





"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    self.config_class(**self.inputs_dict    )
        self.parent.assertTrue(hasattr(snake_case_   , 'hidden_sizes'    )    )
        self.parent.assertTrue(hasattr(snake_case_   , 'num_encoder_blocks'    )    )


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=1_3   , snake_case_=6_4   , snake_case_=3   , snake_case_=4   , snake_case_=[2, 2, 2, 2]   , snake_case_=[8, 4, 2, 1]   , snake_case_=[1_6, 3_2, 6_4, 1_2_8]   , snake_case_=[1, 4, 8, 1_6]   , snake_case_=False   , snake_case_=True   , snake_case_="gelu"   , snake_case_=0.1   , snake_case_=0.02   , snake_case_=3   , snake_case_=None   , ):

        """simple docstring"""

        A_    :			Any									=    parent
        A_    :			Optional[Any]									=    batch_size
        A_    :			List[str]									=    image_size
        A_    :			Optional[int]									=    num_channels
        A_    :			Tuple									=    num_encoder_blocks
        A_    :			Tuple									=    sr_ratios
        A_    :			Optional[int]									=    depths
        A_    :			Dict									=    hidden_sizes
        A_    :			str									=    downsampling_rates
        A_    :			Dict									=    is_training
        A_    :			Union[str, Any]									=    use_labels
        A_    :			List[str]									=    hidden_act
        A_    :			Any									=    hidden_dropout_prob
        A_    :			int									=    initializer_range
        A_    :			List[str]									=    num_labels
        A_    :			str									=    scope

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        A_    :			Optional[int]									=    None
        if self.use_labels:
          A_    :			Tuple									=    ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels    )

        A_    :			Union[str, Any]									=    PoolFormerConfig(
            image_size=self.image_size   , num_channels=self.num_channels   , num_encoder_blocks=self.num_encoder_blocks   , depths=self.depths   , hidden_sizes=self.hidden_sizes   , hidden_act=self.hidden_act   , hidden_dropout_prob=self.hidden_dropout_prob   , initializer_range=self.initializer_range   , )

        return config, pixel_values, labels

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    PoolFormerModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()
        A_    :			Dict									=    model(snake_case_    )
        A_    :			Union[str, Any]									=    self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.prepare_config_and_inputs()
        A_		,			A_		,			A_    :			Optional[int]									=    config_and_inputs
        A_    :			Any									=    {'pixel_values': pixel_values}
        return config, inputs_dict


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



      '''simple docstring'''
      lowercase_ :					int             =  (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
      lowercase_ :					Any             =  (
          {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      lowercase_ :					Union[str, Any]             =  False
      lowercase_ :					Optional[Any]             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    PoolFormerModelTester(self    )
        A_    :			Optional[Any]									=    PoolFormerConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_    )

      @unittest.skip('PoolFormer does not use inputs_embeds'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        def check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    ):
          A_    :			Dict									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.eval()

          with torch.no_grad():
            A_    :			str									=    model(**self._prepare_for_class(snake_case_   , snake_case_    )    )

          A_    :			List[Any]									=    outputs.hidden_states

          A_    :			str									=    self.model_tester.num_encoder_blocks
          self.assertEqual(len(snake_case_    )   , snake_case_    )

          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-3:]    )   , [
                  self.model_tester.hidden_sizes[0],
                  self.model_tester.image_size // 4,
                  self.model_tester.image_size // 4,
              ]   , )

        A_		,			A_    :			Tuple									=    self.model_tester.prepare_config_and_inputs_for_common()

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

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_    :			Optional[int]									=    True

          check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.model_tester.is_training:
          return

        A_		,			A_    :			str									=    self.model_tester.prepare_config_and_inputs_for_common()
        A_    :			str									=    True

        for model_class in self.all_model_classes:
          if model_class in get_values(snake_case_    ):
            continue
          A_    :			List[str]									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.train()
          A_    :			Any									=    self._prepare_for_class(snake_case_   , snake_case_   , return_labels=snake_case_    )
          A_    :			List[str]									=    model(**snake_case_    ).loss
          loss.backward()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

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

        for model_class in self.all_model_classes:
          A_    :			int									=    model_class(snake_case_    )
          A_    :			Any									=    inspect.signature(model.forward    )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          A_    :			Optional[int]									=    [*signature.parameters.keys()]

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

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_    :			Any									=    PoolFormerModel.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





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





@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    PoolFormerImageProcessor()
        A_    :			List[Any]									=    PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'    ).to(snake_case_    )

        A_    :			List[str]									=    image_processor(images=prepare_img()   , return_tensors='pt'    ).to(snake_case_    )

        # forward pass
        with torch.no_grad():
          A_    :			Any									=    model(**snake_case_    )

        # verify the logits
        A_    :			Union[str, Any]									=    torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape   , snake_case_    )

        A_    :			List[Any]									=    torch.tensor([-0.61_13, 0.16_85, -0.04_92]    ).to(snake_case_    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]   , snake_case_   , atol=1E-4    )    )
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

      # Load weights from tf checkpoint
      load_tf_weights_in_canine(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)