





"""simple docstring"""





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import BridgeTowerImageProcessor




class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def __init__(							self       :      Tuple						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Tuple = True						,				__lowercase       :      Tuple = None						,				__lowercase       :      Dict = 32						,				__lowercase       :      Optional[Any] = True						,				__lowercase       :      List[str] = 1 / 255						,				__lowercase       :      Dict = True						,				__lowercase       :      int = True						,				__lowercase       :      Optional[Any] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]						,				__lowercase       :      Union[str, Any] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]						,				__lowercase       :      Any = True						,				__lowercase       :      List[Any]=7						,				__lowercase       :      List[Any]=30						,				__lowercase       :      Any=400						,				__lowercase       :      str=3						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						parent
        __UpperCAmelCase    :						str       =						do_resize
        __UpperCAmelCase    :						str       =						size if size is not None else {"shortest_edge": 288}
        __UpperCAmelCase    :						Tuple       =						size_divisor
        __UpperCAmelCase    :						Optional[int]       =						do_rescale
        __UpperCAmelCase    :						Tuple       =						rescale_factor
        __UpperCAmelCase    :						List[Any]       =						do_normalize
        __UpperCAmelCase    :						List[str]       =						do_center_crop
        __UpperCAmelCase    :						List[Any]       =						image_mean
        __UpperCAmelCase    :						Optional[int]       =						image_std
        __UpperCAmelCase    :						Any       =						do_pad
        __UpperCAmelCase    :						List[str]       =						batch_size
        __UpperCAmelCase    :						Tuple       =						num_channels
        __UpperCAmelCase    :						List[str]       =						min_resolution
        __UpperCAmelCase    :						Union[str, Any]       =						max_resolution

      def       A_					(							self       :      str ):


        '''simple docstring'''




        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }







      def       A_					(							self       :      Any						,				__lowercase       :      List[Any]						,				__lowercase       :      Dict=False ):


        '''simple docstring'''




        if not batched:
          __UpperCAmelCase    :						int       =						self.size["shortest_edge"]
          __UpperCAmelCase    :						List[str]       =						image_inputs[0]
          if isinstance(__lowercase						,				Image.Image ):
            __UpperCAmelCase    :						Optional[Any]       =						image.size
          else:
            __UpperCAmelCase    :						Dict       =						image.shape[1], image.shape[2]
          __UpperCAmelCase    :						Any       =						size / min(__lowercase						,				__lowercase )
          if h < w:
            __UpperCAmelCase    :						Union[str, Any]       =						size, scale * w
          else:
            __UpperCAmelCase    :						Dict       =						scale * h, size

          __UpperCAmelCase    :						Union[str, Any]       =						int((1_333 / 800) * size )
          if max(__lowercase						,				__lowercase ) > max_size:
            __UpperCAmelCase    :						int       =						max_size / max(__lowercase						,				__lowercase )
            __UpperCAmelCase    :						Tuple       =						newh * scale
            __UpperCAmelCase    :						List[Any]       =						neww * scale

          __UpperCAmelCase    :						Optional[int]       =						int(newh + 0.5 ), int(neww + 0.5 )
          __UpperCAmelCase    :						Dict       =						(
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

        else:
          __UpperCAmelCase    :						Union[str, Any]       =						[]
          for image in image_inputs:
            __UpperCAmelCase    :						Optional[Any]       =						self.get_expected_values([image] )
            expected_values.append((expected_height, expected_width) )
          __UpperCAmelCase    :						Tuple       =						max(__lowercase						,				key=lambda __lowercase : item[0] )[0]
          __UpperCAmelCase    :						List[Any]       =						max(__lowercase						,				key=lambda __lowercase : item[1] )[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 							snake_case							(			_A      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       BridgeTowerImageProcessor if is_vision_available() else None

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						BridgeTowerImageProcessingTester(self )

      @property
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(__lowercase						,				'''image_mean''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''image_std''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''do_normalize''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''do_resize''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''size''' ) )
        self.assertTrue(hasattr(__lowercase						,				'''size_divisor''' ) )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.image_processing_class(**self.image_processor_dict )
        # create random PIL images
        __UpperCAmelCase    :						List[str]       =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						,				Image.Image )

        # Test not batched input
        __UpperCAmelCase    :						Union[str, Any]       =						image_processing(image_inputs[0]						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						List[Any]       =						self.image_processor_tester.get_expected_values(__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

        # Test batched
        __UpperCAmelCase    :						Optional[Any]       =						image_processing(__lowercase						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						List[Any]       =						self.image_processor_tester.get_expected_values(__lowercase						,				batched=__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						,				)

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.image_processing_class(**self.image_processor_dict )
        # create random numpy tensors
        __UpperCAmelCase    :						List[Any]       =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__lowercase						,				numpify=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						,				np.ndarray )

        # Test not batched input
        __UpperCAmelCase    :						str       =						image_processing(image_inputs[0]						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						int       =						self.image_processor_tester.get_expected_values(__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

        # Test batched
        __UpperCAmelCase    :						Optional[int]       =						image_processing(__lowercase						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						List[Any]       =						self.image_processor_tester.get_expected_values(__lowercase						,				batched=__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						,				)







      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						self.image_processing_class(**self.image_processor_dict )
        # create random PyTorch tensors
        __UpperCAmelCase    :						str       =						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=__lowercase						,				torchify=__lowercase )
        for image in image_inputs:
          self.assertIsInstance(__lowercase						,				torch.Tensor )

        # Test not batched input
        __UpperCAmelCase    :						List[Any]       =						image_processing(image_inputs[0]						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						List[str]       =						self.image_processor_tester.get_expected_values(__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,				)

        # Test batched
        __UpperCAmelCase    :						str       =						image_processing(__lowercase						,				return_tensors='''pt''' ).pixel_values

        __UpperCAmelCase    :						List[str]       =						self.image_processor_tester.get_expected_values(__lowercase						,				batched=__lowercase )
        self.assertEqual(
            encoded_images.shape						,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )						,				)






"""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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						Any       =						{
      '''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    :						Optional[int]       =						{
      '''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    :						Optional[Any]       =						'''.'''.join(key[1:]  )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __UpperCAmelCase    :						List[str]       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __UpperCAmelCase    :						Dict       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

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

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

      __UpperCAmelCase    :						Dict       =						flax_dict[key]

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

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

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

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

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

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						True

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

  model.save_pretrained(UpperCAmelCase_  )
  processor.save_pretrained(UpperCAmelCase_  )

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


if __name__ == "__main__":
  lowercase__				:List[str]				   =       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.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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