




'''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      a_							(      __snake_case							:			Optional[Any]   )							->						List[Any]:



       """simple docstring"""
       lowerCamelCase_							=checkpoints.load_tax_checkpoint(snake_case_   )
       lowerCamelCase_							=flatten_dict(snake_case_   )
       return flax_params





def      a_							(      __snake_case							:			Dict   )							->						Optional[int]:



       """simple docstring"""
       lowerCamelCase_							={}

       lowerCamelCase_							={
           '''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''',
       }

       lowerCamelCase_							={
           '''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
                     lowerCamelCase_							='''.'''.join(key[1:]   )

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

                     if "decoder" in new_key:
                            for old, new in DECODER_CONVERSION_MAPPING.items():
                                   lowerCamelCase_							=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
                            lowerCamelCase_							=re.sub(r'''layers_(\d+)'''   ,       r'''layer.\1'''   ,       snake_case_   )
                            lowerCamelCase_							=new_key.replace('''encoder'''   ,       '''encoder.encoder'''   )

                     elif "layers" in new_key and "decoder" in new_key:
                            # use regex to replace the layer number
                            lowerCamelCase_							=re.sub(r'''layers_(\d+)'''   ,       r'''layer.\1'''   ,       snake_case_   )

                     lowerCamelCase_							=flax_dict[key]

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

       return converted_torch_dict





def      a_							(      __snake_case							:			Optional[int]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Union[str, Any]=False   ,       __snake_case							:			Optional[int]=False   )							->						Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=get_flax_param(snake_case_   )

       if not use_large:
              lowerCamelCase_							=PixaStructVisionConfig()
              lowerCamelCase_							=PixaStructTextConfig()
       else:
              lowerCamelCase_							=PixaStructVisionConfig(
                  hidden_size=1536   ,       d_ff=3968   ,       num_attention_heads=24   ,       num_hidden_layers=18   )
              lowerCamelCase_							=PixaStructTextConfig(hidden_size=1536   ,       d_ff=3968   ,       num_heads=24   ,       num_layers=18   )
       lowerCamelCase_							=PixaStructConfig(
           vision_config=encoder_config.to_dict()   ,       text_config=decoder_config.to_dict()   ,       is_vqa=snake_case_   )

       lowerCamelCase_							=PixaStructForConditionalGeneration(snake_case_   )

       lowerCamelCase_							=rename_and_convert_flax_params(snake_case_   )
       model.load_state_dict(snake_case_   )

       lowerCamelCase_							=AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''   )
       lowerCamelCase_							=PixaStructImageProcessor()
       lowerCamelCase_							=PixaStructProcessor(image_processor=snake_case_   ,       tokenizer=snake_case_   )

       if use_large:
              lowerCamelCase_							=4096

       lowerCamelCase_							=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_      :		Tuple							      =       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_      :		Dict							      =       parser.parse_args()

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











from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

