




"""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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__="attention"		):
				_lowerCamelCase     :			List[str]    =    params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				_lowerCamelCase     :			int    =    params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				_lowerCamelCase     :			Optional[Any]    =    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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__=False		):
				if split_mlp_wi:
								_lowerCamelCase     :			str    =    params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								_lowerCamelCase     :			Dict    =    params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								_lowerCamelCase     :			Union[str, Any]    =    (wi_a, wi_a)
				else:
								_lowerCamelCase     :			List[Any]    =    params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

				_lowerCamelCase     :			str    =    params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']







def      _snake_case			(				lowercase__							,    *, lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    traverse_util.flatten_dict(variables['target']		)
				_lowerCamelCase     :			Tuple    =    {'/'.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[int]    =    'encoder/layers_0/mlp/wi_0/kernel' in old
				print('Split MLP:'							,    __lowercase		)

				_lowerCamelCase     :			Optional[Any]    =    collections.OrderedDict()

				# Shared embeddings.
				_lowerCamelCase     :			List[str]    =    old['token_embedder/embedding']

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

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

				_lowerCamelCase     :			List[Any]    =    old[
				    'encoder/relpos_bias/rel_embedding'
				].T
				_lowerCamelCase     :			int    =    old['encoder/encoder_norm/scale']

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

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

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

								_lowerCamelCase     :			Any    =    old['decoder/decoder_norm/scale']
								_lowerCamelCase     :			str    =    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     :			List[str]    =    old['decoder/logits_dense/kernel'].T

				return new







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			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:
								_lowerCamelCase     :			str    =    state_dict['shared.weight']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												_lowerCamelCase     :			List[str]    =    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     :			List[str]    =    state_dict['shared.weight']

				return state_dict







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			List[Any]    =    checkpoints.load_tax_checkpoint(__lowercase		)
				_lowerCamelCase     :			Dict    =    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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = False		):
				_lowerCamelCase     :			List[str]    =    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     :			Optional[Any]    =    TaEncoderModel(__lowercase		)
				else:
								_lowerCamelCase     :			Union[str, 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__     						=						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__     						=						parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)

class    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	def __init__(  self	:    List[str]			,  lowercase	:    list[int]	):


				'''simple docstring'''


				_snake_case		=			len(lowercase	)
				_snake_case		=			[0] * len_array

				if len_array > 0:
							_snake_case		=			array[0]

				for i in range(1			,  lowercase	):
							_snake_case		=			self.prefix_sum[i - 1] + array[i]



	def  A							(  self	:    Optional[Any]			,  lowercase	:    int			,  lowercase	:    int	):


				'''simple docstring'''


				if start == 0:
							return self.prefix_sum[end]

				return self.prefix_sum[end] - self.prefix_sum[start - 1]





	def  A							(  self	:    Union[str, Any]			,  lowercase	:    int	):


				'''simple docstring'''


				_snake_case		=			{0}
				for sum_item in self.prefix_sum:
							if sum_item - target_sum in sums:
										return True

							sums.add(lowercase	)

				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()