
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							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		Tuple:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_="attention"		)		->		str:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_=False		)		->		Tuple:
					'''simple docstring'''


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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	*, UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_ = False		)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= traverse_util.flatten_dict(variables['target']		)
					SCREAMING_SNAKE_CASE__													= {"""/""".join(UpperCamelCase_		): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					SCREAMING_SNAKE_CASE__													= """encoder/encoder/mlp/wi_0/kernel""" in old
					print('Split MLP:'	,	UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= collections.OrderedDict()

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

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

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

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

					if not scalable_attention:
										SCREAMING_SNAKE_CASE__													= tax_relpos_bias_lookup(
										    UpperCamelCase_	,	0	,	'encoder'		).T
										SCREAMING_SNAKE_CASE__													= tax_relpos_bias_lookup(
										    UpperCamelCase_	,	0	,	'decoder'		).T

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

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

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

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

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

					return new






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= 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:
										SCREAMING_SNAKE_CASE__													= state_dict["""shared.weight"""]

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

					return state_dict






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_ = False	,	UpperCamelCase_ = False	,	)		->		Dict:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= MTaConfig.from_json_file(UpperCamelCase_		)
					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:
										SCREAMING_SNAKE_CASE__													= UMTaEncoderModel(UpperCamelCase_		)
					else:
										SCREAMING_SNAKE_CASE__													= UMTaForConditionalGeneration(UpperCamelCase_		)

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

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

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


if __name__ == "__main__":
				__snake_case								=  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,
				)
				__snake_case								=  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,
				)













from collections.abc import Generator
from math import sin

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	if len(__magic_name__					) != 32:
		raise ValueError("""Input must be of length 32"""					)

	UpperCamelCase     :int          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			bytes:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :Any          =	format(__magic_name__	,			"""08x"""					)[-8:]
	UpperCamelCase     :Union[str, Any]          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""					)
	return little_endian_hex

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :str          =	B""""""
	for char in message:
		bit_string += format(__magic_name__	,			"""08b"""					).encode("""utf-8"""					)
	UpperCamelCase     :Any          =	format(len(__magic_name__					)	,			"""064b"""					).encode("""utf-8"""					)

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(__magic_name__					) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]					) + to_little_endian(start_len[:32]					)

	return bit_string

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			Generator[list[int], None, None]:






	"""simple docstring"""



	if len(__magic_name__					) % 512 != 0:
		raise ValueError("""Input must have length that's a multiple of 512"""					)

	for pos in range(0	,			len(__magic_name__					)	,			512					):
		UpperCamelCase     :Tuple          =	bit_string[pos : pos + 512]
		UpperCamelCase     :Optional[int]          =	[]
		for i in range(0	,			512	,			32					):
			block_words.append(int(to_little_endian(block[i : i + 32]					)	,			2					)					)
		yield block_words

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :List[str]          =	format(__magic_name__	,			"""032b"""					)
	UpperCamelCase     :Any          =	""""""
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(__magic_name__	,			2					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	return (a + b) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)
	if shift < 0:
		raise ValueError("""Shift must be non-negative"""					)
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	preprocess(__magic_name__					)

	UpperCamelCase     :List[str]          =	[int(2**32 * abs(sin(i + 1					)					)					) for i in range(64					)]

	# Starting states
	UpperCamelCase     :Union[str, Any]          =	0X67_45_23_01
	UpperCamelCase     :Union[str, Any]          =	0XEF_CD_AB_89
	UpperCamelCase     :List[str]          =	0X98_BA_DC_FE
	UpperCamelCase     :int          =	0X10_32_54_76

	UpperCamelCase     :int          =	[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(__magic_name__					):
		UpperCamelCase     :Optional[Any]          =	aa
		UpperCamelCase     :Any          =	ba
		UpperCamelCase     :Tuple          =	ca
		UpperCamelCase     :List[str]          =	da

		# Hash current chunk
		for i in range(64					):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				UpperCamelCase     :int          =	d ^ (b & (c ^ d))
				UpperCamelCase     :Optional[int]          =	i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				UpperCamelCase     :str          =	c ^ (d & (b ^ c))
				UpperCamelCase     :Union[str, Any]          =	(5 * i + 1) % 16
			elif i <= 47:
				UpperCamelCase     :str          =	b ^ c ^ d
				UpperCamelCase     :Optional[int]          =	(3 * i + 5) % 16
			else:
				UpperCamelCase     :List[str]          =	c ^ (b | not_aa(__magic_name__					))
				UpperCamelCase     :int          =	(7 * i) % 16
			UpperCamelCase     :Dict          =	(f + a + added_consts[i] + block_words[g]) % 2**32
			UpperCamelCase     :Tuple          =	d
			UpperCamelCase     :str          =	c
			UpperCamelCase     :Tuple          =	b
			UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			left_rotate_aa(__magic_name__	,			shift_amounts[i]					)					)

		# Add hashed chunk to running total
		UpperCamelCase     :List[str]          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :str          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :int          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :Optional[Any]          =	reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					)
	return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()
