






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  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase):
			return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase="attention"):
			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  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase=False):




			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  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase):
			return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def  lowerCamelCase__ (_UpperCAmelCase							,				*, _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase = False):
			SCREAMING_SNAKE_CASE						 =       traverse_util.flatten_dict(variables['target'])
			SCREAMING_SNAKE_CASE						 =       {'/'.join(lowerCAmelCase__): 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:'							,				lowerCAmelCase__)

			SCREAMING_SNAKE_CASE						 =       collections.OrderedDict()

			# Shared embeddings.
			SCREAMING_SNAKE_CASE						 =       old['token_embedder/embedding']

			# Encoder.
			for i in range(lowerCAmelCase__):
						# Block i, layer 0 (Self Attention).
						SCREAMING_SNAKE_CASE						 =       tax_layer_norm_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'encoder'							,				'pre_attention_layer_norm')
						SCREAMING_SNAKE_CASE						 =       tax_attention_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'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(lowerCAmelCase__							,				lowerCAmelCase__							,				'encoder'							,				'pre_mlp_layer_norm')
						SCREAMING_SNAKE_CASE						 =       tax_mlp_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'encoder'							,				lowerCAmelCase__)
						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(
									    lowerCAmelCase__							,				lowerCAmelCase__							,				'encoder').T

			SCREAMING_SNAKE_CASE						 =       old['encoder/encoder_norm/scale']

			if not scalable_attention:
						SCREAMING_SNAKE_CASE						 =       tax_relpos_bias_lookup(
						    lowerCAmelCase__							,				0							,				'encoder').T
						SCREAMING_SNAKE_CASE						 =       tax_relpos_bias_lookup(
						    lowerCAmelCase__							,				0							,				'decoder').T

			if not is_encoder_only:
						# Decoder.
						for i in range(lowerCAmelCase__):
									# Block i, layer 0 (Self Attention).
									SCREAMING_SNAKE_CASE						 =       tax_layer_norm_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'decoder'							,				'pre_self_attention_layer_norm')
									SCREAMING_SNAKE_CASE						 =       tax_attention_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'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(lowerCAmelCase__							,				lowerCAmelCase__							,				'decoder'							,				'pre_cross_attention_layer_norm')
									SCREAMING_SNAKE_CASE						 =       tax_attention_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'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(lowerCAmelCase__							,				lowerCAmelCase__							,				'decoder'							,				'pre_mlp_layer_norm')
									SCREAMING_SNAKE_CASE						 =       tax_mlp_lookup(lowerCAmelCase__							,				lowerCAmelCase__							,				'decoder'							,				lowerCAmelCase__)
									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(lowerCAmelCase__							,				lowerCAmelCase__							,				'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  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase):
			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  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						 =       checkpoints.load_tax_checkpoint(lowerCAmelCase__)
			SCREAMING_SNAKE_CASE						 =       convert_tax_to_pytorch(
			    lowerCAmelCase__							,				num_layers=config.num_layers							,				is_encoder_only=lowerCAmelCase__							,				scalable_attention=lowerCAmelCase__)
			SCREAMING_SNAKE_CASE						 =       make_state_dict(lowerCAmelCase__							,				lowerCAmelCase__)
			model.load_state_dict(lowerCAmelCase__							,				strict=lowerCAmelCase__)






def  lowerCamelCase__ (_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase = False							,				_UpperCAmelCase = False							,				):
			SCREAMING_SNAKE_CASE						 =       MTaConfig.from_json_file(lowerCAmelCase__)
			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(lowerCAmelCase__)
			else:
						SCREAMING_SNAKE_CASE						 =       UMTaForConditionalGeneration(lowerCAmelCase__)

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

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

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


if __name__ == "__main__":
				a_						:  Union[str, Any]									=			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_						:  int									=			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"""







import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    """The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionImg2ImgPipeline` instead."""
)





