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        lowerCAmelCase_			(					__A							)					->    List[str]:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(__A							)
      UpperCAmelCase__								=  flatten_dict(__A							)
      return flax_params




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  {}

      UpperCAmelCase__								=  {
          "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",
      }

      UpperCAmelCase__								=  {
          "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
                  UpperCAmelCase__								=  ".".join(key[1:]							)

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

                  if "decoder" in new_key:
                        for old, new in DECODER_CONVERSION_MAPPING.items():
                              UpperCAmelCase__								=  new_key.replace(__A,  __A							)

                  if "layers" in new_key and "decoder" not in new_key:
                        # use regex to replace the layer number
                        UpperCAmelCase__								=  re.sub(r"layers_(\d+)",  r"layer.\1",  __A							)
                        UpperCAmelCase__								=  new_key.replace("encoder",  "encoder.encoder"							)

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

                  UpperCAmelCase__								=  flax_dict[key]

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

      return converted_torch_dict




def        lowerCAmelCase_			(					__A,  __A,  __A=False,  __A=False							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  get_flax_param(__A							)

      if not use_large:
            UpperCAmelCase__								=  PixaStructVisionConfig()
            UpperCAmelCase__								=  PixaStructTextConfig()
      else:
            UpperCAmelCase__								=  PixaStructVisionConfig(
                hidden_size=1_536,  d_ff=3_968,  num_attention_heads=24,  num_hidden_layers=18							)
            UpperCAmelCase__								=  PixaStructTextConfig(hidden_size=1_536,  d_ff=3_968,  num_heads=24,  num_layers=18							)
      UpperCAmelCase__								=  PixaStructConfig(
          vision_config=encoder_config.to_dict(),  text_config=decoder_config.to_dict(),  is_vqa=__A							)

      UpperCAmelCase__								=  PixaStructForConditionalGeneration(__A							)

      UpperCAmelCase__								=  rename_and_convert_flax_params(__A							)
      model.load_state_dict(__A							)

      UpperCAmelCase__								=  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"							)
      UpperCAmelCase__								=  PixaStructImageProcessor()
      UpperCAmelCase__								=  PixaStructProcessor(image_processor=__A,  tokenizer=__A							)

      if use_large:
            UpperCAmelCase__								=  4_096

      UpperCAmelCase__								=  True

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

      model.save_pretrained(__A							)
      processor.save_pretrained(__A							)

      print("Model saved in {}".format(__A							)							)


if __name__ == "__main__":
      UpperCamelCase__    							= 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.')
      UpperCamelCase__    							= 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 Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""		)

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



