

"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      AutoConfig.from_pretrained(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__    )
			SCREAMING_SNAKE_CASE__								=      checkpoints.load_tax_checkpoint(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

			if config.model_type == "t5":
						SCREAMING_SNAKE_CASE__								=      """SelfAttention"""
			if config.model_type == "longt5" and config.encoder_attention_type == "local":
						SCREAMING_SNAKE_CASE__								=      """LocalSelfAttention"""
			elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						SCREAMING_SNAKE_CASE__								=      """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__								=      f"""layers_{str(snake_case__    )}"""

						# Self-Attention
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      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__								=      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

						# Layer Normalization
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
						else:
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

						# Layer Normalization
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

						# Assigning
						SCREAMING_SNAKE_CASE__								=      flax_model.params["""encoder"""]["""block"""][str(snake_case__    )]["""layer"""]
						SCREAMING_SNAKE_CASE__								=      tax_attention_key
						SCREAMING_SNAKE_CASE__								=      tax_attention_out
						SCREAMING_SNAKE_CASE__								=      tax_attention_query
						SCREAMING_SNAKE_CASE__								=      tax_attention_value

						SCREAMING_SNAKE_CASE__								=      tax_attention_layer_norm

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

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi_a
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi

						SCREAMING_SNAKE_CASE__								=      tax_mlp_wo
						SCREAMING_SNAKE_CASE__								=      tax_mlp_layer_norm

						SCREAMING_SNAKE_CASE__								=      flax_model_encoder_layer_block

			# Only for layer 0:
			SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
			SCREAMING_SNAKE_CASE__								=      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__								=      tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
						SCREAMING_SNAKE_CASE__								=      tax_encoder_global_rel_embedding

			# Assigning
			SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
			SCREAMING_SNAKE_CASE__								=      tax_encoder_norm

			# Decoder
			for layer_index in range(config.num_layers    ):
						SCREAMING_SNAKE_CASE__								=      f"""layers_{str(snake_case__    )}"""

						# Self-Attention
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

						# Layer Normalization
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
						    """scale"""
						]

						# Encoder-Decoder-Attention
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_module["""key"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_module["""out"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_module["""query"""]["""kernel"""]
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_module["""value"""]["""kernel"""]

						# Layer Normalization
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

						# MLP
						if split_mlp_wi:
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
						else:
									SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

						# Layer Normalization
						SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

						# Assigning
						SCREAMING_SNAKE_CASE__								=      flax_model.params["""decoder"""]["""block"""][str(snake_case__    )]["""layer"""]
						SCREAMING_SNAKE_CASE__								=      tax_attention_key
						SCREAMING_SNAKE_CASE__								=      tax_attention_out
						SCREAMING_SNAKE_CASE__								=      tax_attention_query
						SCREAMING_SNAKE_CASE__								=      tax_attention_value

						SCREAMING_SNAKE_CASE__								=      tax_pre_attention_layer_norm

						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_key
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_out
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_query
						SCREAMING_SNAKE_CASE__								=      tax_enc_dec_attention_value

						SCREAMING_SNAKE_CASE__								=      tax_cross_layer_norm

						if split_mlp_wi:
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi_a
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi_a
						else:
									SCREAMING_SNAKE_CASE__								=      tax_mlp_wi

						SCREAMING_SNAKE_CASE__								=      tax_mlp_wo

						SCREAMING_SNAKE_CASE__								=      txa_mlp_layer_norm

						SCREAMING_SNAKE_CASE__								=      flax_model_decoder_layer_block

			# Decoder Normalization
			SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
			SCREAMING_SNAKE_CASE__								=      txa_decoder_norm

			# Only for layer 0:
			SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
			SCREAMING_SNAKE_CASE__								=      tax_decoder_rel_embedding

			# Token Embeddings
			SCREAMING_SNAKE_CASE__								=      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
			SCREAMING_SNAKE_CASE__								=      txa_token_embeddings

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

			flax_model.save_pretrained(snake_case__    )
			print("""T5X Model was sucessfully converted!"""    )


if __name__ == "__main__":
						A_							:			str											= 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."
						)
						A_							:			List[Any]											= parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2





@slow
@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


