"""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_      (      _lowerCamelCase:		Optional[int]  ,      _lowerCamelCase:		Any  ,      _lowerCamelCase:		Any  ,      _lowerCamelCase:		int="attention"					)       ->					Tuple:



							'''simple docstring'''

							__lowerCamelCase    :						Optional[Any]						      =   params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
							__lowerCamelCase    :						List[str]						      =   params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
							__lowerCamelCase    :						Dict						      =   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_      (      _lowerCamelCase:		int  ,      _lowerCamelCase:		Any  ,      _lowerCamelCase:		int  ,      _lowerCamelCase:		Tuple=False					)       ->					Any:



							'''simple docstring'''

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

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


def       lowercase_      (      _lowerCamelCase:		Union[str, Any]  ,      _lowerCamelCase:		Dict  ,      _lowerCamelCase:		Any  ,      _lowerCamelCase:		Optional[Any]					)       ->					Optional[Any]:



							'''simple docstring'''

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


def       lowercase_      (      _lowerCamelCase:		dict  ,      *, _lowerCamelCase:		int  ,      _lowerCamelCase:		bool					)       ->					Tuple:



							'''simple docstring'''

							__lowerCamelCase    :						List[Any]						      =   traverse_util.flatten_dict(variables["target"]					)
							__lowerCamelCase    :						int						      =   {"/".join(_lowerCamelCase					): 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:"  ,      _lowerCamelCase					)

							__lowerCamelCase    :						List[Any]						      =   collections.OrderedDict()

							# Shared embeddings.
							__lowerCamelCase    :						List[str]						      =   old["token_embedder/embedding"]

							# Encoder.
							for i in range(_lowerCamelCase					):
														# Block i, layer 0 (Self Attention).
														__lowerCamelCase    :						Optional[Any]						      =   tax_layer_norm_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "encoder"  ,      "pre_attention_layer_norm"					)
														__lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase      ,     __lowerCamelCase    :						int						      =   tax_attention_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "encoder"  ,      "attention"					)
														__lowerCamelCase    :						Any						      =   layer_norm
														__lowerCamelCase    :						Any						      =   k.T
														__lowerCamelCase    :						List[Any]						      =   o.T
														__lowerCamelCase    :						int						      =   q.T
														__lowerCamelCase    :						str						      =   v.T

														# Block i, layer 1 (MLP).
														__lowerCamelCase    :						str						      =   tax_layer_norm_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "encoder"  ,      "pre_mlp_layer_norm"					)
														__lowerCamelCase      ,     __lowerCamelCase    :						Any						      =   tax_mlp_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "encoder"  ,      _lowerCamelCase					)
														__lowerCamelCase    :						Tuple						      =   layer_norm
														if split_mlp_wi:
																					__lowerCamelCase    :						Dict						      =   wi[0].T
																					__lowerCamelCase    :						Dict						      =   wi[1].T
														else:
																					__lowerCamelCase    :						Dict						      =   wi.T
														__lowerCamelCase    :						List[Any]						      =   wo.T

							__lowerCamelCase    :						Any						      =   old[
							    "encoder/relpos_bias/rel_embedding"
							].T
							__lowerCamelCase    :						List[str]						      =   old["encoder/encoder_norm/scale"]

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

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

																					# Block i, layer 2 (MLP).
																					__lowerCamelCase    :						Union[str, Any]						      =   tax_layer_norm_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "decoder"  ,      "pre_mlp_layer_norm"					)
																					__lowerCamelCase      ,     __lowerCamelCase    :						List[str]						      =   tax_mlp_lookup(_lowerCamelCase  ,      _lowerCamelCase  ,      "decoder"  ,      _lowerCamelCase					)
																					__lowerCamelCase    :						Dict						      =   layer_norm
																					if split_mlp_wi:
																												__lowerCamelCase    :						List[str]						      =   wi[0].T
																												__lowerCamelCase    :						Union[str, Any]						      =   wi[1].T
																					else:
																												__lowerCamelCase    :						Tuple						      =   wi.T
																					__lowerCamelCase    :						List[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    :						Union[str, Any]						      =   old["decoder/logits_dense/kernel"].T

							return new


def       lowercase_      (      _lowerCamelCase:		Dict  ,      _lowerCamelCase:		bool					)       ->					Any:



							'''simple docstring'''

							__lowerCamelCase    :						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:
														__lowerCamelCase    :						Optional[int]						      =   state_dict["shared.weight"]

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					__lowerCamelCase    :						Union[str, Any]						      =   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[Any]						      =   state_dict["shared.weight"]

							return state_dict


def       lowercase_      (      _lowerCamelCase:		List[Any]  ,      _lowerCamelCase:		Optional[int]  ,      _lowerCamelCase:		str  ,      _lowerCamelCase:		str					)       ->					Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase    :						Dict						      =   checkpoints.load_tax_checkpoint(_lowerCamelCase					)
							__lowerCamelCase    :						str						      =   convert_tax_to_pytorch(_lowerCamelCase  ,      num_layers=config.num_layers  ,      is_encoder_only=_lowerCamelCase					)
							__lowerCamelCase    :						Any						      =   make_state_dict(_lowerCamelCase  ,      _lowerCamelCase					)
							model.load_state_dict(_lowerCamelCase  ,      strict=_lowerCamelCase					)


def       lowercase_      (      _lowerCamelCase:		Any  ,      _lowerCamelCase:		Tuple  ,      _lowerCamelCase:		Optional[Any]  ,      _lowerCamelCase:		bool = False					)       ->					Tuple:



							'''simple docstring'''

							__lowerCamelCase    :						Union[str, Any]						      =   TaConfig.from_json_file(_lowerCamelCase					)
							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(_lowerCamelCase					)
							else:
														__lowerCamelCase    :						str						      =   TaForConditionalGeneration(_lowerCamelCase					)

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

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

							# Verify that we can load the checkpoint.
							model.from_pretrained(_lowerCamelCase					)
							print("Done"					)


if __name__ == "__main__":
				__A     = 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
				)
				__A     = parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)





from __future__ import annotations




def 			__magic_name__				( A		:					list   ):






			'''simple docstring'''







			if len(A   ) == 0:
						return []
			a    ,		a    =			min(A   ), max(A   )
			a    =			int(max_value - min_value   ) + 1
			a    =			[[] for _ in range(A   )]

			for i in my_list:
						buckets[int(i - min_value   )].append(A   )

			return [v for bucket in buckets for v in sorted(A   )]


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

		testmod()
		assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
		assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]






