



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      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       List[str]						,       _lowercase    :       str						,       _lowercase    :       Optional[Any]="attention")							->	Optional[int]:







			"""simple docstring"""
			a__       :						str							=  params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			a__       :						Dict							=  params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			a__       :						Union[str, Any]							=  params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			a__       :						List[Any]							=  params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v

def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       List[str]						,       _lowercase    :       Tuple						,       _lowercase    :       Optional[Any]=False)							->	Any:







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

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

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       Union[str, Any]						,       _lowercase    :       Any						,       _lowercase    :       str)							->	List[Any]:







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

def      lowerCAmelCase_					(  _lowercase    :       dict						,       *, _lowercase    :       int						,       _lowercase    :       bool)							->	Tuple:







			"""simple docstring"""
			a__       :						int							=  traverse_util.flatten_dict(variables["""target"""])
			a__       :						Optional[Any]							=  {"""/""".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__       :						Optional[Any]							=  """encoder/layers_0/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""						,       _lowercase)

			a__       :						int							=  collections.OrderedDict()

			# Shared embeddings.
			a__       :						int							=  old["""token_embedder/embedding"""]

			# Encoder.
			for i in range(_lowercase):
						# Block i, layer 0 (Self Attention).
						a__       :						Optional[Any]							=  tax_layer_norm_lookup(_lowercase						,       _lowercase						,       """encoder"""						,       """pre_attention_layer_norm""")
						a__	,						a__	,						a__	,						a__       :						Union[str, Any]							=  tax_attention_lookup(_lowercase						,       _lowercase						,       """encoder"""						,       """attention""")
						a__       :						Tuple							=  layer_norm
						a__       :						Tuple							=  k.T
						a__       :						Optional[Any]							=  o.T
						a__       :						Dict							=  q.T
						a__       :						Any							=  v.T

						# Block i, layer 1 (MLP).
						a__       :						Union[str, Any]							=  tax_layer_norm_lookup(_lowercase						,       _lowercase						,       """encoder"""						,       """pre_mlp_layer_norm""")
						a__	,						a__       :						Tuple							=  tax_mlp_lookup(_lowercase						,       _lowercase						,       """encoder"""						,       _lowercase)
						a__       :						Optional[int]							=  layer_norm
						if split_mlp_wi:
									a__       :						Union[str, Any]							=  wi[0].T
									a__       :						Optional[Any]							=  wi[1].T
						else:
									a__       :						Union[str, Any]							=  wi.T
						a__       :						List[Any]							=  wo.T

			a__       :						Union[str, Any]							=  old[
			    """encoder/relpos_bias/rel_embedding"""
			].T
			a__       :						Dict							=  old["""encoder/encoder_norm/scale"""]

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

									# Block i, layer 1 (Cross Attention).
									a__       :						Dict							=  tax_layer_norm_lookup(_lowercase						,       _lowercase						,       """decoder"""						,       """pre_cross_attention_layer_norm""")
									a__	,						a__	,						a__	,						a__       :						Optional[Any]							=  tax_attention_lookup(_lowercase						,       _lowercase						,       """decoder"""						,       """encoder_decoder_attention""")
									a__       :						Optional[Any]							=  layer_norm
									a__       :						Optional[int]							=  k.T
									a__       :						Tuple							=  o.T
									a__       :						List[str]							=  q.T
									a__       :						Dict							=  v.T

									# Block i, layer 2 (MLP).
									a__       :						List[str]							=  tax_layer_norm_lookup(_lowercase						,       _lowercase						,       """decoder"""						,       """pre_mlp_layer_norm""")
									a__	,						a__       :						List[str]							=  tax_mlp_lookup(_lowercase						,       _lowercase						,       """decoder"""						,       _lowercase)
									a__       :						Optional[int]							=  layer_norm
									if split_mlp_wi:
												a__       :						Tuple							=  wi[0].T
												a__       :						List[Any]							=  wi[1].T
									else:
												a__       :						List[str]							=  wi.T
									a__       :						Optional[Any]							=  wo.T

						a__       :						List[Any]							=  old["""decoder/decoder_norm/scale"""]
						a__       :						int							=  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__       :						Optional[Any]							=  old["""decoder/logits_dense/kernel"""].T

			return new

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       bool)							->	str:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  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__       :						Any							=  state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									a__       :						Dict							=  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__       :						str							=  state_dict["""shared.weight"""]

			return state_dict

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			a__       :						int							=  checkpoints.load_tax_checkpoint(_lowercase)
			a__       :						Any							=  convert_tax_to_pytorch(_lowercase						,       num_layers=config.num_layers						,       is_encoder_only=_lowercase)
			a__       :						List[Any]							=  make_state_dict(_lowercase						,       _lowercase)
			model.load_state_dict(_lowercase						,       strict=_lowercase)

def      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       List[Any]						,       _lowercase    :       Tuple						,       _lowercase    :       bool = False)							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Any							=  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__       :						Optional[Any]							=  TaEncoderModel(_lowercase)
			else:
						a__       :						Tuple							=  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		:				List[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		:				Optional[Any]      		=parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)









_lowercase		:				Optional[int]      		=[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
			a__       :						List[Any]							=  0
			a__       :						Dict							=  0
			while place < len(_lowercase):
						if (place + 1 < len(_lowercase)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def      lowerCAmelCase_					(  _lowercase    :       int)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			for arabic, roman in ROMAN:
						((a__)	,						(a__))       :						Optional[Any]							=  divmod(_lowercase						,       _lowercase)
						result.append(roman * factor)
						if number == 0:
									break
			return "".join(_lowercase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




