



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        a						(      _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Union[str, Any]="attention"						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Dict=False						):







		'''simple docstring'''





		if split_mlp_wi:
				__UpperCAmelCase    :     List[str]													=	params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
				__UpperCAmelCase    :     Dict													=	params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
				__UpperCAmelCase    :     Optional[int]													=	(wi_a, wi_a)
		else:
				__UpperCAmelCase    :     Optional[int]													=	params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple						):







		'''simple docstring'''





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

def        a						(      _UpperCAmelCase							:					dict ,   *, _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					bool ,   _UpperCAmelCase							:					bool = False						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	traverse_util.flatten_dict(variables['''target''']						)
		__UpperCAmelCase    :     List[str]													=	{'''/'''.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
		__UpperCAmelCase    :     Dict													=	'''encoder/encoder/mlp/wi_0/kernel''' in old
		print('''Split MLP:''' ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     Any													=	collections.OrderedDict()

		# Shared embeddings.
		__UpperCAmelCase    :     Tuple													=	old['''token_embedder/embedding''']

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

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

		__UpperCAmelCase    :     Optional[int]													=	old['''encoder/encoder_norm/scale''']

		if not scalable_attention:
				__UpperCAmelCase    :     Union[str, Any]													=	tax_relpos_bias_lookup(
				    _UpperCAmelCase ,   0 ,   '''encoder'''						).T
				__UpperCAmelCase    :     str													=	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).
						__UpperCAmelCase    :     Optional[Any]													=	tax_layer_norm_lookup(_UpperCAmelCase ,   _UpperCAmelCase ,   '''decoder''' ,   '''pre_self_attention_layer_norm'''						)
						__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	tax_attention_lookup(_UpperCAmelCase ,   _UpperCAmelCase ,   '''decoder''' ,   '''self_attention'''						)
						__UpperCAmelCase    :     str													=	layer_norm
						__UpperCAmelCase    :     Tuple													=	k.T
						__UpperCAmelCase    :     List[str]													=	o.T
						__UpperCAmelCase    :     Union[str, Any]													=	q.T
						__UpperCAmelCase    :     Tuple													=	v.T

						# Block i, layer 1 (Cross Attention).
						__UpperCAmelCase    :     str													=	tax_layer_norm_lookup(_UpperCAmelCase ,   _UpperCAmelCase ,   '''decoder''' ,   '''pre_cross_attention_layer_norm'''						)
						__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	tax_attention_lookup(_UpperCAmelCase ,   _UpperCAmelCase ,   '''decoder''' ,   '''encoder_decoder_attention'''						)
						__UpperCAmelCase    :     Union[str, Any]													=	layer_norm
						__UpperCAmelCase    :     List[Any]													=	k.T
						__UpperCAmelCase    :     Union[str, Any]													=	o.T
						__UpperCAmelCase    :     Union[str, Any]													=	q.T
						__UpperCAmelCase    :     Any													=	v.T

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

						if scalable_attention:
								# convert the rel_embedding of each layer
								__UpperCAmelCase    :     Any													=	tax_relpos_bias_lookup(_UpperCAmelCase ,   _UpperCAmelCase ,   '''decoder'''						).T

				__UpperCAmelCase    :     Any													=	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:
						__UpperCAmelCase    :     Any													=	old['''decoder/logits_dense/kernel'''].T

		return new

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					bool						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	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:
				__UpperCAmelCase    :     List[Any]													=	state_dict['''shared.weight''']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						__UpperCAmelCase    :     int													=	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.'''						)
						__UpperCAmelCase    :     List[Any]													=	state_dict['''shared.weight''']

		return state_dict

def        a						(      _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	checkpoints.load_tax_checkpoint(_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[int]													=	convert_tax_to_pytorch(
		    _UpperCAmelCase ,   num_layers=config.num_layers ,   is_encoder_only=_UpperCAmelCase ,   scalable_attention=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	make_state_dict(_UpperCAmelCase ,   _UpperCAmelCase						)
		model.load_state_dict(_UpperCAmelCase ,   strict=_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					bool = False ,   _UpperCAmelCase							:					bool = False ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	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:
				__UpperCAmelCase    :     List[str]													=	UMTaEncoderModel(_UpperCAmelCase						)
		else:
				__UpperCAmelCase    :     Optional[Any]													=	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__":
				__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,
				)










import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
				import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





