




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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Tuple							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			checkpoints.load_tax_checkpoint(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			flatten_dict(__UpperCAmelCase							)
      return flax_params

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[int]							)					-> Optional[int]:
      SCREAMING_SNAKE_CASE_							=			{}

      SCREAMING_SNAKE_CASE_							=			{
          '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',
      }

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

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

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

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

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

                  SCREAMING_SNAKE_CASE_							=			flax_dict[key]

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

      return converted_torch_dict

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	List[Any]			,			__UpperCAmelCase						:	Optional[int]=False			,			__UpperCAmelCase						:	Any=False							)					-> Optional[int]:
      SCREAMING_SNAKE_CASE_							=			get_flax_param(__UpperCAmelCase							)

      if not use_large:
            SCREAMING_SNAKE_CASE_							=			PixaStructVisionConfig()
            SCREAMING_SNAKE_CASE_							=			PixaStructTextConfig()
      else:
            SCREAMING_SNAKE_CASE_							=			PixaStructVisionConfig(
                hidden_size=15_36			,			d_ff=39_68			,			num_attention_heads=24			,			num_hidden_layers=18							)
            SCREAMING_SNAKE_CASE_							=			PixaStructTextConfig(hidden_size=15_36			,			d_ff=39_68			,			num_heads=24			,			num_layers=18							)
      SCREAMING_SNAKE_CASE_							=			PixaStructConfig(
          vision_config=encoder_config.to_dict()			,			text_config=decoder_config.to_dict()			,			is_vqa=__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			PixaStructForConditionalGeneration(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_							=			rename_and_convert_flax_params(__UpperCAmelCase							)
      model.load_state_dict(__UpperCAmelCase							)

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

      if use_large:
            SCREAMING_SNAKE_CASE_							=			40_96

      SCREAMING_SNAKE_CASE_							=			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__":
   lowerCamelCase__ :   int					=							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__ :   Tuple					=							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

import numpy as np

from transformers import DeiTConfig
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 (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Tuple=13				,				_lowerCAmelCase  :		Dict=30				,				_lowerCAmelCase  :		Tuple=2				,				_lowerCAmelCase  :		Dict=3				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		int=32				,				_lowerCAmelCase  :		List[Any]=2				,				_lowerCAmelCase  :		Tuple=4				,				_lowerCAmelCase  :		Optional[int]=37				,				_lowerCAmelCase  :		List[Any]="gelu"				,				_lowerCAmelCase  :		Optional[Any]=0.1				,				_lowerCAmelCase  :		Any=0.1				,				_lowerCAmelCase  :		Optional[Any]=10				,				_lowerCAmelCase  :		Dict=0.02				,				_lowerCAmelCase  :		Optional[Any]=3				,				_lowerCAmelCase  :		Optional[Any]=None				,				_lowerCAmelCase  :		List[Any]=2				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			image_size
       SCREAMING_SNAKE_CASE_							=			patch_size
       SCREAMING_SNAKE_CASE_							=			num_channels
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			scope
       SCREAMING_SNAKE_CASE_							=			encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE_							=			(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE_							=			num_patches + 2



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       SCREAMING_SNAKE_CASE_							=			self.get_config()

       return config, pixel_values, labels



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return DeiTConfig(
           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=_lowerCAmelCase				,				initializer_range=self.initializer_range				,				encoder_stride=self.encoder_stride				,				)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTModel(config=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTForMaskedImageModeling(config=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(
           result.reconstruction.shape				,				(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       SCREAMING_SNAKE_CASE_							=			1
       SCREAMING_SNAKE_CASE_							=			TFDeiTForMaskedImageModeling(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.reconstruction.shape				,				(self.batch_size, 1, self.image_size, self.image_size)						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassification(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       SCREAMING_SNAKE_CASE_							=			1
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassification(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)						)




 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'pixel_values': pixel_values}
       return config, inputs_dict





@require_tf
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 lowercase_				    =    (
     {
         "feature-extraction": TFDeiTModel,
         "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=_lowerCAmelCase				,				has_text_modality=_lowerCAmelCase				,				hidden_size=37						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='DeiT does not use inputs_embeds'						)
 def       lowerCAmelCase_  (							self  :		Any						):
       pass



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

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



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_							=			['pixel_values']
             self.assertListEqual(arg_names[:1]				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Optional[Any]=False						):
       SCREAMING_SNAKE_CASE_							=			super()._prepare_for_class(_lowerCAmelCase				,				_lowerCAmelCase				,				return_labels=_lowerCAmelCase						)

       if return_labels:
             if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call						).parameters:
                   del inputs_dict["labels"]

       return inputs_dict




 @slow
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_							=			TFDeiTModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)


def 					UpperCAmelCase_							(      )					-> str:
      SCREAMING_SNAKE_CASE_							=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





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


 '''simple docstring'''







 @cached_property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return (
           DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'						)
           if is_vision_available()
           else None
       )




 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'						)

       SCREAMING_SNAKE_CASE_							=			self.default_image_processor
       SCREAMING_SNAKE_CASE_							=			prepare_img()
       SCREAMING_SNAKE_CASE_							=			image_processor(images=_lowerCAmelCase				,				return_tensors='tf'						)

       # forward pass
       SCREAMING_SNAKE_CASE_							=			model(**_lowerCAmelCase						)

       # verify the logits
       SCREAMING_SNAKE_CASE_							=			tf.TensorShape((1, 1_000)						)
       self.assertEqual(outputs.logits.shape				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			tf.constant([-1.0266, 0.1912, -1.2861]						)

       self.assertTrue(np.allclose(outputs.logits[0, :3]				,				_lowerCAmelCase				,				atol=1E-4						)						)