


'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
      TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSwinForImageClassification,
      TFSwinForMaskedImageModeling,
      TFSwinModel,
  )


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class      lowerCamelCase	:

       def __init__(			self	,						a__	,						a__=13	,						a__=32	,						a__=2	,						a__=3	,						a__=16	,						a__=[1, 2, 1]	,						a__=[2, 2, 4]	,						a__=2	,						a__=2.0	,						a__=True	,						a__=0.0	,						a__=0.0	,						a__=0.1	,						a__="gelu"	,						a__=False	,						a__=True	,						a__=0.02	,						a__=1e-5	,						a__=True	,						a__=None	,						a__=True	,						a__=10	,						a__=8	,						):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      parent
        snake_case   : Optional[Any]	  =      batch_size
        snake_case   : Optional[int]	  =      image_size
        snake_case   : Tuple	  =      patch_size
        snake_case   : List[Any]	  =      num_channels
        snake_case   : int	  =      embed_dim
        snake_case   : Dict	  =      depths
        snake_case   : Tuple	  =      num_heads
        snake_case   : str	  =      window_size
        snake_case   : Union[str, Any]	  =      mlp_ratio
        snake_case   : Any	  =      qkv_bias
        snake_case   : List[Any]	  =      hidden_dropout_prob
        snake_case   : str	  =      attention_probs_dropout_prob
        snake_case   : Optional[int]	  =      drop_path_rate
        snake_case   : List[str]	  =      hidden_act
        snake_case   : Tuple	  =      use_absolute_embeddings
        snake_case   : Optional[Any]	  =      patch_norm
        snake_case   : Any	  =      layer_norm_eps
        snake_case   : str	  =      initializer_range
        snake_case   : Union[str, Any]	  =      is_training
        snake_case   : Union[str, Any]	  =      scope
        snake_case   : int	  =      use_labels
        snake_case   : List[Any]	  =      type_sequence_label_size
        snake_case   : int	  =      encoder_stride

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

        snake_case   : Optional[int]	  =      self.get_config()

        return config, pixel_values, labels

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return SwinConfig(
            image_size=self.image_size	,						patch_size=self.patch_size	,						num_channels=self.num_channels	,						embed_dim=self.embed_dim	,						depths=self.depths	,						num_heads=self.num_heads	,						window_size=self.window_size	,						mlp_ratio=self.mlp_ratio	,						qkv_bias=self.qkv_bias	,						hidden_dropout_prob=self.hidden_dropout_prob	,						attention_probs_dropout_prob=self.attention_probs_dropout_prob	,						drop_path_rate=self.drop_path_rate	,						hidden_act=self.hidden_act	,						use_absolute_embeddings=self.use_absolute_embeddings	,						path_norm=self.patch_norm	,						layer_norm_eps=self.layer_norm_eps	,						initializer_range=self.initializer_range	,						encoder_stride=self.encoder_stride	,						)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : Dict	  =      TFSwinModel(config=a__					)
        snake_case   : Tuple	  =      model(a__					)

        snake_case   : Tuple	  =      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
        snake_case   : Any	  =      int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

        self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, expected_seq_len, expected_dim)					)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : Union[str, Any]	  =      TFSwinForMaskedImageModeling(config=a__					)
        snake_case   : Union[str, Any]	  =      model(a__					)
        self.parent.assertEqual(
            result.logits.shape	,						(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

        # test greyscale images
        snake_case   : Any	  =      1
        snake_case   : Union[str, Any]	  =      TFSwinForMaskedImageModeling(a__					)

        snake_case   : Union[str, Any]	  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
        snake_case   : str	  =      model(a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, 1, self.image_size, self.image_size)					)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : List[str]	  =      self.type_sequence_label_size
        snake_case   : Dict	  =      TFSwinForImageClassification(a__					)
        snake_case   : Dict	  =      model(a__	,						labels=a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size)					)

        # test greyscale images
        snake_case   : Optional[int]	  =      1
        snake_case   : Union[str, Any]	  =      TFSwinForImageClassification(a__					)
        snake_case   : Optional[int]	  =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
        snake_case   : Optional[int]	  =      model(a__					)
        self.parent.assertEqual(result.logits.shape	,						(self.batch_size, self.type_sequence_label_size)					)



       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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







@require_tf
class      lowerCamelCase	(  SCREAMING_SNAKE_CASE,			SCREAMING_SNAKE_CASE,			unittest.TestCase     ):
       _UpperCamelCase    		=      (
           (
               TFSwinModel,
               TFSwinForImageClassification,
               TFSwinForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       _UpperCamelCase    		=      (
           {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
           if is_tf_available()
           else {}
       )

       _UpperCamelCase    		=      False
       _UpperCamelCase    		=      False
       _UpperCamelCase    		=      False
       _UpperCamelCase    		=      False

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[str]	  =      TFSwinModelTester(self					)
        snake_case   : int	  =      ConfigTester(self	,						config_class=a__	,						embed_dim=37					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[int]	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : List[Any]	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Any	  =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*a__					)

       @unittest.skip(reason='''Swin does not use inputs_embeds'''					)
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        pass

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : str	  =      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         snake_case   : Union[str, Any]	  =      model_class(a__					)
         self.assertIsInstance(model.get_input_embeddings()	,						tf.keras.layers.Layer					)
         snake_case   : Any	  =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__	,						tf.keras.layers.Dense					)					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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

        for model_class in self.all_model_classes:
         snake_case   : Union[str, Any]	  =      model_class(a__					)
         snake_case   : Tuple	  =      inspect.signature(model.call					)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case   : Any	  =      [*signature.parameters.keys()]

         snake_case   : Optional[Any]	  =      ['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Optional[Any]	  =      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case   : int	  =      True

        for model_class in self.all_model_classes:
         snake_case   : int	  =      True
         snake_case   : Optional[Any]	  =      False
         snake_case   : int	  =      True
         snake_case   : Dict	  =      model_class(a__					)
         snake_case   : Optional[Any]	  =      model(**self._prepare_for_class(a__	,						a__					)					)
         snake_case   : Any	  =      outputs.attentions
         snake_case   : Any	  =      len(self.model_tester.depths					)
         self.assertEqual(len(a__					)	,						a__					)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         snake_case   : str	  =      True
         snake_case   : Optional[int]	  =      config.window_size**2
         snake_case   : Union[str, Any]	  =      model_class(a__					)
         snake_case   : Union[str, Any]	  =      model(**self._prepare_for_class(a__	,						a__					)					)
         snake_case   : Any	  =      outputs.attentions
         self.assertEqual(len(a__					)	,						a__					)

         self.assertListEqual(
             list(attentions[0].shape[-3:]					)	,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,						)
         snake_case   : List[Any]	  =      len(a__					)

         # Check attention is always last and order is fine
         snake_case   : List[Any]	  =      True
         snake_case   : Dict	  =      True
         snake_case   : List[Any]	  =      model_class(a__					)
         snake_case   : str	  =      model(**self._prepare_for_class(a__	,						a__					)					)

         if hasattr(self.model_tester	,						'''num_hidden_states_types'''					):
          snake_case   : int	  =      self.model_tester.num_hidden_states_types
         else:
          # also another +1 for reshaped_hidden_states
          snake_case   : Tuple	  =      2
         self.assertEqual(out_len + added_hidden_states	,						len(a__					)					)

         snake_case   : int	  =      outputs.attentions

         self.assertEqual(len(a__					)	,						a__					)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]					)	,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]	,						)

       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__	,						a__					):






        '''simple docstring'''






        snake_case   : List[Any]	  =      model_class(a__					)
        snake_case   : Optional[int]	  =      model(**self._prepare_for_class(a__	,						a__					)					)
        snake_case   : Dict	  =      outputs.hidden_states

        snake_case   : List[str]	  =      getattr(
            self.model_tester	,						'''expected_num_hidden_layers'''	,						len(self.model_tester.depths					) + 1					)
        self.assertEqual(len(a__					)	,						a__					)

        # Swin has a different seq_length
        snake_case   : str	  =      to_atuple(config.patch_size					)

        snake_case   : int	  =      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]					)	,						[num_patches, self.model_tester.embed_dim]	,						)

        snake_case   : Union[str, Any]	  =      outputs.reshaped_hidden_states
        self.assertEqual(len(a__					)	,						a__					)

        snake_case   : str	  =      reshaped_hidden_states[0].shape

        snake_case   : int	  =      tf.reshape(reshaped_hidden_states[0]	,						(batch_size, num_channels, height * width)					)
        snake_case   : Union[str, Any]	  =      tf.transpose(a__	,						(0, 2, 1)					)

        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]					)	,						[num_patches, self.model_tester.embed_dim]	,						)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : int	  =      self.model_tester.prepare_config_and_inputs_for_common()

        snake_case   : Tuple	  =      to_atuple(self.model_tester.image_size					)

        for model_class in self.all_model_classes:
         snake_case   : int	  =      True
         self.check_hidden_states_output(a__	,						a__	,						a__	,						a__					)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case   : Union[str, Any]	  =      True

         self.check_hidden_states_output(a__	,						a__	,						a__	,						a__					)

       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Dict	  =      self.model_tester.prepare_config_and_inputs_for_common()
        snake_case   : List[Any]	  =      3

        snake_case   : int	  =      to_atuple(self.model_tester.image_size					)
        snake_case   : str	  =      to_atuple(config.patch_size					)

        snake_case   : Any	  =      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        snake_case   : Union[str, Any]	  =      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
         snake_case   : Optional[Any]	  =      True
         self.check_hidden_states_output(a__	,						a__	,						a__	,						(padded_height, padded_width)					)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case   : List[Any]	  =      True
         self.check_hidden_states_output(a__	,						a__	,						a__	,						(padded_height, padded_width)					)



       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case   : List[str]	  =      TFSwinModel.from_pretrained(a__					)
         self.assertIsNotNone(a__					)







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

       @cached_property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        return (
            AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					)
            if is_vision_available()
            else None
        )



       @slow
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






        snake_case   : Dict	  =      TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''					)
        snake_case   : str	  =      self.default_image_processor

        snake_case   : Any	  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
        snake_case   : Tuple	  =      image_processor(images=a__	,						return_tensors='''tf'''					)

        # forward pass
        snake_case   : Any	  =      model(a__					)

        # verify the logits
        snake_case   : List[Any]	  =      tf.TensorShape((1, 10_00)					)
        self.assertEqual(outputs.logits.shape	,						a__					)
        snake_case   : str	  =      tf.constant([-0.0948, -0.6454, -0.0921]					)
        self.assertTrue(np.allclose(outputs.logits[0, :3]	,						a__	,						atol=1e-4					)					)










import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def    _UpperCAmelCase	(   )		->str:


 '''simple docstring'''
 snake_case   : List[str]	  =      ArgumentParser(
     description=(
         '''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
     )       )

 # Optional arguments for the launch helper
 parser.add_argument('''--num_cores''' ,     type=__lowerCamelCase ,     default=1 ,     help='''Number of TPU cores to use (1 or 8).'''       )

 # positional
 parser.add_argument(
     '''training_script''' ,     type=__lowerCamelCase ,     help=(
         '''The full path to the single TPU training '''
         '''program/script to be launched in parallel, '''
         '''followed by all the arguments for the '''
         '''training script'''
     ) ,     )

 # rest from the training program
 parser.add_argument('''training_script_args''' ,     nargs=__lowerCamelCase       )

 return parser.parse_args()






def    _UpperCAmelCase	(   )		->Optional[int]:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      parse_args()

 # Import training_script as a module.
 snake_case   : str	  =      Path(args.training_script       )
 sys.path.append(str(script_fpath.parent.resolve()       )       )
 snake_case   : str	  =      script_fpath.stem
 snake_case   : Optional[int]	  =      importlib.import_module(__lowerCamelCase       )

 # Patch sys.argv
 snake_case   : int	  =      [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores       )]

 xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores       )


if __name__ == "__main__":
  main()






