





"""simple docstring"""




import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
   import torch

   from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=None					,      snake_case_=None					,      snake_case_=None					,      snake_case_="resnet50"					,      snake_case_=3					,      snake_case_=3_2					,      snake_case_=3					,      snake_case_=True					,      snake_case_=True					,      ):

        """simple docstring"""

        A_  :       Tuple										=  parent
        A_  :       Any										=  out_indices if out_indices is not None else [4]
        A_  :       Union[str, Any]										=  stage_names
        A_  :       Any										=  out_features
        A_  :       Union[str, Any]										=  backbone
        A_  :       Dict										=  batch_size
        A_  :       Union[str, Any]										=  image_size
        A_  :       Union[str, Any]										=  num_channels
        A_  :       List[str]										=  use_pretrained_backbone
        A_  :       Tuple										=  is_training

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

        return config, pixel_values

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return TimmBackboneConfig(
            image_size=self.image_size					,      num_channels=self.num_channels					,      out_features=self.out_features					,      out_indices=self.out_indices					,      stage_names=self.stage_names					,      use_pretrained_backbone=self.use_pretrained_backbone					,      backbone=self.backbone					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  TimmBackbone(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        with torch.no_grad():
          A_  :       List[str]										=  model(snake_case_  )
        self.parent.assertEqual(
            result.feature_map[-1].shape					,      (self.batch_size, model.channels[-1], 1_4, 1_4)					,      )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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


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



      '''simple docstring'''
      lowercase_      : Optional[int]	    =   (TimmBackbone,) if is_torch_available() else ()
      lowercase_      : Dict	    =   {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
      lowercase_      : List[str]	    =   False
      lowercase_      : str	    =   False
      lowercase_      : str	    =   False
      lowercase_      : Tuple	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  TimmBackboneModelTester(self  )
        A_  :       List[Any]										=  ConfigTester(self					,      config_class=snake_case_					,      has_text_modality=snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[Any]										=  'resnet18'
        A_  :       Optional[int]										=  'microsoft/resnet-18'

        A_  :       str										=  AutoBackbone.from_pretrained(snake_case_					,      use_timm_backbone=snake_case_  )
        A_  :       Dict										=  AutoBackbone.from_pretrained(snake_case_  )

        self.assertEqual(len(timm_model.out_features  )					,      len(transformers_model.out_features  )  )
        self.assertEqual(len(timm_model.stage_names  )					,      len(transformers_model.stage_names  )  )
        self.assertEqual(timm_model.channels					,      transformers_model.channels  )
        # Out indices are set to the last layer by default. For timm models, we don't know
        # the number of layers in advance, so we set it to (-1,), whereas for transformers
        # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
        self.assertEqual(timm_model.out_indices					,      (-1,)  )
        self.assertEqual(transformers_model.out_indices					,      [len(timm_model.stage_names  ) - 1]  )

        A_  :       str										=  AutoBackbone.from_pretrained(snake_case_					,      use_timm_backbone=snake_case_					,      out_indices=[1, 2, 3]  )
        A_  :       List[Any]										=  AutoBackbone.from_pretrained(snake_case_					,      out_indices=[1, 2, 3]  )

        self.assertEqual(timm_model.out_indices					,      transformers_model.out_indices  )
        self.assertEqual(len(timm_model.out_features  )					,      len(transformers_model.out_features  )  )
        self.assertEqual(timm_model.channels					,      transformers_model.channels  )

      @unittest.skip('TimmBackbone doesn\'t support feed forward chunking'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone initialization is managed on the timm side'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('model weights aren\'t tied in TimmBackbone.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('model weights aren\'t tied in TimmBackbone.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('TimmBackbone doesn\'t support output_attentions.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip('Safetensors is not supported by timm.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

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

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Optional[int]										=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       List[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :       Union[str, Any]										=  True
        A_  :       Any										=  self.has_attentions

        # no need to test all models as different heads yield the same functionality
        A_  :       Dict										=  self.all_model_classes[0]
        A_  :       Tuple										=  model_class(snake_case_  )
        model.to(snake_case_  )

        A_  :       List[Any]										=  self._prepare_for_class(snake_case_					,      snake_case_  )
        A_  :       List[Any]										=  model(**snake_case_  )
        A_  :       Union[str, Any]										=  outputs[0][-1]

        # Encoder-/Decoder-only models
        A_  :       Any										=  outputs.hidden_states[0]
        hidden_states.retain_grad()

        if self.has_attentions:
          A_  :       Tuple										=  outputs.attentions[0]
          attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=snake_case_  )

        self.assertIsNotNone(hidden_states.grad  )

        if self.has_attentions:
          self.assertIsNotNone(attentions.grad  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

        for model_class in self.all_model_classes:
          A_  :       Optional[int]										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          A_  :       Tuple										=  model(**snake_case_  )

          self.assertEqual(len(result.feature_maps  )					,      len(config.out_indices  )  )
          self.assertEqual(len(model.channels  )					,      len(config.out_indices  )  )

          # Check output of last stage is taken if out_features=None, out_indices=None
          A_  :       Dict										=  copy.deepcopy(snake_case_  )
          A_  :       List[Any]										=  None
          A_  :       Union[str, Any]										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          A_  :       Tuple										=  model(**snake_case_  )

          self.assertEqual(len(result.feature_maps  )					,      1  )
          self.assertEqual(len(model.channels  )					,      1  )

          # Check backbone can be initialized with fresh weights
          A_  :       Optional[int]										=  copy.deepcopy(snake_case_  )
          A_  :       List[Any]										=  False
          A_  :       int										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          A_  :       Tuple										=  model(**snake_case_  )






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        super().__init__(*snake_case_					,      **snake_case_  )
        A_  :       Tuple										=  {}

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

        """simple docstring"""

        A_  :       str										=  super().add_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
        if num_added_tokens == 0:
          raise ValueError(
              F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
              ' `placeholder_token` that is not already in the tokenizer.'  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=1					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Tuple										=  []
        if num_vec_per_token == 1:
          self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
          output.append(snake_case_  )
        else:
          A_  :       Tuple										=  []
          for i in range(snake_case_  ):
            A_  :       List[str]										=  placeholder_token + F"""_{i}"""
            self.try_adding_tokens(snake_case_					,      *snake_case_					,      **snake_case_  )
            output.append(snake_case_  )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
        for token in self.token_map:
          if token in placeholder_token:
            raise ValueError(
                F"""The tokenizer already has placeholder token {token} that can get confused with"""
                F""" {placeholder_token}keep placeholder tokens independent"""  )
        A_  :       Any										=  output

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=False					,      snake_case_=1.0  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       Optional[Any]										=  []
          for i in range(len(snake_case_  )  ):
            output.append(self.replace_placeholder_tokens_in_text(text[i]					,      vector_shuffle=snake_case_  )  )
          return output
        for placeholder_token in self.token_map:
          if placeholder_token in text:
            A_  :       List[Any]										=  self.token_map[placeholder_token]
            A_  :       Optional[int]										=  tokens[: 1 + int(len(snake_case_  ) * prop_tokens_to_load  )]
            if vector_shuffle:
              A_  :       Optional[Any]										=  copy.copy(snake_case_  )
              random.shuffle(snake_case_  )
            A_  :       List[str]										=  text.replace(snake_case_					,      ' '.join(snake_case_  )  )
        return text

      def __call__(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().__call__(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      *snake_case_					,      snake_case_=False					,      snake_case_=1.0					,      **snake_case_  ):

        """simple docstring"""

        return super().encode(
            self.replace_placeholder_tokens_in_text(
                snake_case_					,      vector_shuffle=snake_case_					,      prop_tokens_to_load=snake_case_  )					,      *snake_case_					,      **snake_case_					,      )