





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[Any]:
		return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case="attention"							)		->    Union[str, Any]:
		__lowercase											=     __lowercase											=     np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]							)
		__lowercase											=     k_tmp.reshape(k_tmp.shape[0]						,   k_tmp.shape[1] * k_tmp.shape[2]							)
		__lowercase											=     np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]							)
		__lowercase											=     o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,   o_tmp.shape[2]							)
		__lowercase											=     np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]							)
		__lowercase											=     q_tmp.reshape(q_tmp.shape[0]						,   q_tmp.shape[1] * q_tmp.shape[2]							)
		__lowercase											=     np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]							)
		__lowercase											=     v_tmp.reshape(v_tmp.shape[0]						,   v_tmp.shape[1] * v_tmp.shape[2]							)
		return k, o, q, v



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Optional[Any]:
		if split_mlp_wi:
				__lowercase											=     params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
				__lowercase											=     params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
				__lowercase											=     (wi_a, wi_a)
		else:
				__lowercase											=     params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    List[Any]:
		return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   *, snake_case						,   snake_case						,   snake_case = False							)		->    str:
		__lowercase											=     traverse_util.flatten_dict(variables['target']							)
		__lowercase											=     {'/'.join(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
		__lowercase											=     'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'						,   snake_case							)

		__lowercase											=     collections.OrderedDict()

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

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

				# Block i, layer 1 (MLP).
				__lowercase											=     tax_layer_norm_lookup(snake_case						,   snake_case						,   'encoder'						,   'pre_mlp_layer_norm'							)
				__lowercase						,      __lowercase											=     tax_mlp_lookup(snake_case						,   snake_case						,   'encoder'						,   snake_case							)
				__lowercase											=     layer_norm
				if split_mlp_wi:
						__lowercase											=     wi[0].T
						__lowercase											=     wi[1].T
				else:
						__lowercase											=     wi.T
				__lowercase											=     wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						__lowercase											=     tax_relpos_bias_lookup(
						    snake_case						,   snake_case						,   'encoder'							).T

		__lowercase											=     old['encoder/encoder_norm/scale']

		if not scalable_attention:
				__lowercase											=     tax_relpos_bias_lookup(
				    snake_case						,   0						,   'encoder'							).T
				__lowercase											=     tax_relpos_bias_lookup(
				    snake_case						,   0						,   'decoder'							).T

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

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

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

						if scalable_attention:
								# convert the rel_embedding of each layer
								__lowercase											=     tax_relpos_bias_lookup(snake_case						,   snake_case						,   'decoder'							).T

				__lowercase											=     old['decoder/decoder_norm/scale']

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

		return new



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    str:
		__lowercase											=     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:
				__lowercase											=     state_dict['shared.weight']

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

		return state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    Any:
		__lowercase											=     checkpoints.load_tax_checkpoint(snake_case							)
		__lowercase											=     convert_tax_to_pytorch(
		    snake_case						,   num_layers=config.num_layers						,   is_encoder_only=snake_case						,   scalable_attention=snake_case							)
		__lowercase											=     make_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case						,   strict=snake_case							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case = False						,   snake_case = False						,   )		->    List[Any]:
		__lowercase											=     MTaConfig.from_json_file(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:
				__lowercase											=     UMTaEncoderModel(snake_case							)
		else:
				__lowercase											=     UMTaForConditionalGeneration(snake_case							)

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

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

		# Verify that we can load the checkpoint.
		model.from_pretrained(snake_case							)
		print('Done'							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      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
		)
		parser.add_argument(
		    '''--scalable_attention''',
		    action='''store_true''',
		    help='''Whether the model uses scaled attention (umt5 model)''',
		    default=False,
		)
		SCREAMING_SNAKE_CASE_   :      str			=   parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path,
		    args.config_file,
		    args.pytorch_dump_path,
		    args.is_encoder_only,
		    args.scalable_attention,
		)











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = False							)		->    str:

		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected string as input, found {type(snake_case							)}"
				raise ValueError(snake_case							)
		if not isinstance(snake_case						,   snake_case							):
				__lowercase											=     F"Expected boolean as use_pascal parameter, found {type(snake_case							)}"
				raise ValueError(snake_case							)

		__lowercase											=     input_str.split('_'							)

		__lowercase											=     0 if use_pascal else 1

		__lowercase											=     words[start_index:]

		__lowercase											=     [word[0].upper() + word[1:] for word in words_to_capitalize]

		__lowercase											=     '' if use_pascal else words[0]

		return "".join([initial_word, *capitalized_words]							)


if __name__ == "__main__":
		from doctest import testmod

		testmod()




