






import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
   import importlib.resources as pkg_resources
except ImportError:
   # Try backported to PY<37 `importlib_resources`.
   import importlib_resources as pkg_resources

from . import resources


A_		:		str			= 'https://github.com/huggingface/datasets/tree/main/src/datasets/utils'
A_		:		Tuple			= F'''{BASE_REF_URL}/{__file__}'''
A_		:		Optional[Any]			= logging.getLogger(__name__)

def   snake_case					(UpperCAmelCase__  )       ->				Tuple[Any, str]:
  UpperCamelCase_:					int          =				pkg_resources.read_text(snake_case__	,    snake_case__  )
  return yaml.safe_load(snake_case__  ), F'''{BASE_REF_URL}/resources/{resource}'''


A_	, A_		:		Optional[Any]			= load_yaml_resource('readme_structure.yaml')

A_		:		Tuple			= [
    '[Needs More Information]',
    '[More Information Needed]',
    '(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)',
]

# Dictionary representation of section/readme, error_list, warning_list
A_		:		int			= Tuple[dict, List[str], List[str]]




class 				_lowerCAmelCase:





       """simple docstring"""







       def __init__(					self  ,			_lowerCamelCase  ,			_lowerCamelCase  ,			_lowerCamelCase = None  ,			_lowerCamelCase = False							):
         UpperCamelCase_:					List[Any]          =				name
         UpperCamelCase_:					Any          =				level
         UpperCamelCase_:					List[str]          =				lines
         UpperCamelCase_:					Dict          =				''
         UpperCamelCase_:					List[str]          =				True
         UpperCamelCase_:					int          =				{}
         UpperCamelCase_:					Any          =				[]
         UpperCamelCase_:					List[Any]          =				[]
         if self.lines is not None:
           self.parse(suppress_parsing_errors=__a							)





       def 			_a    (					self  ,			_lowerCamelCase = False							):
         UpperCamelCase_:					Tuple          =				''
         UpperCamelCase_:					Tuple          =				[]
         UpperCamelCase_:					Tuple          =				False
         for line in self.lines:
           if line.strip(' \n'							) == "":
             continue
           elif line.strip(' \n'							)[:3] == "```":
             UpperCamelCase_:					Union[str, Any]          =				not code_start
           elif line.split()[0] == self.level + "#" and not code_start:
             if current_sub_level != "":
               UpperCamelCase_:					List[Any]          =				Section(__a  ,			self.level + '#'  ,			__a							)
               UpperCamelCase_:					Optional[int]          =				[]
             else:
               if current_lines != []:
                 self.text += "".join(__a							).strip()
                 if self.text != "" and self.text not in FILLER_TEXT:
                   UpperCamelCase_:					List[Any]          =				False
                 UpperCamelCase_:					List[Any]          =				[]

             UpperCamelCase_:					str          =				' '.join(line.split()[1:]							).strip(' \n'							)
           else:
             current_lines.append(__a							)
         else:
           if current_sub_level != "":
             if current_sub_level in self.content:
               self.parsing_error_list.append(
                   f'''Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections.'''							)
             UpperCamelCase_:					str          =				Section(__a  ,			self.level + '#'  ,			__a							)
           else:
             if current_lines != []:
               self.text += "".join(__a							).strip()
               if self.text != "" and self.text not in FILLER_TEXT:
                 UpperCamelCase_:					Any          =				False

         if self.level == "" and not suppress_parsing_errors:
           if self.parsing_error_list != [] or self.parsing_warning_list != []:
             UpperCamelCase_:					Union[str, Any]          =				'\n'.join('-\t' + x for x in self.parsing_error_list + self.parsing_warning_list							)
             UpperCamelCase_:					Dict          =				f'''The following issues were found while parsing the README at `{self.name}`:\n''' + errors
             raise ValueError(__a							)





       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					Tuple          =				[]
         UpperCamelCase_:					int          =				[]
         if structure["allow_empty"] is False:
           # If content is expected
           if self.is_empty_text and self.content == {}:
             # If no content is found, mention it in the error_list
             error_list.append(f'''Expected some content in section `{self.name}` but it is empty.'''							)

         if structure["allow_empty_text"] is False:
           # If some text is expected
           if self.is_empty_text:
             # If no text is found, mention it in the error_list
             error_list.append(
                 f'''Expected some text in section `{self.name}` but it is empty (text in subsections are ignored).'''							)
                 # Subsections Validation
         if structure["subsections"] is not None:
           # If subsections are expected
           if self.content == {}:
             # If no subsections are present
             UpperCamelCase_:					Optional[int]          =				[subsection['name'] for subsection in structure['subsections']]
             # Mention the expected values in the error_list
             error_list.append(
                 f'''Section `{self.name}` expected the following subsections: {', '.join(['`'+x+'`' for x in values]							)}. Found \'None\'.'''							)
           else:
             # If some subsections are present
             UpperCamelCase_:					str          =				[subsection['name'] for subsection in structure['subsections']]
             UpperCamelCase_:					Optional[Any]          =				False
             for idx, name in enumerate(__a							):
               if name not in self.content:
                 # If the expected subsection is not present
                 error_list.append(f'''Section `{self.name}` is missing subsection: `{name}`.'''							)
                 UpperCamelCase_:					Dict          =				True
               else:
                 # If the subsection is present, validate subsection, return the result
                 # and concat the errors from subsection to section error_list

                 # Skip sublevel validation if current level is `###`
                 if self.level == "###":
                   continue
                 else:
                   UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_:					Any          =				self.content[name].validate(
                       structure['subsections'][idx]							)
                 error_list += subsec_error_list
                 warning_list += subsec_warning_list

             if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
               for name in self.content:
                 if name not in structure_names:
                   # If an extra subsection is present
                   warning_list.append(
                       f'''`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown.'''							)
         if error_list:
           # If there are errors, do not return the dictionary as it is invalid
           return {}, error_list, warning_list
         else:
           return self.to_dict(), error_list, warning_list





       def 			_a    (					self							):
         return {
             "name": self.name,
             "text": self.text,
             "is_empty_text": self.is_empty_text,
             "subsections": [value.to_dict() for value in self.content.values()],
         }







class 				_lowerCAmelCase(       _UpperCamelCase					):  # Level 0





       """simple docstring"""







       def __init__(					self  ,			_lowerCamelCase  ,			_lowerCamelCase  ,			_lowerCamelCase = None  ,			_lowerCamelCase = False							):
         super().__init__(name=__a  ,			level=''							)  # Not using lines here as we need to use a child class parse
         UpperCamelCase_:					Union[str, Any]          =				structure
         UpperCamelCase_:					List[Any]          =				-2
         UpperCamelCase_:					str          =				0
         UpperCamelCase_:					List[Any]          =				lines
         if self.lines is not None:
           self.parse(suppress_parsing_errors=__a							)





       def 			_a    (					self							):





         if self.structure is None:
           UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_:					str          =				self._validate(__a							)
         else:
           UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_:					Dict          =				self._validate(self.structure							)
         if error_list != [] or warning_list != []:
           UpperCamelCase_:					Optional[int]          =				'\n'.join(['-\t' + x for x in error_list + warning_list]							)
           UpperCamelCase_:					Union[str, Any]          =				f'''The following issues were found for the README at `{self.name}`:\n''' + errors
           raise ValueError(__a							)





       @classmethod
       def 			_a    (					cls  ,			_lowerCamelCase  ,			_lowerCamelCase = None  ,			_lowerCamelCase = False							):





         with open(__a  ,			encoding='utf-8'							) as f:
           UpperCamelCase_:					List[Any]          =				f.readlines()
         return cls(__a  ,			__a  ,			__a  ,			suppress_parsing_errors=__a							)





       @classmethod
       def 			_a    (					cls  ,			_lowerCamelCase  ,			_lowerCamelCase = None  ,			_lowerCamelCase = "root"  ,			_lowerCamelCase = False							):
         UpperCamelCase_:					List[str]          =				string.split('\n'							)
         return cls(__a  ,			__a  ,			__a  ,			suppress_parsing_errors=__a							)





       def 			_a    (					self  ,			_lowerCamelCase = False							):
         UpperCamelCase_:					Optional[Any]          =				0

         for line in self.lines:
           self.yaml_tags_line_count += 1
           if line.strip(' \n'							) == "---":
             self.tag_count += 1
             if self.tag_count == 2:
               break
           line_count += 1
         if self.tag_count == 2:
           UpperCamelCase_:					Any          =				self.lines[line_count + 1 :]  # Get the last + 1 th item.
         else:
           UpperCamelCase_:					Optional[Any]          =				self.lines[self.tag_count :]
         super().parse(suppress_parsing_errors=__a							)





       def __str__(					self							):
         return str(self.to_dict()							)





       def 			_a    (					self  ,			_lowerCamelCase							):
         UpperCamelCase_:					str          =				[]
         UpperCamelCase_:					Union[str, Any]          =				[]
         if self.yaml_tags_line_count == 0:
           warning_list.append('Empty YAML markers are present in the README.'							)
         elif self.tag_count == 0:
           warning_list.append('No YAML markers are present in the README.'							)
         elif self.tag_count == 1:
           warning_list.append('Only the start of YAML tags present in the README.'							)
         # Check how many first level sections are present.
         UpperCamelCase_:					Tuple          =				len(self.content.keys()							)
         if num_first_level_keys > 1:
           # If more than one, add to the error list, continue
           error_list.append(
               f'''The README has several first-level headings: {', '.join(['`'+x+'`' for x in list(self.content.keys()							)]							)}. Only one heading is expected. Skipping further validation for this README.'''							)
         elif num_first_level_keys < 1:
           # If less than one, append error.
           error_list.append(
               'The README has no first-level headings. One heading is expected. Skipping further validation for this README.'							)

         else:
           # If one exactly
           UpperCamelCase_:					Dict          =				list(self.content.keys()							)[0]  # Get the key
           if start_key.startswith('Dataset Card for'							):  # Check correct start
             # If the starting is correct, validate all the sections
             UpperCamelCase_      ,UpperCamelCase_      ,UpperCamelCase_:					Tuple          =				self.content[start_key].validate(
                 readme_structure['subsections'][0]							)
             error_list += sec_error_list
             warning_list += sec_warning_list
           else:
             # If not found, append error
             error_list.append(
                 'No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'							)
         if error_list:
           # If there are errors, do not return the dictionary as it is invalid
           return {}, error_list, warning_list
         else:
           return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
   from argparse import ArgumentParser

   A_		:		Any			= ArgumentParser(usage='Validate the content (excluding YAML tags) of a README.md file.')
   ap.add_argument('readme_filepath')
   A_		:		Dict			= ap.parse_args()
   A_		:		Any			= Path(args.readme_filepath)
   A_		:		Optional[int]			= ReadMe.from_readme(readme_filepath)


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     lowercase						:



 '''simple docstring'''


 def __init__(self       ,       __a       ,       __a = 13       ,       __a = 64       ,       __a = 2       ,       __a = 3       ,       __a = 3       ,       __a = True       ,       __a = True       ,       __a = 128       ,       __a=[16, 32, 64, 128]       ,       __a = 7       ,       __a = 4       ,       __a = 37       ,       __a = "gelu"       ,       __a = 0.1       ,       __a = 0.1       ,       __a = 10       ,       __a = 0.02       ,       __a = 2       ,       __a = 1       ,       __a = 128       ,       __a = [2, 2, 2, 2]       ,       __a = 2       ,       __a = 2       ,       ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				parent
  UpperCAmelCase__							=				batch_size
  UpperCAmelCase__							=				image_size
  UpperCAmelCase__							=				patch_size
  UpperCAmelCase__							=				num_channels
  UpperCAmelCase__							=				is_training
  UpperCAmelCase__							=				use_labels
  UpperCAmelCase__							=				hidden_size
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				intermediate_size
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				type_sequence_label_size
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				encoder_stride
  UpperCAmelCase__							=				num_attention_outputs
  UpperCAmelCase__							=				embed_dim
  UpperCAmelCase__							=				embed_dim + 1
  UpperCAmelCase__							=				resolution
  UpperCAmelCase__							=				depths
  UpperCAmelCase__							=				hidden_sizes
  UpperCAmelCase__							=				dim
  UpperCAmelCase__							=				mlp_expansion_ratio




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

  UpperCAmelCase__							=				None
  if self.use_labels:
   UpperCAmelCase__							=				ids_tensor([self.batch_size]       ,       self.type_sequence_label_size )

  UpperCAmelCase__							=				self.get_config()
  return config, pixel_values, labels




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  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   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				EfficientFormerModel(config=__a )
  model.to(__a )
  model.eval()
  UpperCAmelCase__							=				model(__a )
  self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size) )




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.type_sequence_label_size
  UpperCAmelCase__							=				EfficientFormerForImageClassification(__a )
  model.to(__a )
  model.eval()
  UpperCAmelCase__							=				model(__a       ,       labels=__a )
  self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size) )

  # test greyscale images
  UpperCAmelCase__							=				1
  UpperCAmelCase__							=				EfficientFormerForImageClassification(__a )
  model.to(__a )
  model.eval()

  UpperCAmelCase__							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
  UpperCAmelCase__							=				model(__a )
  self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size) )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





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







@require_torch
class     lowercase						(       _UpperCamelCase ,				_UpperCamelCase ,				unittest.TestCase					):



 '''simple docstring'''


 __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   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				EfficientFormerModelTester(self )
  UpperCAmelCase__							=				ConfigTester(
      self       ,       config_class=__a       ,       has_text_modality=__a       ,       hidden_size=37 )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  self.config_tester.run_common_tests()




 @unittest.skip(reason='EfficientFormer does not use inputs_embeds' )
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  pass




 @unittest.skip(reason='EfficientFormer does not support input and output embeddings' )
 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  pass




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()

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

   UpperCAmelCase__							=				['pixel_values']
   self.assertListEqual(arg_names[:1]       ,       __a )




 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  def check_hidden_states_output(__a       ,       __a       ,       __a ):
   UpperCAmelCase__							=				model_class(__a )
   model.to(__a )
   model.eval()

   with torch.no_grad():
    UpperCAmelCase__							=				model(**self._prepare_for_class(__a       ,       __a ) )

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

   UpperCAmelCase__							=				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__							=				self.model_tester.encoder_seq_length
    if hasattr(self.model_tester       ,       'chunk_length' ) and self.model_tester.chunk_length > 1:
     UpperCAmelCase__							=				seq_length * self.model_tester.chunk_length
   else:
    UpperCAmelCase__							=				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__							=				outputs.decoder_hidden_states

    self.assertIsInstance(__a       ,       (list, tuple) )
    self.assertEqual(len(__a )       ,       __a )
    UpperCAmelCase__							=				getattr(self.model_tester       ,       'seq_length'       ,       __a )
    UpperCAmelCase__							=				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__							=				self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   UpperCAmelCase__							=				True
   check_hidden_states_output(__a       ,       __a       ,       __a )

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

   check_hidden_states_output(__a       ,       __a       ,       __a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a=False ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				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   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				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   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_image_modeling(*__a )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*__a )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  if not self.model_tester.is_training:
   return

  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()
  UpperCAmelCase__							=				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__							=				model_class(__a )
   model.to(__a )
   model.train()
   UpperCAmelCase__							=				self._prepare_for_class(__a       ,       __a       ,       return_labels=__a )
   UpperCAmelCase__							=				model(**__a ).loss
   loss.backward()




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()

  UpperCAmelCase__							=				[
      {'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__							=				problem_type['title']
     UpperCAmelCase__							=				problem_type['num_labels']

     UpperCAmelCase__							=				model_class(__a )
     model.to(__a )
     model.train()

     UpperCAmelCase__							=				self._prepare_for_class(__a       ,       __a       ,       return_labels=__a )

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

     UpperCAmelCase__							=				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__							=				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   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCAmelCase__							=				EfficientFormerModel.from_pretrained(__a )
   self.assertIsNotNone(__a )




 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__				,    UpperCAmelCase__							=				self.model_tester.prepare_config_and_inputs_for_common()
  UpperCAmelCase__							=				True

  UpperCAmelCase__							=				getattr(self.model_tester       ,       'seq_length'       ,       __a )
  UpperCAmelCase__							=				getattr(self.model_tester       ,       'encoder_seq_length'       ,       __a )
  UpperCAmelCase__							=				getattr(self.model_tester       ,       'key_length'       ,       __a )
  UpperCAmelCase__							=				getattr(self.model_tester       ,       'chunk_length'       ,       __a )

  if chunk_length is not None and hasattr(self.model_tester       ,       'num_hashes' ):
   UpperCAmelCase__							=				encoder_seq_length * self.model_tester.num_hashes

  for model_class in self.all_model_classes:
   UpperCAmelCase__							=				True
   UpperCAmelCase__							=				False
   UpperCAmelCase__							=				True
   UpperCAmelCase__							=				model_class(__a )
   model.to(__a )
   model.eval()
   with torch.no_grad():
    UpperCAmelCase__							=				model(**self._prepare_for_class(__a       ,       __a ) )
   UpperCAmelCase__							=				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__							=				True
   UpperCAmelCase__							=				model_class(__a )
   model.to(__a )
   model.eval()
   with torch.no_grad():
    UpperCAmelCase__							=				model(**self._prepare_for_class(__a       ,       __a ) )
   UpperCAmelCase__							=				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 						UpperCamelCase_(			)       -> Tuple:
 UpperCAmelCase__							=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
 return image







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



 '''simple docstring'''


 @cached_property
 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  return (
      EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300' )
      if is_vision_available()
      else None
  )




 @slow
 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300' ).to(
      __a )

  UpperCAmelCase__							=				self.default_image_processor
  UpperCAmelCase__							=				prepare_img()
  UpperCAmelCase__							=				image_processor(images=__a       ,       return_tensors='pt' ).to(__a )

  # forward pass
  with torch.no_grad():
   UpperCAmelCase__							=				model(**__a )

  # verify the logits
  UpperCAmelCase__							=				(1, 1000)
  self.assertEqual(outputs.logits.shape       ,       __a )

  UpperCAmelCase__							=				torch.tensor([-0.05_55, 0.48_25, -0.08_52] ).to(__a )
  self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,       __a       ,       atol=1E-4 ) )




 @slow
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				EfficientFormerForImageClassificationWithTeacher.from_pretrained(
      'snap-research/efficientformer-l1-300' ).to(__a )

  UpperCAmelCase__							=				self.default_image_processor
  UpperCAmelCase__							=				prepare_img()
  UpperCAmelCase__							=				image_processor(images=__a       ,       return_tensors='pt' ).to(__a )

  # forward pass
  with torch.no_grad():
   UpperCAmelCase__							=				model(**__a )

  # verify the logits
  UpperCAmelCase__							=				(1, 1000)
  self.assertEqual(outputs.logits.shape       ,       __a )

  UpperCAmelCase__							=				torch.tensor([-0.13_12, 0.43_53, -1.04_99] ).to(__a )
  self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,       __a       ,       atol=1E-4 ) )



