




'''simple docstring'''


import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()







class     SCREAMING_SNAKE_CASE						(	unittest.TestCase							):






      @property
      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           a_    :       Any    	=     1
           a_    :       List[Any]    	=     3
           a_    :       Union[str, Any]    	=     (32, 32)

           a_    :       List[Any]    	=     floats_tensor((batch_size, num_channels) + sizes	,	rng=random.Random(0					)					).to(lowercase__					)
           return image






      @property
      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           torch.manual_seed(0					)
           a_    :       Optional[Any]    	=     UNetaDModel(
               block_out_channels=(32, 64)	,	layers_per_block=2	,	sample_size=32	,	in_channels=6	,	out_channels=3	,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,	)
           return model






      @property
      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           torch.manual_seed(0					)
           a_    :       List[str]    	=     VQModel(
               block_out_channels=[32, 64]	,	in_channels=3	,	out_channels=3	,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,	latent_channels=3	,	)
           return model






      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     """cpu"""
           a_    :       Optional[int]    	=     self.dummy_uncond_unet
           a_    :       Optional[Any]    	=     DDIMScheduler()
           a_    :       List[str]    	=     self.dummy_vq_model

           a_    :       Dict    	=     LDMSuperResolutionPipeline(unet=lowercase__	,	vqvae=lowercase__	,	scheduler=lowercase__					)
           ldm.to(lowercase__					)
           ldm.set_progress_bar_config(disable=lowercase__					)

           a_    :       List[Any]    	=     self.dummy_image.to(lowercase__					)

           a_    :       int    	=     torch.Generator(device=lowercase__					).manual_seed(0					)
           a_    :       str    	=     ldm(image=lowercase__	,	generator=lowercase__	,	num_inference_steps=2	,	output_type="""numpy"""					).images

           a_    :       Dict    	=     image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           a_    :       Union[str, Any]    	=     np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]					)

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






      @unittest.skipIf(torch_device != """cuda"""	,	"""This test requires a GPU"""					)
      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           a_    :       Union[str, Any]    	=     self.dummy_uncond_unet
           a_    :       int    	=     DDIMScheduler()
           a_    :       Tuple    	=     self.dummy_vq_model

           # put models in fp16
           a_    :       Dict    	=     unet.half()
           a_    :       str    	=     vqvae.half()

           a_    :       int    	=     LDMSuperResolutionPipeline(unet=lowercase__	,	vqvae=lowercase__	,	scheduler=lowercase__					)
           ldm.to(lowercase__					)
           ldm.set_progress_bar_config(disable=lowercase__					)

           a_    :       Dict    	=     self.dummy_image.to(lowercase__					)

           a_    :       Union[str, Any]    	=     ldm(lowercase__	,	num_inference_steps=2	,	output_type="""numpy"""					).images

           assert image.shape == (1, 64, 64, 3)







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






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           a_    :       List[Any]    	=     load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/vq_diffusion/teddy_bear_pool.png"""					)
           a_    :       Dict    	=     init_image.resize((64, 64)	,	resample=PIL_INTERPOLATION["""lanczos"""]					)

           a_    :       List[str]    	=     LDMSuperResolutionPipeline.from_pretrained("""duongna/ldm-super-resolution"""	,	device_map="""auto"""					)
           ldm.set_progress_bar_config(disable=lowercase__					)

           a_    :       Optional[int]    	=     torch.manual_seed(0					)
           a_    :       Optional[Any]    	=     ldm(image=lowercase__	,	generator=lowercase__	,	num_inference_steps=20	,	output_type="""numpy"""					).images

           a_    :       Any    	=     image[0, -3:, -3:, -1]

           assert image.shape == (1, 256, 256, 3)
           a_    :       int    	=     np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]					)

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






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_    :       Optional[Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

          if split_mlp_wi:
               a_    :       Tuple    	=     tax_mlp_wi_a
               a_    :       Dict    	=     tax_mlp_wi_a
          else:
               a_    :       Optional[Any]    	=     tax_mlp_wi

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

     # Only for layer 0:
     a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Union[str, Any]    	=     tax_encoder_rel_embedding

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_    :       int    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

          # Encoder-Decoder-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          a_    :       str    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_    :       List[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

          if split_mlp_wi:
               a_    :       int    	=     tax_mlp_wi_a
               a_    :       Optional[int]    	=     tax_mlp_wi_a
          else:
               a_    :       Tuple    	=     tax_mlp_wi

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

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

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

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

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


if __name__ == "__main__":
 lowerCAmelCase_			:     List[Any]							    =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 lowerCAmelCase_			:     str							    =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
