


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    :


     def __init__(     self       ,				A_       ,				A_ = 13       ,				A_ = 64       ,				A_ = 2       ,				A_ = 3       ,				A_ = 3       ,				A_ = True       ,				A_ = True       ,				A_ = 1_28       ,				A_=[16, 32, 64, 1_28]       ,				A_ = 7       ,				A_ = 4       ,				A_ = 37       ,				A_ = "gelu"       ,				A_ = 0.1       ,				A_ = 0.1       ,				A_ = 10       ,				A_ = 0.0_2       ,				A_ = 2       ,				A_ = 1       ,				A_ = 1_28       ,				A_ = [2, 2, 2, 2]       ,				A_ = 2       ,				A_ = 2       ,				):
       _UpperCAmelCase		:       Any       =    parent
       _UpperCAmelCase		:       Dict       =    batch_size
       _UpperCAmelCase		:       List[Any]       =    image_size
       _UpperCAmelCase		:       Optional[Any]       =    patch_size
       _UpperCAmelCase		:       Dict       =    num_channels
       _UpperCAmelCase		:       Optional[int]       =    is_training
       _UpperCAmelCase		:       Dict       =    use_labels
       _UpperCAmelCase		:       Any       =    hidden_size
       _UpperCAmelCase		:       str       =    num_hidden_layers
       _UpperCAmelCase		:       Union[str, Any]       =    num_attention_heads
       _UpperCAmelCase		:       Optional[int]       =    intermediate_size
       _UpperCAmelCase		:       List[Any]       =    hidden_act
       _UpperCAmelCase		:       Optional[int]       =    hidden_dropout_prob
       _UpperCAmelCase		:       int       =    attention_probs_dropout_prob
       _UpperCAmelCase		:       Dict       =    type_sequence_label_size
       _UpperCAmelCase		:       Union[str, Any]       =    initializer_range
       _UpperCAmelCase		:       Any       =    encoder_stride
       _UpperCAmelCase		:       Tuple       =    num_attention_outputs
       _UpperCAmelCase		:       Optional[int]       =    embed_dim
       _UpperCAmelCase		:       Optional[int]       =    embed_dim + 1
       _UpperCAmelCase		:       List[Any]       =    resolution
       _UpperCAmelCase		:       Union[str, Any]       =    depths
       _UpperCAmelCase		:       Tuple       =    hidden_sizes
       _UpperCAmelCase		:       List[str]       =    dim
       _UpperCAmelCase		:       str       =    mlp_expansion_ratio


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Any       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       _UpperCAmelCase		:       Dict       =    None
       if self.use_labels:
         _UpperCAmelCase		:       Optional[int]       =    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size						)

       _UpperCAmelCase		:       Optional[Any]       =    self.get_config()
       return config, pixel_values, labels


     def 					__snake_case(     self						):
       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=A_       ,				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 					__snake_case(     self       ,				A_       ,				A_       ,				A_						):
       _UpperCAmelCase		:       List[str]       =    EfficientFormerModel(config=A_						)
       model.to(A_						)
       model.eval()
       _UpperCAmelCase		:       Optional[Any]       =    model(A_						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)						)


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_						):
       _UpperCAmelCase		:       Dict       =    self.type_sequence_label_size
       _UpperCAmelCase		:       int       =    EfficientFormerForImageClassification(A_						)
       model.to(A_						)
       model.eval()
       _UpperCAmelCase		:       Tuple       =    model(A_       ,				labels=A_						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       _UpperCAmelCase		:       List[str]       =    1
       _UpperCAmelCase		:       Optional[Any]       =    EfficientFormerForImageClassification(A_						)
       model.to(A_						)
       model.eval()

       _UpperCAmelCase		:       Any       =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       _UpperCAmelCase		:       str       =    model(A_						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)						)




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


@require_torch
class 		_SCREAMING_SNAKE_CASE    (	A       ,  A       ,  unittest.TestCase    ):
     __SCREAMING_SNAKE_CASE    				=						(
         (
             EfficientFormerModel,
             EfficientFormerForImageClassificationWithTeacher,
             EfficientFormerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     __SCREAMING_SNAKE_CASE    				=						(
         {
             '''feature-extraction''': EfficientFormerModel,
             '''image-classification''': (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_torch_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE    				=						False

     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False
     __SCREAMING_SNAKE_CASE    				=						False


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Optional[int]       =    EfficientFormerModelTester(self						)
       _UpperCAmelCase		:       Optional[Any]       =    ConfigTester(
           self       ,				config_class=A_       ,				has_text_modality=A_       ,				hidden_size=37						)


     def 					__snake_case(     self						):
       self.config_tester.run_common_tests()


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


     @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""						)
     def 					__snake_case(     self						):
       pass


     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    self.model_tester.prepare_config_and_inputs_for_common()

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

         _UpperCAmelCase		:       Union[str, Any]       =    ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]       ,				A_						)


     def 					__snake_case(     self						):
       def check_hidden_states_output(A_       ,				A_       ,				A_						):
         _UpperCAmelCase		:       Dict       =    model_class(A_						)
         model.to(A_						)
         model.eval()

         with torch.no_grad():
           _UpperCAmelCase		:       Optional[int]       =    model(**self._prepare_for_class(A_       ,				A_						)						)

         _UpperCAmelCase		:       Any       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _UpperCAmelCase		:       List[Any]       =    getattr(
             self.model_tester       ,				"""expected_num_hidden_layers"""       ,				self.model_tester.num_hidden_layers + 1						)
         self.assertEqual(len(A_						)       ,				A_						)

         if hasattr(self.model_tester       ,				"""encoder_seq_length"""						):
           _UpperCAmelCase		:       Any       =    self.model_tester.encoder_seq_length
           if hasattr(self.model_tester       ,				"""chunk_length"""						) and self.model_tester.chunk_length > 1:
             _UpperCAmelCase		:       List[str]       =    seq_length * self.model_tester.chunk_length
         else:
           _UpperCAmelCase		:       Any       =    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:
           _UpperCAmelCase		:       Optional[Any]       =    outputs.decoder_hidden_states

           self.assertIsInstance(A_       ,				(list, tuple)						)
           self.assertEqual(len(A_						)       ,				A_						)
           _UpperCAmelCase		:       int       =    getattr(self.model_tester       ,				"""seq_length"""       ,				A_						)
           _UpperCAmelCase		:       int       =    getattr(self.model_tester       ,				"""decoder_seq_length"""       ,				A_						)

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

       _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    self.model_tester.prepare_config_and_inputs_for_common()

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

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _UpperCAmelCase		:       str       =    True

         check_hidden_states_output(A_       ,				A_       ,				A_						)


     def 					__snake_case(     self       ,				A_       ,				A_       ,				A_=False						):
       _UpperCAmelCase		:       Union[str, Any]       =    super()._prepare_for_class(A_       ,				A_       ,				return_labels=A_						)

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

       return inputs_dict


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Any       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_						)


     @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""						)
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       int       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*A_						)


     def 					__snake_case(     self						):
       _UpperCAmelCase		:       int       =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_						)


     def 					__snake_case(     self						):
       if not self.model_tester.is_training:
         return

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

       for model_class in self.all_model_classes:
         # EfficientFormerForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(A_						)
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
           continue
         _UpperCAmelCase		:       str       =    model_class(A_						)
         model.to(A_						)
         model.train()
         _UpperCAmelCase		:       Tuple       =    self._prepare_for_class(A_       ,				A_       ,				return_labels=A_						)
         _UpperCAmelCase		:       str       =    model(**A_						).loss
         loss.backward()


     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       str       =    self.model_tester.prepare_config_and_inputs_for_common()

       _UpperCAmelCase		:       List[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(A_						),
             ]
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
           continue

         for problem_type in problem_types:
           with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''						):
             _UpperCAmelCase		:       List[Any]       =    problem_type["""title"""]
             _UpperCAmelCase		:       Optional[Any]       =    problem_type["""num_labels"""]

             _UpperCAmelCase		:       Tuple       =    model_class(A_						)
             model.to(A_						)
             model.train()

             _UpperCAmelCase		:       List[Any]       =    self._prepare_for_class(A_       ,				A_       ,				return_labels=A_						)

             if problem_type["num_labels"] > 1:
               _UpperCAmelCase		:       Optional[Any]       =    inputs["""labels"""].unsqueeze(1						).repeat(1       ,				problem_type["""num_labels"""]						)

             _UpperCAmelCase		:       int       =    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=A_						) as warning_list:
               _UpperCAmelCase		:       str       =    model(**A_						).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 					__snake_case(     self						):
       for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase		:       str       =    EfficientFormerModel.from_pretrained(A_						)
         self.assertIsNotNone(A_						)




     def 					__snake_case(     self						):
       _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    self.model_tester.prepare_config_and_inputs_for_common()
       _UpperCAmelCase		:       List[Any]       =    True

       _UpperCAmelCase		:       Optional[Any]       =    getattr(self.model_tester       ,				"""seq_length"""       ,				A_						)
       _UpperCAmelCase		:       Dict       =    getattr(self.model_tester       ,				"""encoder_seq_length"""       ,				A_						)
       _UpperCAmelCase		:       Any       =    getattr(self.model_tester       ,				"""key_length"""       ,				A_						)
       _UpperCAmelCase		:       Any       =    getattr(self.model_tester       ,				"""chunk_length"""       ,				A_						)

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

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

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         _UpperCAmelCase		:       int       =    True
         _UpperCAmelCase		:       Optional[int]       =    model_class(A_						)
         model.to(A_						)
         model.eval()
         with torch.no_grad():
           _UpperCAmelCase		:       List[Any]       =    model(**self._prepare_for_class(A_       ,				A_						)						)
         _UpperCAmelCase		:       List[Any]       =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(A_						)       ,				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       a__						(		):
  _UpperCAmelCase		:       Any       =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
  return image





@require_torch
@require_vision
class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):


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


     @slow
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       List[str]       =    EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""						).to(
           A_						)

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

       # forward pass
       with torch.no_grad():
         _UpperCAmelCase		:       int       =    model(**A_						)

       # verify the logits
       _UpperCAmelCase		:       Tuple       =    (1, 10_00)
       self.assertEqual(outputs.logits.shape       ,				A_						)

       _UpperCAmelCase		:       str       =    torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]						).to(A_						)
       self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,				A_       ,				atol=1e-4						)						)




     @slow
     def 					__snake_case(     self						):
       _UpperCAmelCase		:       Union[str, Any]       =    EfficientFormerForImageClassificationWithTeacher.from_pretrained(
           """snap-research/efficientformer-l1-300"""						).to(A_						)

       _UpperCAmelCase		:       List[str]       =    self.default_image_processor
       _UpperCAmelCase		:       int       =    prepare_img()
       _UpperCAmelCase		:       Any       =    image_processor(images=A_       ,				return_tensors="""pt"""						).to(A_						)

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

       # verify the logits
       _UpperCAmelCase		:       str       =    (1, 10_00)
       self.assertEqual(outputs.logits.shape       ,				A_						)

       _UpperCAmelCase		:       Optional[int]       =    torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]						).to(A_						)
       self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,				A_       ,				atol=1e-4						)						)









import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'




def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
  _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
  with open(snake_case__	,      """wb"""    ) as file:
    file.write(image.content    )




def       a__						(		):
  _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
  _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
  _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
  _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
  _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
  _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
  _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
  save_image(snake_case__	,      snake_case__    )
  return (title, description, image_title)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
 print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')





