


"""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__			(				SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     List[str]								=							checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     str								=							flatten_dict(SCREAMING_SNAKE_CASE				)
  return flax_params
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[str]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							{}

  lowerCAmelCase     :     Optional[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",
  }

  lowerCAmelCase     :     Union[str, 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
      lowerCAmelCase     :     int								=							".".join(key[1:]				)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        lowerCAmelCase     :     Tuple								=							new_key.replace(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          lowerCAmelCase     :     Any								=							new_key.replace(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        lowerCAmelCase     :     Union[str, Any]								=							re.sub(r"layers_(\d+)"				,      r"layer.\1"				,      SCREAMING_SNAKE_CASE				)
        lowerCAmelCase     :     List[str]								=							new_key.replace("encoder"				,      "encoder.encoder"				)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        lowerCAmelCase     :     List[Any]								=							re.sub(r"layers_(\d+)"				,      r"layer.\1"				,      SCREAMING_SNAKE_CASE				)

      lowerCAmelCase     :     List[str]								=							flax_dict[key]

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

  return converted_torch_dict
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Any=False				,      SCREAMING_SNAKE_CASE    :    Tuple=False				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							get_flax_param(SCREAMING_SNAKE_CASE				)

  if not use_large:
    lowerCAmelCase     :     Dict								=							PixaStructVisionConfig()
    lowerCAmelCase     :     List[Any]								=							PixaStructTextConfig()
  else:
    lowerCAmelCase     :     List[str]								=							PixaStructVisionConfig(
        hidden_size=1_5_3_6				,      d_ff=3_9_6_8				,      num_attention_heads=2_4				,      num_hidden_layers=1_8				)
    lowerCAmelCase     :     str								=							PixaStructTextConfig(hidden_size=1_5_3_6				,      d_ff=3_9_6_8				,      num_heads=2_4				,      num_layers=1_8				)
  lowerCAmelCase     :     int								=							PixaStructConfig(
      vision_config=encoder_config.to_dict()				,      text_config=decoder_config.to_dict()				,      is_vqa=SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     int								=							PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Optional[int]								=							rename_and_convert_flax_params(SCREAMING_SNAKE_CASE				)
  model.load_state_dict(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     str								=							AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"				)
  lowerCAmelCase     :     Any								=							PixaStructImageProcessor()
  lowerCAmelCase     :     Optional[int]								=							PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE				,      tokenizer=SCREAMING_SNAKE_CASE				)

  if use_large:
    lowerCAmelCase     :     str								=							4_0_9_6

  lowerCAmelCase     :     Optional[Any]								=							True

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

  model.save_pretrained(SCREAMING_SNAKE_CASE				)
  processor.save_pretrained(SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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.''')
     lowerCAmelCase__      				=   parser.parse_args()

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








from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Union[str, Any]=1_3,lowercase_ :       Tuple=3_0,lowercase_ :       List[Any]=2,lowercase_ :       Optional[int]=3,lowercase_ :       Union[str, Any]=True,lowercase_ :       Tuple=True,lowercase_ :       Any=3_2,lowercase_ :       List[str]=2,lowercase_ :       Optional[int]=4,lowercase_ :       Union[str, Any]=3_7,lowercase_ :       Tuple="gelu",lowercase_ :       str=0.1,lowercase_ :       Tuple=0.1,lowercase_ :       Union[str, Any]=1_0,lowercase_ :       int=0.02,lowercase_ :       List[Any]=3,lowercase_ :       Any=None,)->    Dict:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   patch_size
        A__          =   num_channels
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        A__          =   (image_size // patch_size) ** 2
        A__          =   num_patches + 1


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        A__          =   None
        if self.use_labels:
            A__          =   ids_tensor([self.batch_size],self.type_sequence_label_size		)

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        return ViTConfig(
            image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,is_decoder=lowercase_,initializer_range=self.initializer_range,)


    def 						snake_case__     (      self :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   TFViTModel(config=lowercase_		)
        A__          =   model(lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        A__          =   (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   self.type_sequence_label_size
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   model(lowercase_,labels=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A__          =   1
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       int		)->    List[Any]:





        '''simple docstring'''







        A__          =   TFViTModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer)		)
            A__          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase_,tf.keras.layers.Layer		)		)


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            A__          =   inspect.signature(model.call		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A__          =   [*signature.parameters.keys()]

            A__          =   ['pixel_values']
            self.assertListEqual(arg_names[:1],lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
        self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






    A__          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
    return image


@require_tf
@require_vision
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='tf'		)

        # forward pass
        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   tf.TensorShape((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

        tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4		)

