




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     UpperCamelCase_   :

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   num_channels
           A__							=   is_training
           A__							=   use_labels
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   num_labels
           A__							=   image_size
           A__							=   layer_depths
           A__							=   embed_dims


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Dict:





           '''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.num_labels)

           A__							=   self.get_config()

           return config, pixel_values, labels


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Union[str, Any]:





           '''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=UpperCAmelCase__ ,			layer_scale_init_value=1e-5 ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     str)						->Tuple:





           '''simple docstring'''

           A__							=   SwiftFormerModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__)
           self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.embed_dims[-1], 7, 7))


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Any)						->str:





           '''simple docstring'''

           A__							=   self.num_labels
           A__							=   SwiftFormerForImageClassification(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__							=   model(UpperCAmelCase__ ,			labels=UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels))

           A__							=   SwiftFormerForImageClassification(UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()

           A__							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
           A__							=   model(UpperCAmelCase__)
           self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels))





    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Any:





           '''simple docstring'''

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


@require_torch
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    UpperCAmelCase__							  =				(
        {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

           A__							=   SwiftFormerModelTester(self)
           A__							=   ConfigTester(
               self ,			config_class=UpperCAmelCase__ ,			has_text_modality=UpperCAmelCase__ ,			hidden_size=37 ,			num_attention_heads=12 ,			num_hidden_layers=12 ,			)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->int:





           '''simple docstring'''

           self.config_tester.run_common_tests()


    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Optional[Any]:





           '''simple docstring'''

           pass


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Any:





           '''simple docstring'''

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__							=   model_class(UpperCAmelCase__)
                  A__							=   model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(UpperCAmelCase__ ,			nn.Linear))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->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(UpperCAmelCase__)
                  A__							=   inspect.signature(model.forward)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  A__							=   [*signature.parameters.keys()]

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Union[str, Any]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

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


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[Any]:





           '''simple docstring'''

           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__							=   SwiftFormerModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)


    @unittest.skip(reason='''SwiftFormer does not output attentions''')
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Union[str, Any]:





           '''simple docstring'''

           pass


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





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Dict):
                  A__							=   model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

                  with torch.no_grad():
                         A__							=   model(**self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__))

                  A__							=   outputs.hidden_states

                  A__							=   8
                  self.assertEqual(len(UpperCAmelCase__) ,			UpperCAmelCase__)  # 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(UpperCAmelCase__)):
                         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),
                                 ]) ,			)

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__							=   True
                  check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  A__							=   True

                  check_hidden_states_output(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->int:





           '''simple docstring'''

           def _config_zero_init(UpperCAmelCase__				:     Union[str, Any]):
                  A__							=   copy.deepcopy(UpperCAmelCase__)
                  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(UpperCAmelCase__ ,			UpperCAmelCase__ ,			1e-10)
                         if isinstance(getattr(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__) ,			UpperCAmelCase__):
                                A__							=   _config_zero_init(getattr(UpperCAmelCase__ ,			UpperCAmelCase__))
                                setattr(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)
                  return configs_no_init

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           A__							=   _config_zero_init(UpperCAmelCase__)
           for model_class in self.all_model_classes:
                  A__							=   model_class(config=UpperCAmelCase__)
                  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 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Optional[int]:





           '''simple docstring'''

           pass







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



       """simple docstring"""

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


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

    '''simple docstring'''






    @cached_property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->List[Any]:





           '''simple docstring'''

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





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->str:





           '''simple docstring'''

           A__							=   SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''').to(UpperCAmelCase__)

           A__							=   self.default_image_processor
           A__							=   prepare_img()
           A__							=   image_processor(images=UpperCAmelCase__ ,			return_tensors='''pt''').to(UpperCAmelCase__)

           # forward pass
           with torch.no_grad():
                  A__							=   model(**UpperCAmelCase__)

           # verify the logits
           A__							=   torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape ,			UpperCAmelCase__)

           A__							=   torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]).to(UpperCAmelCase__)
           self.assertTrue(torch.allclose(outputs.logits[0, :3] ,			UpperCAmelCase__ ,			atol=1e-4))








import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   AutoConfig.from_pretrained(lowercase_						)
       A__							=   FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_						)
       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)

       A__							=   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       if config.model_type == "t5":
              A__							=   '''SelfAttention'''
       if config.model_type == "longt5" and config.encoder_attention_type == "local":
              A__							=   '''LocalSelfAttention'''
       elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   '''TransientGlobalSelfAttention'''
       else:
              raise ValueError(
                  '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
                  ''' attribute with a value from [\'local\', \'transient-global].'''						)

       # Encoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''encoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_attention_layer_norm

              # Global input layer norm
              if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                     A__							=   tax_global_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo
              A__							=   tax_mlp_layer_norm

              A__							=   flax_model_encoder_layer_block

       # Only for layer 0:
       A__							=   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_encoder_rel_embedding

       # Side/global relative position_bias + layer norm
       if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
              A__							=   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
              A__							=   tax_encoder_global_rel_embedding

       # Assigning
       A__							=   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       A__							=   tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_layers						):
              A__							=   f"""layers_{str(lowercase_						)}"""

              # Self-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                  '''scale'''
              ]

              # Encoder-Decoder-Attention
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
              A__							=   tax_enc_dec_attention_module['''key''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''out''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''query''']['''kernel''']
              A__							=   tax_enc_dec_attention_module['''value''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

              # Layer Normalization
              A__							=   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              A__							=   flax_model.params['''decoder''']['''block'''][str(lowercase_						)]['''layer''']
              A__							=   tax_attention_key
              A__							=   tax_attention_out
              A__							=   tax_attention_query
              A__							=   tax_attention_value

              A__							=   tax_pre_attention_layer_norm

              A__							=   tax_enc_dec_attention_key
              A__							=   tax_enc_dec_attention_out
              A__							=   tax_enc_dec_attention_query
              A__							=   tax_enc_dec_attention_value

              A__							=   tax_cross_layer_norm

              if split_mlp_wi:
                     A__							=   tax_mlp_wi_a
                     A__							=   tax_mlp_wi_a
              else:
                     A__							=   tax_mlp_wi

              A__							=   tax_mlp_wo

              A__							=   txa_mlp_layer_norm

              A__							=   flax_model_decoder_layer_block

       # Decoder Normalization
       A__							=   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       A__							=   txa_decoder_norm

       # Only for layer 0:
       A__							=   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       A__							=   tax_decoder_rel_embedding

       # Token Embeddings
       A__							=   tax_model['''target''']['''token_embedder''']['''embedding''']
       A__							=   txa_token_embeddings

       # LM Head (only in v1.1 and LongT5 checkpoints)
       if "logits_dense" in tax_model["target"]["decoder"]:
              A__							=   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

       flax_model.save_pretrained(lowercase_						)
       print('''T5X Model was sucessfully converted!'''						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
  )
  parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
  parser.add_argument(
      """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
  )
  _lowerCamelCase     :		Tuple								=       parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


