



'''simple docstring'''

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Tuple     ,						_SCREAMING_SNAKE_CASE   :str     ,						_SCREAMING_SNAKE_CASE   :Any							):
				SCREAMING_SNAKE_CASE	:    Tuple          =			AutoConfig.from_pretrained(_A							)
				SCREAMING_SNAKE_CASE	:    Any          =			FlaxAutoModelForSeqaSeqLM.from_config(config=_A							)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			checkpoints.load_tax_checkpoint(_A							)

				SCREAMING_SNAKE_CASE	:    Dict          =			"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

				if config.model_type == "t5":
								SCREAMING_SNAKE_CASE	:    Any          =			"""SelfAttention"""
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								SCREAMING_SNAKE_CASE	:    List[str]          =			"""LocalSelfAttention"""
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								SCREAMING_SNAKE_CASE	:    Any          =			"""TransientGlobalSelfAttention"""
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''							)

				# Encoder
				for layer_index in range(config.num_layers							):
								SCREAMING_SNAKE_CASE	:    Tuple          =			F'''layers_{str(_A							)}'''

								# Self-Attention
								SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    int          =			tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    Tuple          =			tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    List[str]          =			tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE	:    Tuple          =			tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

								# Layer Normalization
								SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

								if split_mlp_wi:
												SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
												SCREAMING_SNAKE_CASE	:    Dict          =			tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
								else:
												SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

								SCREAMING_SNAKE_CASE	:    int          =			tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

								# Layer Normalization
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

								# Assigning
								SCREAMING_SNAKE_CASE	:    Optional[Any]          =			flax_model.params["""encoder"""]["""block"""][str(_A							)]["""layer"""]
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_attention_key
								SCREAMING_SNAKE_CASE	:    Tuple          =			tax_attention_out
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_attention_query
								SCREAMING_SNAKE_CASE	:    List[str]          =			tax_attention_value

								SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE	:    int          =			tax_global_layer_norm

								if split_mlp_wi:
												SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_mlp_wi_a
												SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_mlp_wi_a
								else:
												SCREAMING_SNAKE_CASE	:    Any          =			tax_mlp_wi

								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_mlp_wo
								SCREAMING_SNAKE_CASE	:    Tuple          =			tax_mlp_layer_norm

								SCREAMING_SNAKE_CASE	:    Dict          =			flax_model_encoder_layer_block

				# Only for layer 0:
				SCREAMING_SNAKE_CASE	:    Dict          =			tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
				SCREAMING_SNAKE_CASE	:    str          =			tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								SCREAMING_SNAKE_CASE	:    int          =			tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
								SCREAMING_SNAKE_CASE	:    Optional[Any]          =			tax_encoder_global_rel_embedding

				# Assigning
				SCREAMING_SNAKE_CASE	:    List[str]          =			tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers							):
								SCREAMING_SNAKE_CASE	:    Dict          =			F'''layers_{str(_A							)}'''

								# Self-Attention
								SCREAMING_SNAKE_CASE	:    int          =			tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    Tuple          =			tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    Any          =			tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    Any          =			tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

								# Layer Normalization
								SCREAMING_SNAKE_CASE	:    List[str]          =			tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
								    """scale"""
								]

								# Encoder-Decoder-Attention
								SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
								SCREAMING_SNAKE_CASE	:    int          =			tax_enc_dec_attention_module["""key"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    Tuple          =			tax_enc_dec_attention_module["""out"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_enc_dec_attention_module["""query"""]["""kernel"""]
								SCREAMING_SNAKE_CASE	:    str          =			tax_enc_dec_attention_module["""value"""]["""kernel"""]

								# Layer Normalization
								SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

								# MLP
								if split_mlp_wi:
												SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
												SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
								else:
												SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

								SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

								# Layer Normalization
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

								# Assigning
								SCREAMING_SNAKE_CASE	:    Tuple          =			flax_model.params["""decoder"""]["""block"""][str(_A							)]["""layer"""]
								SCREAMING_SNAKE_CASE	:    Dict          =			tax_attention_key
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_attention_out
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_attention_query
								SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_attention_value

								SCREAMING_SNAKE_CASE	:    Any          =			tax_pre_attention_layer_norm

								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_enc_dec_attention_key
								SCREAMING_SNAKE_CASE	:    str          =			tax_enc_dec_attention_out
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_enc_dec_attention_query
								SCREAMING_SNAKE_CASE	:    Dict          =			tax_enc_dec_attention_value

								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_cross_layer_norm

								if split_mlp_wi:
												SCREAMING_SNAKE_CASE	:    Optional[int]          =			tax_mlp_wi_a
												SCREAMING_SNAKE_CASE	:    Any          =			tax_mlp_wi_a
								else:
												SCREAMING_SNAKE_CASE	:    Optional[Any]          =			tax_mlp_wi

								SCREAMING_SNAKE_CASE	:    Optional[Any]          =			tax_mlp_wo

								SCREAMING_SNAKE_CASE	:    Dict          =			txa_mlp_layer_norm

								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			flax_model_decoder_layer_block

				# Decoder Normalization
				SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
				SCREAMING_SNAKE_CASE	:    int          =			txa_decoder_norm

				# Only for layer 0:
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tax_decoder_rel_embedding

				# Token Embeddings
				SCREAMING_SNAKE_CASE	:    List[Any]          =			tax_model["""target"""]["""token_embedder"""]["""embedding"""]
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								SCREAMING_SNAKE_CASE	:    str          =			tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

				flax_model.save_pretrained(_A							)
				print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
	snake_case_      =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
	)
	parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
	parser.add_argument(
	    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
	)
	snake_case_      =  parser.parse_args()
	convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












"""simple docstring"""







from __future__ import annotations

from scipy.special import comb  # type: ignore


class      UpperCamelCase		:






						def __init__(		self  :Any			, __magic_name__  :list[tuple[float, float]]				)      ->str:
										lowercase    :      List[Any]       = list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										lowercase    :      Optional[int]       = len(__magic_name__				) - 1






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->list[float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										lowercase    :      list[float]       = []
										for i in range(len(self.list_of_points				)				):
														# basis function for each i
														output_values.append(
														    comb(self.degree			, __magic_name__				) * ((1 - t) ** (self.degree - i)) * (t**i)				)
										# the basis must sum up to 1 for it to produce a valid Bezier curve.
										assert round(sum(__magic_name__				)			, 5				) == 1
										return output_values






						def      __snake_case      (		self  :List[str]			, __magic_name__  :float				)      ->tuple[float, float]:
										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										lowercase    :      str       = self.basis_function(__magic_name__				)
										lowercase    :      Optional[int]       = 0.0
										lowercase    :      List[str]       = 0.0
										for i in range(len(self.list_of_points				)				):
														# For all points, sum up the product of i-th basis function and i-th point.
														x += basis_function[i] * self.list_of_points[i][0]
														y += basis_function[i] * self.list_of_points[i][1]
										return (x, y)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :float = 0.01				)      ->List[str]:
										from matplotlib import pyplot as plt  # type: ignore

										lowercase    :      list[float]       = []  # x coordinates of points to plot
										lowercase    :      list[float]       = []  # y coordinates of points to plot

										lowercase    :      int       = 0.0
										while t <= 1:
														lowercase    :      List[Any]       = self.bezier_curve_function(__magic_name__				)
														to_plot_x.append(value[0]				)
														to_plot_y.append(value[1]				)
														t += step_size

										lowercase    :      Union[str, Any]       = [i[0] for i in self.list_of_points]
										lowercase    :      Optional[int]       = [i[1] for i in self.list_of_points]

										plt.plot(
										    __magic_name__			, __magic_name__			, color="""blue"""			, label="""Curve of Degree """ + str(self.degree				)			, )
										plt.scatter(__magic_name__			, __magic_name__			, color="""red"""			, label="""Control Points"""				)
										plt.legend()
										plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3
