


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    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		int="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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Any=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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		dict       ,  *, SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		bool	):





			"""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  ,  UpperCamelCase  ,  UpperCamelCase  ,  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  ,  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  ,  UpperCamelCase  ,  UpperCamelCase  ,  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  ,  UpperCamelCase  ,  UpperCamelCase  ,  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  ,  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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		bool	):





			"""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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):





			"""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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		bool = 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__":
			__snake_case            =   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
			)
			__snake_case            =   parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)








def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			return [
			    txt[:a] + txt[a].upper() + txt[a + 1 :]
			    for a in range(len(SCREAMING_SNAKE_CASE_	)	)
			    if txt[a].isalpha()
			]


if __name__ == "__main__":
			__import__("doctest").testmod()




