





"""simple docstring"""





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwiftFormerForImageClassification, SwiftFormerModel
  from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor




class 							snake_case							:

      '''simple docstring'''



      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any						,				__lowercase       :      List[Any]=13						,				__lowercase       :      List[Any]=3						,				__lowercase       :      Union[str, Any]=True						,				__lowercase       :      Tuple=True						,				__lowercase       :      Dict=0.1						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Tuple=224						,				__lowercase       :      str=1_000						,				__lowercase       :      List[str]=[3, 3, 6, 4]						,				__lowercase       :      Dict=[48, 56, 112, 220]						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						parent
        __UpperCAmelCase    :						Optional[int]       =						batch_size
        __UpperCAmelCase    :						Any       =						num_channels
        __UpperCAmelCase    :						str       =						is_training
        __UpperCAmelCase    :						Optional[Any]       =						use_labels
        __UpperCAmelCase    :						Optional[Any]       =						hidden_dropout_prob
        __UpperCAmelCase    :						Tuple       =						attention_probs_dropout_prob
        __UpperCAmelCase    :						Optional[int]       =						num_labels
        __UpperCAmelCase    :						Tuple       =						image_size
        __UpperCAmelCase    :						int       =						layer_depths
        __UpperCAmelCase    :						Dict       =						embed_dims

      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        __UpperCAmelCase    :						Union[str, Any]       =						None
        if self.use_labels:
          __UpperCAmelCase    :						Tuple       =						ids_tensor([self.batch_size]						,				self.num_labels )

        __UpperCAmelCase    :						Union[str, Any]       =						self.get_config()

        return config, pixel_values, labels

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return SwiftFormerConfig(
            depths=self.layer_depths						,				embed_dims=self.embed_dims						,				mlp_ratio=4						,				downsamples=[True, True, True, True]						,				hidden_act='''gelu'''						,				num_labels=self.num_labels						,				down_patch_size=3						,				down_stride=2						,				down_pad=1						,				drop_rate=0.0						,				drop_path_rate=0.0						,				use_layer_scale=__lowercase						,				layer_scale_init_value=1e-5						,				)

      def       A_					(							self       :      Dict						,				__lowercase       :      Optional[Any]						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						SwiftFormerModel(config=__lowercase )
        model.to(__lowercase )
        model.eval()
        __UpperCAmelCase    :						Optional[Any]       =						model(__lowercase )
        self.parent.assertEqual(result.last_hidden_state.shape						,				(self.batch_size, self.embed_dims[-1], 7, 7) )

      def       A_					(							self       :      Union[str, Any]						,				__lowercase       :      Optional[int]						,				__lowercase       :      List[str]						,				__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.num_labels
        __UpperCAmelCase    :						int       =						SwiftFormerForImageClassification(__lowercase )
        model.to(__lowercase )
        model.eval()
        __UpperCAmelCase    :						Optional[int]       =						model(__lowercase						,				labels=__lowercase )
        self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels) )

        __UpperCAmelCase    :						Optional[Any]       =						SwiftFormerForImageClassification(__lowercase )
        model.to(__lowercase )
        model.eval()

        __UpperCAmelCase    :						Optional[int]       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
        __UpperCAmelCase    :						int       =						model(__lowercase )
        self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels) )







      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        ((__UpperCAmelCase)       ,     (__UpperCAmelCase)       ,     (__UpperCAmelCase))    :						Optional[int]       =						self.prepare_config_and_inputs()
        __UpperCAmelCase    :						int       =						{'''pixel_values''': pixel_values}
        return config, inputs_dict




@require_torch
class 							snake_case							(			__UpperCAmelCase      ,  __UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Union[str, Any]         =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      _A     :       str         =       (
          {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      _A     :       Union[str, Any]         =       False
      _A     :       Dict         =       False
      _A     :       Any         =       False
      _A     :       Tuple         =       False
      _A     :       str         =       False

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						SwiftFormerModelTester(self )
        __UpperCAmelCase    :						int       =						ConfigTester(
            self						,				config_class=__lowercase						,				has_text_modality=__lowercase						,				hidden_size=37						,				num_attention_heads=12						,				num_hidden_layers=12						,				)

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        self.config_tester.run_common_tests()

      @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
      def       A_					(							self       :      str ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						Optional[int]       =						model_class(__lowercase )
          __UpperCAmelCase    :						str       =						model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(__lowercase						,				nn.Linear ) )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						str       =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						Dict       =						model_class(__lowercase )
          __UpperCAmelCase    :						Tuple       =						inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase    :						List[str]       =						[*signature.parameters.keys()]

          __UpperCAmelCase    :						List[Any]       =						['''pixel_values''']
          self.assertListEqual(arg_names[:1]						,				__lowercase )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase )

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


        '''simple docstring'''




        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase    :						Tuple       =						SwiftFormerModel.from_pretrained(__lowercase )
          self.assertIsNotNone(__lowercase )

      @unittest.skip(reason='''SwiftFormer does not output attentions''' )
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        pass

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        def check_hidden_states_output(__lowercase       :      Optional[Any]						,				__lowercase       :      Tuple						,				__lowercase       :      Union[str, Any] ):
          __UpperCAmelCase    :						Optional[int]       =						model_class(__lowercase )
          model.to(__lowercase )
          model.eval()

          with torch.no_grad():
            __UpperCAmelCase    :						List[Any]       =						model(**self._prepare_for_class(__lowercase						,				__lowercase ) )

          __UpperCAmelCase    :						Optional[Any]       =						outputs.hidden_states

          __UpperCAmelCase    :						Dict       =						8
          self.assertEqual(len(__lowercase )						,				__lowercase )  # TODO

          # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
          # with the width and height being successively divided by 2, after every 2 blocks
          for i in range(len(__lowercase ) ):
            self.assertEqual(
                hidden_states[i].shape						,				torch.Size(
                    [
                        self.model_tester.batch_size,
                        self.model_tester.embed_dims[i // 2],
                        (self.model_tester.image_size // 4) // 2 ** (i // 2),
                        (self.model_tester.image_size // 4) // 2 ** (i // 2),
                    ] )						,				)

        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						List[str]       =						True
          check_hidden_states_output(__lowercase						,				__lowercase						,				__lowercase )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          __UpperCAmelCase    :						Tuple       =						True

          check_hidden_states_output(__lowercase						,				__lowercase						,				__lowercase )

      def       A_					(							self       :      str ):


        '''simple docstring'''




        def _config_zero_init(__lowercase       :      List[Any] ):
          __UpperCAmelCase    :						Optional[Any]       =						copy.deepcopy(__lowercase )
          for key in configs_no_init.__dict__.keys():
            if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
              setattr(__lowercase						,				__lowercase						,				1e-10 )
            if isinstance(getattr(__lowercase						,				__lowercase						,				__lowercase )						,				__lowercase ):
              __UpperCAmelCase    :						Optional[Any]       =						_config_zero_init(getattr(__lowercase						,				__lowercase ) )
              setattr(__lowercase						,				__lowercase						,				__lowercase )
          return configs_no_init

        __UpperCAmelCase       ,     __UpperCAmelCase    :						Union[str, Any]       =						self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase    :						List[str]       =						_config_zero_init(__lowercase )
        for model_class in self.all_model_classes:
          __UpperCAmelCase    :						int       =						model_class(config=__lowercase )
          for name, param in model.named_parameters():
            if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1e9) / 1e9).round().item()						,				[0.0, 1.0]						,				msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''						,				)







      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        pass


def       lowerCamelCase_       (   )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
  return image







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

      '''simple docstring'''



      @cached_property
      def       A_					(							self       :      str ):


        '''simple docstring'''




        return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None







      @slow
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(__lowercase )

        __UpperCAmelCase    :						List[str]       =						self.default_image_processor
        __UpperCAmelCase    :						Dict       =						prepare_img()
        __UpperCAmelCase    :						str       =						image_processor(images=__lowercase						,				return_tensors='''pt''' ).to(__lowercase )

        # forward pass
        with torch.no_grad():
          __UpperCAmelCase    :						Dict       =						model(**__lowercase )

        # verify the logits
        __UpperCAmelCase    :						int       =						torch.Size((1, 1_000) )
        self.assertEqual(outputs.logits.shape						,				__lowercase )

        __UpperCAmelCase    :						Any       =						torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]] ).to(__lowercase )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]						,				__lowercase						,				atol=1e-4 ) )






"""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
  )