


'''simple docstring'''
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      (			__a ,					__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






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


def 			a      (			__a ,					__a ,					__a ,					__a="attention"   )  ->	int:



					'''simple docstring'''






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


def 			a      (			__a ,					__a ,					__a ,					__a=False   )  ->	List[str]:



					'''simple docstring'''






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

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


def 			a      (			__a ,					__a ,					__a ,					__a   )  ->	Union[str, Any]:



					'''simple docstring'''






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


def 			a      (			__a ,					*, __a ,					__a ,					__a = False   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :str												= traverse_util.flatten_dict(variables['''target''']   )
					UpperCamelCase__   :Dict												= {'''/'''.join(__a   ): v for k, v in old.items()}

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

					UpperCamelCase__   :Any												= collections.OrderedDict()

					# Shared embeddings.
					UpperCamelCase__   :List[str]												= old['''token_embedder/embedding''']

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

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

					UpperCamelCase__   :str												= old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										UpperCamelCase__   :Optional[int]												= tax_relpos_bias_lookup(
										    __a ,					0 ,					'''encoder'''   ).T
										UpperCamelCase__   :int												= tax_relpos_bias_lookup(
										    __a ,					0 ,					'''decoder'''   ).T

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

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

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

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

										UpperCamelCase__   :Dict												= 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__   :Tuple												= old['''decoder/logits_dense/kernel'''].T

					return new


def 			a      (			__a ,					__a   )  ->	List[str]:



					'''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[str]												= state_dict['''shared.weight''']

					if not is_encoder_only:
										if "decoder.embed_tokens.weight" not in state_dict:
															UpperCamelCase__   :Tuple												= 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__   :Tuple												= state_dict['''shared.weight''']

					return state_dict


def 			a      (			__a ,					__a ,					__a ,					__a ,					__a   )  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__   :int												= checkpoints.load_tax_checkpoint(__a   )
					UpperCamelCase__   :List[str]												= convert_tax_to_pytorch(
					    __a ,					num_layers=config.num_layers ,					is_encoder_only=__a ,					scalable_attention=__a   )
					UpperCamelCase__   :List[str]												= make_state_dict(__a ,					__a   )
					model.load_state_dict(__a ,					strict=__a   )


def 			a      (			__a ,					__a ,					__a ,					__a = False ,					__a = False ,					)  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__   :int												= MTaConfig.from_json_file(__a   )
					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__   :Dict												= UMTaEncoderModel(__a   )
					else:
										UpperCamelCase__   :Optional[int]												= UMTaForConditionalGeneration(__a   )

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

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

					# Verify that we can load the checkpoint.
					model.from_pretrained(__a   )
					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,
		)



'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .multicontrolnet import MultiControlNetModel
		from .pipeline_controlnet import StableDiffusionControlNetPipeline
		from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
		from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
		from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline