			,       strict=_lowercase				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = False				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		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:
										lowerCamelCase_					:      str     =		TaEncoderModel(_lowercase				)
					else:
										lowerCamelCase_					:      Optional[Any]     =		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__":
					__lowercase							:   str					=     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
					)
					__lowercase							:   List[Any]					=     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 collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


