




'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFResNetForImageClassification, TFResNetModel
   from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]   ,_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE="relu"   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=None   ,)				->     Optional[Any]:
          _snake_case				= parent
          _snake_case				= batch_size
          _snake_case				= image_size
          _snake_case				= num_channels
          _snake_case				= embeddings_size
          _snake_case				= hidden_sizes
          _snake_case				= depths
          _snake_case				= is_training
          _snake_case				= use_labels
          _snake_case				= hidden_act
          _snake_case				= num_labels
          _snake_case				= scope
          _snake_case				= len(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     List[str]:
          _snake_case				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

          _snake_case				= None
          if self.use_labels:
               _snake_case				= ids_tensor([self.batch_size]   ,self.num_labels       )

          _snake_case				= self.get_config()

          return config, pixel_values, labels






     def 							_lowercase      (					self       )				->     Dict:
          return ResNetConfig(
              num_channels=self.num_channels   ,embeddings_size=self.embeddings_size   ,hidden_sizes=self.hidden_sizes   ,depths=self.depths   ,hidden_act=self.hidden_act   ,num_labels=self.num_labels   ,image_size=self.image_size   ,)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Dict:
          _snake_case				= TFResNetModel(config=_SCREAMING_SNAKE_CASE       )
          _snake_case				= model(_SCREAMING_SNAKE_CASE       )
          # expected last hidden states: B, C, H // 32, W // 32
          self.parent.assertEqual(
              result.last_hidden_state.shape   ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Optional[Any]:
          _snake_case				= self.num_labels
          _snake_case				= TFResNetForImageClassification(_SCREAMING_SNAKE_CASE       )
          _snake_case				= model(_SCREAMING_SNAKE_CASE   ,labels=_SCREAMING_SNAKE_CASE       )
          self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)       )






     def 							_lowercase      (					self       )				->     List[str]:
          _snake_case				= self.prepare_config_and_inputs()
          _snake_case				,   _snake_case				,   _snake_case				= config_and_inputs
          _snake_case				= {"pixel_values": pixel_values}
          return config, inputs_dict







@require_tf
class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       Any       							=		(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     SCREAMING_SNAKE_CASE_				:       List[str]       							=		(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_				:       Dict       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		False
     SCREAMING_SNAKE_CASE_				:       str       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		False






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= TFResNetModelTester(self       )
          _snake_case				= ConfigTester(self   ,config_class=_SCREAMING_SNAKE_CASE   ,has_text_modality=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          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 							_lowercase      (					self       )				->     List[str]:
          return






     @unittest.skip(reason="ResNet does not use inputs_embeds"       )
     def 							_lowercase      (					self       )				->     Dict:
          pass






     @unittest.skip(reason="ResNet does not support input and output embeddings"       )
     def 							_lowercase      (					self       )				->     Tuple:
          pass






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case				= model_class(_SCREAMING_SNAKE_CASE       )
               _snake_case				= inspect.signature(model.call       )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _snake_case				= [*signature.parameters.keys()]

               _snake_case				= ["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Optional[int]:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          def check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= model_class(_SCREAMING_SNAKE_CASE       )
               _snake_case				= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )

               _snake_case				= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _snake_case				= self.model_tester.num_stages
               self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,expected_num_stages + 1       )

               # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]       )   ,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,)

          _snake_case				,   _snake_case				= self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case				= ["basic", "bottleneck"]
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    _snake_case				= layer_type
                    _snake_case				= True
                    check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

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

                    check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE       )






     @slow
     def 							_lowercase      (					self       )				->     Any:
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case				= TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE       )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )







def       __a      (  )		->					Any:

     """simple docstring"""




     _snake_case				= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
     return image





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






     @cached_property
     def 							_lowercase      (					self       )				->     str:
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
              if is_vision_available()
              else None
          )






     @slow
     def 							_lowercase      (					self       )				->     List[str]:
          _snake_case				= TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

          _snake_case				= self.default_image_processor
          _snake_case				= prepare_img()
          _snake_case				= image_processor(images=_SCREAMING_SNAKE_CASE   ,return_tensors="tf"       )

          # forward pass
          _snake_case				= model(**_SCREAMING_SNAKE_CASE       )

          # verify the logits
          _snake_case				= tf.TensorShape((1, 1_000)       )
          self.assertEqual(outputs.logits.shape   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]       )

          self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()   ,_SCREAMING_SNAKE_CASE   ,atol=1e-4       )       )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_		:     Dict          =		{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     str          =		[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_		:     Optional[int]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


