






import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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 transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       EfficientFormerForImageClassification,
       EfficientFormerForImageClassificationWithTeacher,
       EfficientFormerModel,
   )
   from transformers.models.efficientformer.modeling_efficientformer import (
       EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )


if is_vision_available():
   from PIL import Image

   from transformers import EfficientFormerImageProcessor


class        SCREAMING_SNAKE_CASE	:
     '''simple docstring'''


     def __init__( self				:		Any    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		int = 13    ,       UpperCAmelCase_				:		int = 64    ,       UpperCAmelCase_				:		int = 2    ,       UpperCAmelCase_				:		int = 3    ,       UpperCAmelCase_				:		int = 3    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		bool = True    ,       UpperCAmelCase_				:		int = 128    ,       UpperCAmelCase_				:		Union[str, Any]=[16, 32, 64, 128]    ,       UpperCAmelCase_				:		int = 7    ,       UpperCAmelCase_				:		int = 4    ,       UpperCAmelCase_				:		int = 37    ,       UpperCAmelCase_				:		str = "gelu"    ,       UpperCAmelCase_				:		float = 0.1    ,       UpperCAmelCase_				:		float = 0.1    ,       UpperCAmelCase_				:		int = 10    ,       UpperCAmelCase_				:		float = 0.02    ,       UpperCAmelCase_				:		int = 2    ,       UpperCAmelCase_				:		int = 1    ,       UpperCAmelCase_				:		int = 128    ,       UpperCAmelCase_				:		List[int] = [2, 2, 2, 2]    ,       UpperCAmelCase_				:		int = 2    ,       UpperCAmelCase_				:		int = 2    ,       ):
       SCREAMING_SNAKE_CASE				:					int		  =			parent
       SCREAMING_SNAKE_CASE				:					List[str]		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			patch_size
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					str		  =			is_training
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			use_labels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			hidden_size
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			num_hidden_layers
       SCREAMING_SNAKE_CASE				:					int		  =			num_attention_heads
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			intermediate_size
       SCREAMING_SNAKE_CASE				:					Dict		  =			hidden_act
       SCREAMING_SNAKE_CASE				:					Tuple		  =			hidden_dropout_prob
       SCREAMING_SNAKE_CASE				:					List[str]		  =			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE				:					List[str]		  =			type_sequence_label_size
       SCREAMING_SNAKE_CASE				:					int		  =			initializer_range
       SCREAMING_SNAKE_CASE				:					Dict		  =			encoder_stride
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			num_attention_outputs
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			embed_dim
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			embed_dim + 1
       SCREAMING_SNAKE_CASE				:					int		  =			resolution
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			depths
       SCREAMING_SNAKE_CASE				:					Dict		  =			hidden_sizes
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			dim
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			mlp_expansion_ratio


     def  _A				( self				:		Dict					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			None
       if self.use_labels:
         SCREAMING_SNAKE_CASE				:					str		  =			ids_tensor([self.batch_size]    ,       self.type_sequence_label_size					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			self.get_config()
       return config, pixel_values, labels


     def  _A				( self				:		Optional[int]					):
       return EfficientFormerConfig(
           image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       intermediate_size=self.intermediate_size    ,       hidden_act=self.hidden_act    ,       hidden_dropout_prob=self.hidden_dropout_prob    ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,       is_decoder=UpperCAmelCase_    ,       initializer_range=self.initializer_range    ,       encoder_stride=self.encoder_stride    ,       resolution=self.resolution    ,       depths=self.depths    ,       hidden_sizes=self.hidden_sizes    ,       dim=self.dim    ,       mlp_expansion_ratio=self.mlp_expansion_ratio    ,       )


     def  _A				( self				:		Union[str, Any]    ,       UpperCAmelCase_				:		Dict    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			EfficientFormerModel(config=UpperCAmelCase_					)
       model.to(UpperCAmelCase_					)
       model.eval()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			model(UpperCAmelCase_					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)					)


     def  _A				( self				:		Dict    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		str					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			self.type_sequence_label_size
       SCREAMING_SNAKE_CASE				:					str		  =			EfficientFormerForImageClassification(UpperCAmelCase_					)
       model.to(UpperCAmelCase_					)
       model.eval()
       SCREAMING_SNAKE_CASE				:					int		  =			model(UpperCAmelCase_    ,       labels=UpperCAmelCase_					)
       self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)					)

       # test greyscale images
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			1
       SCREAMING_SNAKE_CASE				:					int		  =			EfficientFormerForImageClassification(UpperCAmelCase_					)
       model.to(UpperCAmelCase_					)
       model.eval()

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			model(UpperCAmelCase_					)
       self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.type_sequence_label_size)					)






     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )  ,     (
           SCREAMING_SNAKE_CASE
       )  ,     (
           SCREAMING_SNAKE_CASE
       )  ,     
       )				:					Optional[Any]		  =			config_and_inputs
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		(
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_							:						List[Any]								=		(
         {
             '''feature-extraction''': EfficientFormerModel,
             '''image-classification''': (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     UpperCamelCase_							:						Optional[int]								=		False

     UpperCamelCase_							:						Optional[int]								=		False
     UpperCamelCase_							:						Union[str, Any]								=		False
     UpperCamelCase_							:						Optional[int]								=		False


     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					Dict		  =			EfficientFormerModelTester(self					)
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			ConfigTester(
           self    ,       config_class=UpperCAmelCase_    ,       has_text_modality=UpperCAmelCase_    ,       hidden_size=37					)


     def  _A				( self				:		Any					):
       self.config_tester.run_common_tests()


     @unittest.skip(reason="EfficientFormer does not use inputs_embeds"					)
     def  _A				( self				:		int					):
       pass


     @unittest.skip(reason="EfficientFormer does not support input and output embeddings"					)
     def  _A				( self				:		List[Any]					):
       pass


     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE				:					List[Any]		  =			["pixel_values"]
         self.assertListEqual(arg_names[:1]    ,       UpperCAmelCase_					)


     def  _A				( self				:		Optional[Any]					):
       def check_hidden_states_output(UpperCAmelCase_				:		int    ,       UpperCAmelCase_				:		str    ,       UpperCAmelCase_				:		int					):
         SCREAMING_SNAKE_CASE				:					List[str]		  =			model_class(UpperCAmelCase_					)
         model.to(UpperCAmelCase_					)
         model.eval()

         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:					List[Any]		  =			model(**self._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_					)					)

         SCREAMING_SNAKE_CASE				:					str		  =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         SCREAMING_SNAKE_CASE				:					List[str]		  =			getattr(
             self.model_tester    ,       "expected_num_hidden_layers"    ,       self.model_tester.num_hidden_layers + 1					)
         self.assertEqual(len(UpperCAmelCase_					)    ,       UpperCAmelCase_					)

         if hasattr(self.model_tester    ,       "encoder_seq_length"					):
           SCREAMING_SNAKE_CASE				:					Any		  =			self.model_tester.encoder_seq_length
           if hasattr(self.model_tester    ,       "chunk_length"					) and self.model_tester.chunk_length > 1:
             SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			seq_length * self.model_tester.chunk_length
         else:
           SCREAMING_SNAKE_CASE				:					Dict		  =			self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[-1].shape[-2:]					)    ,       [seq_length, self.model_tester.hidden_size]    ,       )

         if config.is_encoder_decoder:
           SCREAMING_SNAKE_CASE				:					str		  =			outputs.decoder_hidden_states

           self.assertIsInstance(UpperCAmelCase_    ,       (list, tuple)					)
           self.assertEqual(len(UpperCAmelCase_					)    ,       UpperCAmelCase_					)
           SCREAMING_SNAKE_CASE				:					Any		  =			getattr(self.model_tester    ,       "seq_length"    ,       UpperCAmelCase_					)
           SCREAMING_SNAKE_CASE				:					str		  =			getattr(self.model_tester    ,       "decoder_seq_length"    ,       UpperCAmelCase_					)

           self.assertListEqual(
               list(hidden_states[-1].shape[-2:]					)    ,       [decoder_seq_length, self.model_tester.hidden_size]    ,       )

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

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			True
         check_hidden_states_output(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)

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

         check_hidden_states_output(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)


     def  _A				( self				:		str    ,       UpperCAmelCase_				:		Any    ,       UpperCAmelCase_				:		List[Any]    ,       UpperCAmelCase_				:		str=False					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			super()._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_    ,       return_labels=UpperCAmelCase_					)

       if return_labels:
         if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
           del inputs_dict["labels"]

       return inputs_dict


     def  _A				( self				:		Any					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_					)


     @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"					)
     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_					)


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					int		  =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)


     def  _A				( self				:		List[str]					):
       if not self.model_tester.is_training:
         return

       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Tuple		  =			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			True

       for model_class in self.all_model_classes:
         # EfficientFormerForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(UpperCAmelCase_					)
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
           continue
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			model_class(UpperCAmelCase_					)
         model.to(UpperCAmelCase_					)
         model.train()
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_    ,       return_labels=UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Any		  =			model(**UpperCAmelCase_					).loss
         loss.backward()


     def  _A				( self				:		Any					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			[
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
         if (
             model_class
             not in [
                 *get_values(UpperCAmelCase_					),
             ]
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
           continue

         for problem_type in problem_types:
           with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''					):
             SCREAMING_SNAKE_CASE				:					str		  =			problem_type["title"]
             SCREAMING_SNAKE_CASE				:					int		  =			problem_type["num_labels"]

             SCREAMING_SNAKE_CASE				:					Any		  =			model_class(UpperCAmelCase_					)
             model.to(UpperCAmelCase_					)
             model.train()

             SCREAMING_SNAKE_CASE				:					List[Any]		  =			self._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_    ,       return_labels=UpperCAmelCase_					)

             if problem_type["num_labels"] > 1:
               SCREAMING_SNAKE_CASE				:					Dict		  =			inputs["labels"].unsqueeze(1					).repeat(1    ,       problem_type["num_labels"]					)

             SCREAMING_SNAKE_CASE				:					Tuple		  =			inputs["labels"].to(problem_type["dtype"]					)

             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
             # they have the same size." which is a symptom something in wrong for the regression problem.
             # See https://github.com/huggingface/transformers/issues/11780
             with warnings.catch_warnings(record=UpperCAmelCase_					) as warning_list:
               SCREAMING_SNAKE_CASE				:					Dict		  =			model(**UpperCAmelCase_					).loss
             for w in warning_list:
               if "Using a target size that is different to the input size" in str(w.message					):
                 raise ValueError(
                     f'''Something is going wrong in the regression problem: intercepted {w.message}'''					)

             loss.backward()


     @slow
     def  _A				( self				:		int					):
       for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			EfficientFormerModel.from_pretrained(UpperCAmelCase_					)
         self.assertIsNotNone(UpperCAmelCase_					)






     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			True

       SCREAMING_SNAKE_CASE				:					Tuple		  =			getattr(self.model_tester    ,       "seq_length"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			getattr(self.model_tester    ,       "encoder_seq_length"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					int		  =			getattr(self.model_tester    ,       "key_length"    ,       UpperCAmelCase_					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			getattr(self.model_tester    ,       "chunk_length"    ,       UpperCAmelCase_					)

       if chunk_length is not None and hasattr(self.model_tester    ,       "num_hashes"					):
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			encoder_seq_length * self.model_tester.num_hashes

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			True
         SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			False
         SCREAMING_SNAKE_CASE				:					int		  =			True
         SCREAMING_SNAKE_CASE				:					List[str]		  =			model_class(UpperCAmelCase_					)
         model.to(UpperCAmelCase_					)
         model.eval()
         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:					Any		  =			model(**self._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_					)					)
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(UpperCAmelCase_					)    ,       self.model_tester.num_attention_outputs					)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         SCREAMING_SNAKE_CASE				:					List[str]		  =			True
         SCREAMING_SNAKE_CASE				:					Tuple		  =			model_class(UpperCAmelCase_					)
         model.to(UpperCAmelCase_					)
         model.eval()
         with torch.no_grad():
           SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			model(**self._prepare_for_class(UpperCAmelCase_    ,       UpperCAmelCase_					)					)
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(UpperCAmelCase_					)    ,       self.model_tester.num_attention_outputs					)

         if chunk_length is not None:
           self.assertListEqual(
               list(attentions[0].shape[-4:]					)    ,       [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]    ,       )
         else:
           self.assertListEqual(
               list(attentions[0].shape[-3:]					)    ,       [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,       )





def      lowerCamelCase__	(   ):

  """simple docstring"""






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


@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     @cached_property
     def  _A				( self				:		Dict					):
       return (
           EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"					)
           if is_vision_available()
           else None
       )


     @slow
     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"					).to(
           UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.default_image_processor
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_img()
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processor(images=UpperCAmelCase_    ,       return_tensors="pt"					).to(UpperCAmelCase_					)

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE				:					int		  =			model(**UpperCAmelCase_					)

       # verify the logits
       SCREAMING_SNAKE_CASE				:					Dict		  =			(1, 1000)
       self.assertEqual(outputs.logits.shape    ,       UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Dict		  =			torch.tensor([-0.0_555, 0.4_825, -0.0_852]					).to(UpperCAmelCase_					)
       self.assertTrue(torch.allclose(outputs.logits[0][:3]    ,       UpperCAmelCase_    ,       atol=1E-4					)					)






     @slow
     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Tuple		  =			EfficientFormerForImageClassificationWithTeacher.from_pretrained(
           "snap-research/efficientformer-l1-300"					).to(UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.default_image_processor
       SCREAMING_SNAKE_CASE				:					Dict		  =			prepare_img()
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			image_processor(images=UpperCAmelCase_    ,       return_tensors="pt"					).to(UpperCAmelCase_					)

       # forward pass
       with torch.no_grad():
         SCREAMING_SNAKE_CASE				:					List[Any]		  =			model(**UpperCAmelCase_					)

       # verify the logits
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			(1, 1000)
       self.assertEqual(outputs.logits.shape    ,       UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Any		  =			torch.tensor([-0.1_312, 0.4_353, -1.0_499]					).to(UpperCAmelCase_					)
       self.assertTrue(torch.allclose(outputs.logits[0][:3]    ,       UpperCAmelCase_    ,       atol=1E-4					)					)














import math
from collections.abc import Generator


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1) ))
  for i in (n for n in numbers if n > 1):
    # only need to check for factors up to sqrt(i)
    SCREAMING_SNAKE_CASE				:					str		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(2  ,   lowercase ):
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Generator		  =			(i for i in range(1  ,   (max_n + 1)  ,   2 ))
  # It's useless to test even numbers as they will not be prime
  if max_n > 2:
    yield 2  # Because 2 will not be tested, it's necessary to yield it now
  for i in (n for n in numbers if n > 1):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			int(math.sqrt(lowercase ) ) + 1
    for j in range(3  ,   lowercase  ,   2 ):
      # As we removed the even numbers, we don't need them now
      if (i % j) == 0:
        break
    else:
      yield i


def      lowerCamelCase__	(   ):

  """simple docstring"""






  from timeit import timeit

  SCREAMING_SNAKE_CASE				:					Tuple		  =			"from __main__ import slow_primes, primes, fast_primes"
  print(timeit("slow_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )
  print(timeit("fast_primes(1_000_000_000_000)"  ,   setup=lowercase  ,   number=1000000 ) )


if __name__ == "__main__":
   snake_case			 =						int(input("""Calculate primes up to:\n>> """).strip())
   for ret in primes(number):
      print(ret)
   benchmark()






