


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		List[str]							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Dict   	=		FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Tuple   	=		checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	int   	=		'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              UpperCamelCase    :	Dict   	=		'''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              UpperCamelCase    :	List[str]   	=		'''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCamelCase    :	Optional[int]   	=		'''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       ):
              UpperCamelCase    :	Tuple   	=		F"""layers_{str(SCREAMING_SNAKE_CASE_       )}"""

              # Self-Attention
              UpperCamelCase    :	Dict   	=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              UpperCamelCase    :	Optional[int]   	=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              UpperCamelCase    :	str   	=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              UpperCamelCase    :	Any   	=		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":
                     UpperCamelCase    :	Tuple   	=		tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              UpperCamelCase    :	Any   	=		tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     UpperCamelCase    :	str   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     UpperCamelCase    :	Union[str, Any]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     UpperCamelCase    :	Optional[int]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              UpperCamelCase    :	List[Any]   	=		tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	List[str]   	=		tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              UpperCamelCase    :	Union[str, Any]   	=		flax_model.params['''encoder''']['''block'''][str(SCREAMING_SNAKE_CASE_       )]['''layer''']
              UpperCamelCase    :	str   	=		tax_attention_key
              UpperCamelCase    :	int   	=		tax_attention_out
              UpperCamelCase    :	Dict   	=		tax_attention_query
              UpperCamelCase    :	Dict   	=		tax_attention_value

              UpperCamelCase    :	int   	=		tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     UpperCamelCase    :	List[str]   	=		tax_global_layer_norm

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

              UpperCamelCase    :	Tuple   	=		tax_mlp_wo
              UpperCamelCase    :	Dict   	=		tax_mlp_layer_norm

              UpperCamelCase    :	Tuple   	=		flax_model_encoder_layer_block

       # Only for layer 0:
       UpperCamelCase    :	Any   	=		tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       UpperCamelCase    :	Any   	=		tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              UpperCamelCase    :	List[str]   	=		tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              UpperCamelCase    :	Optional[Any]   	=		tax_encoder_global_rel_embedding

       # Assigning
       UpperCamelCase    :	Any   	=		tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       UpperCamelCase    :	List[Any]   	=		tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers       ):
              UpperCamelCase    :	Any   	=		F"""layers_{str(SCREAMING_SNAKE_CASE_       )}"""

              # Self-Attention
              UpperCamelCase    :	Union[str, Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              UpperCamelCase    :	List[Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              UpperCamelCase    :	List[str]   	=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              UpperCamelCase    :	str   	=		tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	Union[str, Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              UpperCamelCase    :	Optional[int]   	=		tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              UpperCamelCase    :	Tuple   	=		tax_enc_dec_attention_module['''key''']['''kernel''']
              UpperCamelCase    :	Dict   	=		tax_enc_dec_attention_module['''out''']['''kernel''']
              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_module['''query''']['''kernel''']
              UpperCamelCase    :	Optional[Any]   	=		tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     UpperCamelCase    :	Optional[Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     UpperCamelCase    :	Tuple   	=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              UpperCamelCase    :	Optional[Any]   	=		tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              UpperCamelCase    :	int   	=		flax_model.params['''decoder''']['''block'''][str(SCREAMING_SNAKE_CASE_       )]['''layer''']
              UpperCamelCase    :	str   	=		tax_attention_key
              UpperCamelCase    :	str   	=		tax_attention_out
              UpperCamelCase    :	Union[str, Any]   	=		tax_attention_query
              UpperCamelCase    :	List[Any]   	=		tax_attention_value

              UpperCamelCase    :	int   	=		tax_pre_attention_layer_norm

              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_key
              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_out
              UpperCamelCase    :	Optional[int]   	=		tax_enc_dec_attention_query
              UpperCamelCase    :	Union[str, Any]   	=		tax_enc_dec_attention_value

              UpperCamelCase    :	List[str]   	=		tax_cross_layer_norm

              if split_mlp_wi:
                     UpperCamelCase    :	str   	=		tax_mlp_wi_a
                     UpperCamelCase    :	Optional[Any]   	=		tax_mlp_wi_a
              else:
                     UpperCamelCase    :	List[str]   	=		tax_mlp_wi

              UpperCamelCase    :	Tuple   	=		tax_mlp_wo

              UpperCamelCase    :	int   	=		txa_mlp_layer_norm

              UpperCamelCase    :	List[Any]   	=		flax_model_decoder_layer_block

       # Decoder Normalization
       UpperCamelCase    :	Optional[Any]   	=		tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       UpperCamelCase    :	Optional[Any]   	=		txa_decoder_norm

       # Only for layer 0:
       UpperCamelCase    :	int   	=		tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       UpperCamelCase    :	Union[str, Any]   	=		tax_decoder_rel_embedding

       # Token Embeddings
       UpperCamelCase    :	int   	=		tax_model['''target''']['''token_embedder''']['''embedding''']
       UpperCamelCase    :	int   	=		txa_token_embeddings

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

       flax_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
       print('''T5X Model was sucessfully converted!'''       )


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





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

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

