






'''simple docstring'''






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= checkpoints.load_tax_checkpoint(__snake_case  )
   __snake_case				:      Dict     							= flatten_dict(__snake_case  )
   return flax_params




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  )    -> Optional[Any]:







   """simple docstring"""
   __snake_case				:      List[Any]     							= {}

   __snake_case				:      Tuple     							= {
       'token_embedder': 'embeddings',
       'encoder_norm': 'layernorm',
       'kernel': 'weight',
       '.out': '.output',
       'scale': 'weight',
       'embedders_0.pos_embedding': 'row_embedder.weight',
       'embedders_1.pos_embedding': 'column_embedder.weight',
   }

   __snake_case				:      Optional[Any]     							= {
       'query': 'attention.query',
       'key': 'attention.key',
       'value': 'attention.value',
       'output.dense': 'output',
       'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
       'pre_self_attention_layer_norm': 'self_attention.layer_norm',
       'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
       'mlp.': 'mlp.DenseReluDense.',
       'pre_mlp_layer_norm': 'mlp.layer_norm',
       'self_attention.o': 'self_attention.attention.o',
       'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
       'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
       'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
       'decoder.logits_dense.weight': 'decoder.lm_head.weight',
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         __snake_case				:      Union[str, Any]     							= '.'.join(key[1:]  )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            __snake_case				:      Any     							= new_key.replace(__snake_case  ,		__snake_case  )

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               __snake_case				:      Optional[int]     							= new_key.replace(__snake_case  ,		__snake_case  )

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            __snake_case				:      Dict     							= re.sub(R'layers_(\d+)'  ,		R'layer.\1'  ,		__snake_case  )
            __snake_case				:      Optional[int]     							= new_key.replace('encoder'  ,		'encoder.encoder'  )

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            __snake_case				:      List[str]     							= re.sub(R'layers_(\d+)'  ,		R'layer.\1'  ,		__snake_case  )

         __snake_case				:      Optional[int]     							= flax_dict[key]

   __snake_case				:      str     							= {}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         __snake_case				:      List[Any]     							= torch.from_numpy(converted_dict[key].T  )
      else:
         __snake_case				:      Optional[Any]     							= torch.from_numpy(converted_dict[key]  )

   return converted_torch_dict




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Any  ,		A  :       str=False  ,		A  :       Dict=False  )    -> Any:







   """simple docstring"""
   __snake_case				:      List[Any]     							= get_flax_param(__snake_case  )

   if not use_large:
      __snake_case				:      Tuple     							= PixaStructVisionConfig()
      __snake_case				:      Optional[Any]     							= PixaStructTextConfig()
   else:
      __snake_case				:      List[Any]     							= PixaStructVisionConfig(
          hidden_size=15_36  ,		d_ff=39_68  ,		num_attention_heads=24  ,		num_hidden_layers=18  )
      __snake_case				:      Any     							= PixaStructTextConfig(hidden_size=15_36  ,		d_ff=39_68  ,		num_heads=24  ,		num_layers=18  )
   __snake_case				:      List[Any]     							= PixaStructConfig(
       vision_config=encoder_config.to_dict()  ,		text_config=decoder_config.to_dict()  ,		is_vqa=__snake_case  )

   __snake_case				:      Any     							= PixaStructForConditionalGeneration(__snake_case  )

   __snake_case				:      Dict     							= rename_and_convert_flax_params(__snake_case  )
   model.load_state_dict(__snake_case  )

   __snake_case				:      Union[str, Any]     							= AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'  )
   __snake_case				:      Dict     							= PixaStructImageProcessor()
   __snake_case				:      Optional[Any]     							= PixaStructProcessor(image_processor=__snake_case  ,		tokenizer=__snake_case  )

   if use_large:
      __snake_case				:      Optional[Any]     							= 40_96

   __snake_case				:      Optional[int]     							= True

   # mkdir if needed
   os.makedirs(__snake_case  ,		exist_ok=__snake_case  )

   model.save_pretrained(__snake_case  )
   processor.save_pretrained(__snake_case  )

   print('Model saved in {}'.format(__snake_case  )  )


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
      parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
      __A    	=   parser.parse_args()

      convert_pixastruct_original_pytorch_checkpoint_to_hf(
          args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
      )







'''simple docstring'''






import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()




class a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case				:      List[Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy'					,   output_loading_info=__a)
      self.assertIsNotNone(__a)
      self.assertEqual(len(loading_info['missing_keys'])					,   0)

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

      assert image is not None, "Make sure output is not None"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

      torch.manual_seed(0)
      if torch.cuda.is_available():
         torch.cuda.manual_seed_all(0)

      __snake_case				:      Tuple     							= torch.randn(1					,   model.config.in_channels					,   model.config.sample_size					,   model.config.sample_size)
      __snake_case				:      Optional[int]     							= image.to(__a)
      with torch.no_grad():
         __snake_case				:      List[Any]     							= model(__a).sample

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143])
      # fmt: on
      self.assertTrue(torch.allclose(__a					,   __a					,   atol=1E-3))