




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 							lowercase					(snake_case__   : int      ,       snake_case__   : Any      ,       snake_case__   : List[str] )     ->  Dict:


				'''simple docstring'''






				return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Tuple      ,       snake_case__   : Dict="attention" )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				k_tmp.reshape(k_tmp.shape[0]      ,       k_tmp.shape[1] * k_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,       o_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				q_tmp.reshape(q_tmp.shape[0]      ,       q_tmp.shape[1] * q_tmp.shape[2] )
				lowerCAmelCase  =				np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
				lowerCAmelCase  =				v_tmp.reshape(v_tmp.shape[0]      ,       v_tmp.shape[1] * v_tmp.shape[2] )
				return k, o, q, v





def 							lowercase					(snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : List[str]=False )     ->  Union[str, Any]:


				'''simple docstring'''






				if split_mlp_wi:
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
								lowerCAmelCase  =				(wi_a, wi_a)
				else:
								lowerCAmelCase  =				params[f'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Optional[int]      ,       snake_case__   : int )     ->  Union[str, Any]:


				'''simple docstring'''






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





def 							lowercase					(snake_case__   : dict      ,       *, snake_case__   : int      ,       snake_case__   : bool      ,       snake_case__   : bool = False )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				traverse_util.flatten_dict(variables["""target"""] )
				lowerCAmelCase  =				{"""/""".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
				lowerCAmelCase  =				"""encoder/encoder/mlp/wi_0/kernel""" in old
				print("""Split MLP:"""      ,       snake_case__ )

				lowerCAmelCase  =				collections.OrderedDict()

				# Shared embeddings.
				lowerCAmelCase  =				old["""token_embedder/embedding"""]

				# Encoder.
				for i in range(snake_case__ ):
								# Block i, layer 0 (Self Attention).
								lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """pre_attention_layer_norm""" )
								lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """attention""" )
								lowerCAmelCase  =				layer_norm
								lowerCAmelCase  =				k.T
								lowerCAmelCase  =				o.T
								lowerCAmelCase  =				q.T
								lowerCAmelCase  =				v.T

								# Block i, layer 1 (MLP).
								lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       """pre_mlp_layer_norm""" )
								lowerCAmelCase ,						lowerCAmelCase  =				tax_mlp_lookup(snake_case__      ,       snake_case__      ,       """encoder"""      ,       snake_case__ )
								lowerCAmelCase  =				layer_norm
								if split_mlp_wi:
												lowerCAmelCase  =				wi[0].T
												lowerCAmelCase  =				wi[1].T
								else:
												lowerCAmelCase  =				wi.T
								lowerCAmelCase  =				wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCAmelCase  =				tax_relpos_bias_lookup(
												    snake_case__      ,       snake_case__      ,       """encoder""" ).T

				lowerCAmelCase  =				old["""encoder/encoder_norm/scale"""]

				if not scalable_attention:
								lowerCAmelCase  =				tax_relpos_bias_lookup(
								    snake_case__      ,       0      ,       """encoder""" ).T
								lowerCAmelCase  =				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).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_self_attention_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """self_attention""" )
												lowerCAmelCase  =				layer_norm
												lowerCAmelCase  =				k.T
												lowerCAmelCase  =				o.T
												lowerCAmelCase  =				q.T
												lowerCAmelCase  =				v.T

												# Block i, layer 1 (Cross Attention).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_cross_attention_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				tax_attention_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """encoder_decoder_attention""" )
												lowerCAmelCase  =				layer_norm
												lowerCAmelCase  =				k.T
												lowerCAmelCase  =				o.T
												lowerCAmelCase  =				q.T
												lowerCAmelCase  =				v.T

												# Block i, layer 2 (MLP).
												lowerCAmelCase  =				tax_layer_norm_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       """pre_mlp_layer_norm""" )
												lowerCAmelCase ,						lowerCAmelCase  =				tax_mlp_lookup(snake_case__      ,       snake_case__      ,       """decoder"""      ,       snake_case__ )
												lowerCAmelCase  =				layer_norm
												if split_mlp_wi:
																lowerCAmelCase  =				wi[0].T
																lowerCAmelCase  =				wi[1].T
												else:
																lowerCAmelCase  =				wi.T
												lowerCAmelCase  =				wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowerCAmelCase  =				tax_relpos_bias_lookup(snake_case__      ,       snake_case__      ,       """decoder""" ).T

								lowerCAmelCase  =				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:
												lowerCAmelCase  =				old["""decoder/logits_dense/kernel"""].T

				return new





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : bool )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				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  =				state_dict["""shared.weight"""]

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowerCAmelCase  =				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  =				state_dict["""shared.weight"""]

				return state_dict





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Optional[Any]      ,       snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : int )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				checkpoints.load_tax_checkpoint(snake_case__ )
				lowerCAmelCase  =				convert_tax_to_pytorch(
				    snake_case__      ,       num_layers=config.num_layers      ,       is_encoder_only=snake_case__      ,       scalable_attention=snake_case__ )
				lowerCAmelCase  =				make_state_dict(snake_case__      ,       snake_case__ )
				model.load_state_dict(snake_case__      ,       strict=snake_case__ )





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : List[Any]      ,       snake_case__   : bool = False      ,       snake_case__   : bool = False      ,       )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				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:
								lowerCAmelCase  =				UMTaEncoderModel(snake_case__ )
				else:
								lowerCAmelCase  =				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__":
		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
		)
		parser.add_argument(
		    '--scalable_attention',
		    action='store_true',
		    help='Whether the model uses scaled attention (umt5 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,
		    args.scalable_attention,
		)











"""simple docstring"""







from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Collection[float] | None = None					):
										if components is None:
														lowerCAmelCase  =				[]
										lowerCAmelCase  =				list(lowerCAmelCase					)


						def __len__(							self				:			List[Any]					):
										return len(self.__components					)


						def __str__(							self				:			str					):
										return "(" + ",".join(map(lowerCAmelCase						,						self.__components					)					) + ")"


						def __add__(							self				:			Any						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] + other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:
														raise Exception("""must have the same size"""					)


						def __sub__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										lowerCAmelCase  =				len(self					)
										if size == len(lowerCAmelCase					):
														lowerCAmelCase  =				[self.__components[i] - other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return Vector(lowerCAmelCase					)
										else:  # error case
														raise Exception("""must have the same size"""					)


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			List[Any]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						(float, int)					):
														lowerCAmelCase  =				[c * other for c in self.__components]
														return Vector(lowerCAmelCase					)
										elif isinstance(lowerCAmelCase						,						lowerCAmelCase					) and len(self					) == len(lowerCAmelCase					):
														lowerCAmelCase  =				len(self					)
														lowerCAmelCase  =				[self.__components[i] * other.component(lowerCAmelCase					) for i in range(lowerCAmelCase					)]
														return sum(lowerCAmelCase					)
										else:  # error case
														raise Exception("""invalid operand!"""					)


						def   __lowercase (							self				:			Tuple					):
										return Vector(self.__components					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			int					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					) and -len(self.__components					) <= i < len(self.__components					):
														return self.__components[i]
										else:
														raise Exception("""index out of range"""					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										assert -len(self.__components					) <= pos < len(self.__components					)
										lowerCAmelCase  =				value


						def   __lowercase (							self				:			Optional[int]					):
										if len(self.__components					) == 0:
														raise Exception("""Vector is empty"""					)
										lowerCAmelCase  =				[c**2 for c in self.__components]
										return math.sqrt(sum(lowerCAmelCase					)					)




						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			Vector						,						lowerCAmelCase				:			bool = False					):
										lowerCAmelCase  =				self * other
										lowerCAmelCase  =				self.euclidean_length() * other.euclidean_length()
										if deg:
														return math.degrees(math.acos(num / den					)					)
										else:
														return math.acos(num / den					)





def 							lowercase					(snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ )
				return Vector([0] * dimension )





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				assert isinstance(snake_case__      ,       snake_case__ ) and (isinstance(snake_case__      ,       snake_case__ ))
				lowerCAmelCase  =				[0] * dimension
				lowerCAmelCase  =				1
				return Vector(snake_case__ )





def 							lowercase					(snake_case__   : float      ,       snake_case__   : Vector      ,       snake_case__   : Vector )     ->  Vector:


				'''simple docstring'''






				assert (
				    isinstance(snake_case__      ,       snake_case__ )
				    and isinstance(snake_case__      ,       snake_case__ )
				    and (isinstance(snake_case__      ,       (int, float) ))
				)
				return x * scalar + y





def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Vector:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )]
				return Vector(snake_case__ )






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Any						,						lowerCAmelCase				:			list[list[float]]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				matrix
										lowerCAmelCase  =				w
										lowerCAmelCase  =				h


						def __str__(							self				:			List[str]					):
										lowerCAmelCase  =				""""""
										for i in range(self.__height					):
														ans += "|"
														for j in range(self.__width					):
																		if j < self.__width - 1:
																						ans += str(self.__matrix[i][j]					) + ","
																		else:
																						ans += str(self.__matrix[i][j]					) + "|\n"
										return ans


						def __add__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] + other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrix must have the same dimension!"""					)


						def __sub__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			Matrix					):
										if self.__width == other.width() and self.__height == other.height():
														lowerCAmelCase  =				[]
														for i in range(self.__height					):
																		lowerCAmelCase  =				[
																		    self.__matrix[i][j] - other.component(lowerCAmelCase						,						lowerCAmelCase					)
																		    for j in range(self.__width					)
																		]
																		matrix.append(lowerCAmelCase					)
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										else:
														raise Exception("""matrices must have the same dimension!"""					)


						@overload
						def __mul__(							self				:			Any						,						lowerCAmelCase				:			float					):
										...


						@overload
						def __mul__(							self				:			Optional[int]						,						lowerCAmelCase				:			Vector					):
										...


						def __mul__(							self				:			Dict						,						lowerCAmelCase				:			float | Vector					):
										if isinstance(lowerCAmelCase						,						lowerCAmelCase					):  # matrix-vector
														if len(lowerCAmelCase					) == self.__width:
																		lowerCAmelCase  =				zero_vector(self.__height					)
																		for i in range(self.__height					):
																						lowerCAmelCase  =				[
																						    self.__matrix[i][j] * other.component(lowerCAmelCase					)
																						    for j in range(self.__width					)
																						]
																						ans.change_component(lowerCAmelCase						,						sum(lowerCAmelCase					)					)
																		return ans
														else:
																		raise Exception(
																		    """vector must have the same size as the """
																		    """number of columns of the matrix!"""					)
										elif isinstance(lowerCAmelCase						,						(int, float)					):  # matrix-scalar
														lowerCAmelCase  =				[
														    [self.__matrix[i][j] * other for j in range(self.__width					)]
														    for i in range(self.__height					)
														]
														return Matrix(lowerCAmelCase						,						self.__width						,						self.__height					)
										return None


						def   __lowercase (							self				:			Any					):
										return self.__height


						def   __lowercase (							self				:			int					):
										return self.__width


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			float					):
										if 0 <= x < self.__height and 0 <= y < self.__width:
														lowerCAmelCase  =				value
										else:
														raise Exception("""change_component: indices out of bounds"""					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										lowerCAmelCase  =				self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(lowerCAmelCase					)					):
														lowerCAmelCase  =				minor[i][:y] + minor[i][y + 1 :]
										return Matrix(lowerCAmelCase						,						self.__width - 1						,						self.__height - 1					).determinant()


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(lowerCAmelCase						,						lowerCAmelCase					)
										else:
														raise Exception("""Indices out of bounds"""					)




						def   __lowercase (							self				:			List[Any]					):
										if self.__height != self.__width:
														raise Exception("""Matrix is not square"""					)
										if self.__height < 1:
														raise Exception("""Matrix has no element"""					)
										elif self.__height == 1:
														return self.__matrix[0][0]
										elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
										else:
														lowerCAmelCase  =				[
														    self.__matrix[0][y] * self.cofactor(0						,						lowerCAmelCase					) for y in range(self.__width					)
														]
														return sum(lowerCAmelCase					)





def 							lowercase					(snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				lowerCAmelCase  =				[[0] * n for _ in range(snake_case__ )]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )






def 							lowercase					(snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int      ,       snake_case__   : int )     ->  Matrix:


				'''simple docstring'''






				random.seed(snake_case__ )
				lowerCAmelCase  =				[
				    [random.randint(snake_case__      ,       snake_case__ ) for _ in range(snake_case__ )] for _ in range(snake_case__ )
				]
				return Matrix(snake_case__      ,       snake_case__      ,       snake_case__ )





