

"""simple docstring"""


import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


a__   :       List[str]   						=			logging.get_logger(__name__)

if is_vision_available():
   import PIL.Image
   from PIL import Image
   from PIL.Image import Image as ImageType
else:
   a__   :       Dict   						=			object

if is_torch_available():
   import torch

if is_soundfile_availble():
   import soundfile as sf



class        lowercase_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			value

       def __str__(     self       ):






        '''simple docstring'''

        return self.to_string()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        logger.error(
            "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"       )
        return self._value

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        logger.error(
            "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"       )
        return str(self._value       )



class        lowercase_			(       a_			,a_							):

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._value

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        return self._value



class        lowercase_			(       a_			,a_							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        super().__init__(snake_case__       )

        if not is_vision_available():
         raise ImportError("PIL must be installed in order to handle images."       )

        snake_case__      							=			None
        snake_case__      							=			None
        snake_case__      							=			None

        if isinstance(snake_case__				,   snake_case__       ):
         snake_case__      							=			value
        elif isinstance(snake_case__				,   (str, pathlib.Path)       ):
         snake_case__      							=			value
        elif isinstance(snake_case__				,   torch.Tensor       ):
         snake_case__      							=			value
        else:
         raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(snake_case__       )}"       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=None       ):






        '''simple docstring'''

        from IPython.display import Image, display

        display(Image(self.to_string()       )       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if self._raw is not None:
         return self._raw

        if self._path is not None:
         snake_case__      							=			Image.open(self._path       )
         return self._raw

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if self._path is not None:
         return self._path

        if self._raw is not None:
         snake_case__      							=			tempfile.mkdtemp()
         snake_case__      							=			os.path.join(snake_case__				,   str(uuid.uuida()       ) + ".png"       )
         self._raw.save(self._path       )

         return self._path

        if self._tensor is not None:
         snake_case__      							=			self._tensor.cpu().detach().numpy()

         # There is likely simpler than load into image into save
         snake_case__      							=			Image.fromarray((array * 255).astype(np.uinta       )       )

         snake_case__      							=			tempfile.mkdtemp()
         snake_case__      							=			os.path.join(snake_case__				,   str(uuid.uuida()       ) + ".png"       )

         img.save(self._path       )

         return self._path



class        lowercase_			(       a_							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=1_6000       ):






        '''simple docstring'''

        super().__init__(snake_case__       )

        if not is_soundfile_availble():
         raise ImportError("soundfile must be installed in order to handle audio."       )

        snake_case__      							=			None
        snake_case__      							=			None

        snake_case__      							=			samplerate

        if isinstance(snake_case__				,   (str, pathlib.Path)       ):
         snake_case__      							=			value
        elif isinstance(snake_case__				,   torch.Tensor       ):
         snake_case__      							=			value
        else:
         raise ValueError(F"Unsupported audio type: {type(snake_case__       )}"       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE=None				,   __SCREAMING_SNAKE_CASE=None       ):






        '''simple docstring'''

        from IPython.display import Audio, display

        display(Audio(self.to_string()				,   rate=self.samplerate       )       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if self._tensor is not None:
         return self._tensor

        if self._path is not None:
         snake_case__							,				snake_case__      							=			sf.read(self._path       )
         snake_case__      							=			torch.tensor(snake_case__       )
         return self._tensor

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if self._path is not None:
         return self._path

        if self._tensor is not None:
         snake_case__      							=			tempfile.mkdtemp()
         snake_case__      							=			os.path.join(snake_case__				,   str(uuid.uuida()       ) + ".wav"       )
         sf.write(self._path				,   self._tensor				,   samplerate=self.samplerate       )
         return self._path
a__   :       str   						=			{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
a__   :       List[Any]   						=			{str: AgentText}

if is_vision_available():
   a__   :       Optional[int]   						=			AgentImage





def 							a__     (					*_UpperCAmelCase		,       **_UpperCAmelCase		)      ->					str:

 '''simple docstring'''






 snake_case__      							=			[(arg.to_raw() if isinstance(_lowerCAmelCase		,       _lowerCAmelCase		) else arg) for arg in args]
 snake_case__      							=			{k: (v.to_raw() if isinstance(_lowerCAmelCase		,       _lowerCAmelCase		) else v) for k, v in kwargs.items()}
 return args, kwargs





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase=None		)      ->					int:

 '''simple docstring'''






 if isinstance(_lowerCAmelCase		,       _lowerCAmelCase		):
  snake_case__      							=			{}
  for i, (k, v) in enumerate(outputs.items()		):
   if output_types is not None:
    # If the class has defined outputs, we can map directly according to the class definition
    if output_types[i] in AGENT_TYPE_MAPPING:
     snake_case__      							=			AGENT_TYPE_MAPPING[output_types[i]](_lowerCAmelCase		)
    else:
     snake_case__      							=			AgentType(_lowerCAmelCase		)

   else:
    # If the class does not have defined output, then we map according to the type
    for _k, _v in INSTANCE_TYPE_MAPPING.items():
     if isinstance(_lowerCAmelCase		,       _k		):
      snake_case__      							=			_v(_lowerCAmelCase		)
    if k not in decoded_outputs:
     snake_case__      							=			AgentType[v]

 elif isinstance(_lowerCAmelCase		,       (list, tuple)		):
  snake_case__      							=			type(_lowerCAmelCase		)()
  for i, v in enumerate(_lowerCAmelCase		):
   if output_types is not None:
    # If the class has defined outputs, we can map directly according to the class definition
    if output_types[i] in AGENT_TYPE_MAPPING:
     decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](_lowerCAmelCase		)		)
    else:
     decoded_outputs.append(AgentType(_lowerCAmelCase		)		)
   else:
    # If the class does not have defined output, then we map according to the type
    snake_case__      							=			False
    for _k, _v in INSTANCE_TYPE_MAPPING.items():
     if isinstance(_lowerCAmelCase		,       _k		):
      decoded_outputs.append(_v(_lowerCAmelCase		)		)
      snake_case__      							=			True

    if not found:
     decoded_outputs.append(AgentType(_lowerCAmelCase		)		)

 else:
  if output_types[0] in AGENT_TYPE_MAPPING:
   # If the class has defined outputs, we can map directly according to the class definition
   snake_case__      							=			AGENT_TYPE_MAPPING[output_types[0]](_lowerCAmelCase		)

  else:
   # If the class does not have defined output, then we map according to the type
   for _k, _v in INSTANCE_TYPE_MAPPING.items():
    if isinstance(_lowerCAmelCase		,       _k		):
     return _v(_lowerCAmelCase		)
   return AgentType(_lowerCAmelCase		)

 return decoded_outputs








"""simple docstring"""


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_			:

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






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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.type_sequence_label_size       )

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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=__SCREAMING_SNAKE_CASE				,   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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

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

        # test greyscale images
        snake_case__      							=			1
        snake_case__      							=			EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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_torch
class        lowercase_			(       __lowerCAmelCase			,__lowerCAmelCase			,unittest.TestCase							):
       snake_case__			:							str										=       (
           (
               EfficientFormerModel,
               EfficientFormerForImageClassificationWithTeacher,
               EfficientFormerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       snake_case__			:							Optional[Any]										=       (
           {
               """feature-extraction""": EfficientFormerModel,
               """image-classification""": (
                   EfficientFormerForImageClassification,
                   EfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_torch_available()
           else {}
       )
       snake_case__			:							Tuple										=       False

       snake_case__			:							List[Any]										=       False
       snake_case__			:							Union[str, Any]										=       False
       snake_case__			:							Optional[Any]										=       False

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerModelTester(self       )
        snake_case__      							=			ConfigTester(
            self				,   config_class=__SCREAMING_SNAKE_CASE				,   has_text_modality=__SCREAMING_SNAKE_CASE				,   hidden_size=37       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

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






        '''simple docstring'''

        pass

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






        '''simple docstring'''

        pass

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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.forward       )
         # 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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        def check_hidden_states_output(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()

         with torch.no_grad():
          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__      							=			getattr(
             self.model_tester				,   "expected_num_hidden_layers"				,   self.model_tester.num_hidden_layers + 1       )
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   __SCREAMING_SNAKE_CASE       )

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

          self.assertIsInstance(__SCREAMING_SNAKE_CASE				,   (list, tuple)       )
          self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   __SCREAMING_SNAKE_CASE       )
          snake_case__      							=			getattr(self.model_tester				,   "seq_length"				,   __SCREAMING_SNAKE_CASE       )
          snake_case__      							=			getattr(self.model_tester				,   "decoder_seq_length"				,   __SCREAMING_SNAKE_CASE       )

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

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         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 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=False       ):






        '''simple docstring'''

        snake_case__      							=			super()._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   return_labels=__SCREAMING_SNAKE_CASE       )

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

        return inputs_dict

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

       @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"       )
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        if not self.model_tester.is_training:
         return

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

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			self.model_tester.prepare_config_and_inputs_for_common()

        snake_case__      							=			[
            {"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(__SCREAMING_SNAKE_CASE       ),
             ]
             or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
         ):
          continue

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

           snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
           model.to(__SCREAMING_SNAKE_CASE       )
           model.train()

           snake_case__      							=			self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   return_labels=__SCREAMING_SNAKE_CASE       )

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

           snake_case__      							=			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=__SCREAMING_SNAKE_CASE       ) as warning_list:
            snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       ).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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

        snake_case__      							=			getattr(self.model_tester				,   "seq_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "encoder_seq_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "key_length"				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			getattr(self.model_tester				,   "chunk_length"				,   __SCREAMING_SNAKE_CASE       )

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

        for model_class in self.all_model_classes:
         snake_case__      							=			True
         snake_case__      							=			False
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   self.model_tester.num_attention_outputs       )

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         snake_case__      							=			True
         snake_case__      							=			model_class(__SCREAMING_SNAKE_CASE       )
         model.to(__SCREAMING_SNAKE_CASE       )
         model.eval()
         with torch.no_grad():
          snake_case__      							=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )       )
         snake_case__      							=			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(__SCREAMING_SNAKE_CASE       )				,   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__     (					)      ->					Dict:

 '''simple docstring'''






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



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

       @cached_property
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"       ).to(
            __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.default_image_processor
        snake_case__      							=			prepare_img()
        snake_case__      							=			image_processor(images=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

        # forward pass
        with torch.no_grad():
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       )

        # verify the logits
        snake_case__      							=			(1, 1000)
        self.assertEqual(outputs.logits.shape				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.tensor([-0.0555, 0.4825, -0.0852]       ).to(__SCREAMING_SNAKE_CASE       )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-4       )       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			EfficientFormerForImageClassificationWithTeacher.from_pretrained(
            "snap-research/efficientformer-l1-300"       ).to(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.default_image_processor
        snake_case__      							=			prepare_img()
        snake_case__      							=			image_processor(images=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

        # forward pass
        with torch.no_grad():
         snake_case__      							=			model(**__SCREAMING_SNAKE_CASE       )

        # verify the logits
        snake_case__      							=			(1, 1000)
        self.assertEqual(outputs.logits.shape				,   __SCREAMING_SNAKE_CASE       )

        snake_case__      							=			torch.tensor([-0.1312, 0.4353, -1.0499]       ).to(__SCREAMING_SNAKE_CASE       )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]				,   __SCREAMING_SNAKE_CASE				,   atol=1e-4       )       )

