




'''simple docstring'''



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCAmelCase_    :					Optional[Any]					      =						logging.get_logger(__name__)  # pylint: disable=invalid-name





class       lowerCAmelCase				(	UpperCAmelCase__):
       __lowercase					: int	    =     ["""safety_checker""", """feature_extractor"""]


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = True      ,					) ->					List[str]:



            '''simple docstring'''




            super().__init__()
            if hasattr(scheduler.config      ,					'''steps_offset'''					) and scheduler.config.steps_offset != 1:
                 __snake_case         =					(
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                     ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                     ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                     ' file'
                 )
                 deprecate('''steps_offset!=1'''      ,					'''1.0.0'''      ,					snake_case_      ,					standard_warn=snake_case_					)
                 __snake_case         =					dict(scheduler.config					)
                 __snake_case         =					1
                 __snake_case         =					FrozenDict(snake_case_					)

            self.register_modules(
                vae=snake_case_      ,					text_encoder=snake_case_      ,					tokenizer=snake_case_      ,					unet=snake_case_      ,					scheduler=snake_case_      ,					safety_checker=snake_case_      ,					feature_extractor=snake_case_      ,					)
            self.register_to_config(requires_safety_checker=snake_case_					)


       @property
       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            return {k: getattr(self      ,					snake_case_					) for k in self.config.keys() if not k.startswith('''_'''					)}


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE = "auto"					) ->					int:



            '''simple docstring'''




            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 __snake_case         =					self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(snake_case_					)


       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            self.enable_attention_slicing(snake_case_					)


       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 0.8      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					) ->					Optional[Any]:



            '''simple docstring'''




            return StableDiffusionInpaintPipelineLegacy(**self.components					)(
                prompt=snake_case_      ,					image=snake_case_      ,					mask_image=snake_case_      ,					strength=snake_case_      ,					num_inference_steps=snake_case_      ,					guidance_scale=snake_case_      ,					negative_prompt=snake_case_      ,					num_images_per_prompt=snake_case_      ,					eta=snake_case_      ,					generator=snake_case_      ,					output_type=snake_case_      ,					return_dict=snake_case_      ,					callback=snake_case_      ,					)


       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 0.8      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					str:



            '''simple docstring'''




            return StableDiffusionImgaImgPipeline(**self.components					)(
                prompt=snake_case_      ,					image=snake_case_      ,					strength=snake_case_      ,					num_inference_steps=snake_case_      ,					guidance_scale=snake_case_      ,					negative_prompt=snake_case_      ,					num_images_per_prompt=snake_case_      ,					eta=snake_case_      ,					generator=snake_case_      ,					output_type=snake_case_      ,					return_dict=snake_case_      ,					callback=snake_case_      ,					callback_steps=snake_case_      ,					)




       @torch.no_grad()
       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = 512      ,					__SCREAMING_SNAKE_CASE = 512      ,					__SCREAMING_SNAKE_CASE = 50      ,					__SCREAMING_SNAKE_CASE = 7.5      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					__SCREAMING_SNAKE_CASE = 0.0      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = "pil"      ,					__SCREAMING_SNAKE_CASE = True      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = 1      ,					) ->					List[Any]:



            '''simple docstring'''




            return StableDiffusionPipeline(**self.components					)(
                prompt=snake_case_      ,					height=snake_case_      ,					width=snake_case_      ,					num_inference_steps=snake_case_      ,					guidance_scale=snake_case_      ,					negative_prompt=snake_case_      ,					num_images_per_prompt=snake_case_      ,					eta=snake_case_      ,					generator=snake_case_      ,					latents=snake_case_      ,					output_type=snake_case_      ,					return_dict=snake_case_      ,					callback=snake_case_      ,					callback_steps=snake_case_      ,					)












'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def      _UpperCamelCase	(_lowerCamelCase							:       Optional[Any]				,		_lowerCamelCase							:       List[Any]				,		_lowerCamelCase							:       Dict		)->				Dict:




     '''simple docstring'''





     __snake_case         =					TaConfig.from_pretrained(_lowerCamelCase		)
     __snake_case         =					FlaxTaForConditionalGeneration(config=_lowerCamelCase		)
     __snake_case         =					checkpoints.load_tax_checkpoint(_lowerCamelCase		)

     __snake_case         =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

     # Encoder
     for layer_index in range(config.num_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

          # Self-Attention
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_attention_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo
          __snake_case         =					tax_mlp_layer_norm

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

     # Assigning
     __snake_case         =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
     __snake_case         =					tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers		):
          __snake_case         =					f'''layers_{str(_lowerCamelCase		)}'''

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

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]

          # Encoder-Decoder-Attention
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

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

          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

          # Layer Normalization
          __snake_case         =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

          # Assigning
          __snake_case         =					tax_attention_key
          __snake_case         =					tax_attention_out
          __snake_case         =					tax_attention_query
          __snake_case         =					tax_attention_value

          __snake_case         =					tax_pre_attention_layer_norm

          __snake_case         =					tax_enc_dec_attention_key
          __snake_case         =					tax_enc_dec_attention_out
          __snake_case         =					tax_enc_dec_attention_query
          __snake_case         =					tax_enc_dec_attention_value

          __snake_case         =					tax_cross_layer_norm

          if split_mlp_wi:
               __snake_case         =					tax_mlp_wi_a
               __snake_case         =					tax_mlp_wi_a
          else:
               __snake_case         =					tax_mlp_wi

          __snake_case         =					tax_mlp_wo

          __snake_case         =					txa_mlp_layer_norm

     # Decoder Normalization
     __snake_case         =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
     __snake_case         =					txa_decoder_norm

     # Only for layer 0:
     __snake_case         =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
     __snake_case         =					tax_decoder_rel_embedding

     # Token Embeddings
     __snake_case         =					tax_model['''target''']['''token_embedder''']['''embedding''']
     __snake_case         =					txa_token_embeddings

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

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


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






