




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
 from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Tuple=13       ,				snake_case    :       List[str]=32       ,				snake_case    :       str=3       ,				snake_case    :       Dict=[3, 3, 5]       ,				snake_case    :       Dict=[32, 16, 24]       ,				snake_case    :       Optional[int]=[16, 24, 40]       ,				snake_case    :       str=[1, 1, 2]       ,				snake_case    :       Tuple=[1, 1, 2]       ,				snake_case    :       List[Any]=[1, 6, 6]       ,				snake_case    :       int=True       ,				snake_case    :       Union[str, Any]=True       ,				snake_case    :       Dict=37       ,				snake_case    :       Tuple="gelu"       ,				snake_case    :       Optional[int]=10       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  kernel_sizes
          __UpperCamelCase     						=  in_channels
          __UpperCamelCase     						=  out_channels
          __UpperCamelCase     						=  strides
          __UpperCamelCase     						=  num_block_repeats
          __UpperCamelCase     						=  expand_ratios
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  num_labels
          __UpperCamelCase     						=  use_labels







      def 					snake_case		(   self    :       Tuple ):
          __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.num_labels )

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







      def 					snake_case		(   self    :       Optional[int] ):
          return EfficientNetConfig(
              num_channels=self.num_channels       ,				kernel_sizes=self.kernel_sizes       ,				in_channels=self.in_channels       ,				out_channels=self.out_channels       ,				strides=self.strides       ,				num_block_repeats=self.num_block_repeats       ,				expand_ratios=self.expand_ratios       ,				hidden_act=self.hidden_act       ,				num_labels=self.num_labels       ,				)







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Dict       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  EfficientNetModel(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case )
          # expected last hidden states: B, C, H // 4, W // 4
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)       ,				)







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       List[str]       ,				snake_case    :       List[str]       ,				snake_case    :       List[Any] ):
          __UpperCamelCase     						=  EfficientNetForImageClassification(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels) )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[str]			=    (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
      lowerCAmelCase__	:       int			=    (
          {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__	:       int			=    False
      lowerCAmelCase__	:       Any			=    False
      lowerCAmelCase__	:       Tuple			=    False
      lowerCAmelCase__	:       List[Any]			=    False
      lowerCAmelCase__	:       Tuple			=    False







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  EfficientNetModelTester(self )
          __UpperCamelCase     						=  ConfigTester(
              self       ,				config_class=snake_case       ,				has_text_modality=snake_case       ,				hidden_size=37 )







      def 					snake_case		(   self    :       Tuple ):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







      def 					snake_case		(   self    :       Any ):
          return







      @unittest.skip(reason='''EfficientNet does not use inputs_embeds''' )
      def 					snake_case		(   self    :       Union[str, Any] ):
          pass







      @unittest.skip(reason='''EfficientNet does not support input and output embeddings''' )
      def 					snake_case		(   self    :       Tuple ):
          pass







      @unittest.skip(reason='''EfficientNet does not use feedforward chunking''' )
      def 					snake_case		(   self    :       Optional[int] ):
          pass







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase     						=  model_class(snake_case )
              __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]       ,				snake_case )







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       Union[str, Any] ):
          def check_hidden_states_output(snake_case    :       int       ,				snake_case    :       int       ,				snake_case    :       str ):
              __UpperCamelCase     						=  model_class(snake_case )
              model.to(snake_case )
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase     						=  model(**self._prepare_for_class(snake_case       ,				snake_case ) )

              __UpperCamelCase     						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
              __UpperCamelCase     						=  sum(config.num_block_repeats ) * 4
              self.assertEqual(len(snake_case )       ,				snake_case )

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

          __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(snake_case       ,				snake_case       ,				snake_case )

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

              check_hidden_states_output(snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case )







      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def 					snake_case		(   self    :       List[str] ):
          pass







      @slow
      def 					snake_case		(   self    :       Dict ):
          for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase     						=  EfficientNetModel.from_pretrained(snake_case )
              self.assertIsNotNone(snake_case )







      @is_pipeline_test
      @require_vision
      @slow
      def 					snake_case		(   self    :       Tuple ):
          super().test_pipeline_image_classification()







def     __SCREAMING_SNAKE_CASE      (     )				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image


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

      """simple docstring"""

      @cached_property
      def 					snake_case		(   self    :       Tuple ):
          return AutoImageProcessor.from_pretrained('''google/efficientnet-b7''' ) if is_vision_available() else None







      @slow
      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7''' ).to(snake_case )

          __UpperCamelCase     						=  self.default_image_processor
          __UpperCamelCase     						=  prepare_img()
          __UpperCamelCase     						=  image_processor(images=snake_case       ,				return_tensors='''pt''' ).to(snake_case )

          # forward pass
          with torch.no_grad():
              __UpperCamelCase     						=  model(**snake_case )

          # verify the logits
          __UpperCamelCase     						=  torch.Size((1, 1000) )
          self.assertEqual(outputs.logits.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor([0.00_01, 0.00_02, 0.00_02] ).to(snake_case )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				snake_case       ,				atol=1E-4 ) )







'''simple docstring'''




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int   )	->      Union[str, Any]:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    A_     =   sorted(_UpperCamelCase   )
    A_     =   [bisect.bisect_right(_UpperCamelCase,   _UpperCamelCase   ) for y in x]
    return quantized





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple=0   )	->      str:
    A_     =   np.arange(start=3,   stop=_UpperCamelCase,   step=4   ).tolist() if k > 0 else [10]
    A_     =   _quantize(_UpperCamelCase,   _UpperCamelCase   )
    # count number of elements per group
    A_     =   np.unique(_UpperCamelCase,   return_counts=_UpperCamelCase   )[1]
    A_     =   [0] + bins + [np.inf]
    logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCamelCase   )   )
    logger.info('''Count of instances per bin: {}'''.format(_UpperCamelCase   )   )
    return groups




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           if not isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
               raise ValueError(
                   '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_SCREAMING_SNAKE_CASE ) )
           A_     =   sampler
           A_     =   group_ids
           A_     =   batch_size

       def __iter__(      self )		->     Tuple:
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )

           A_     =   0
           for idx in self.sampler:
               A_     =   self.group_ids[idx]
               buffer_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               samples_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               if len(buffer_per_group[group_id] ) == self.batch_size:
                   yield buffer_per_group[group_id]  # TODO
                   num_batches += 1
                   del buffer_per_group[group_id]
               assert len(buffer_per_group[group_id] ) < self.batch_size

           # now we have run out of elements that satisfy
           # the group criteria, let's return the remaining
           # elements so that the size of the sampler is
           # deterministic
           A_     =   len(self )
           A_     =   expected_num_batches - num_batches
           if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               A_     =   []
               for group_id, idxs in sorted(buffer_per_group.items()    ,     key=lambda _SCREAMING_SNAKE_CASE : x[0] ):
                   batch_idx.extend(_SCREAMING_SNAKE_CASE )
                   if len(_SCREAMING_SNAKE_CASE ) >= self.batch_size:
                       yield batch_idx[: self.batch_size]
                       A_     =   batch_idx[self.batch_size :]
                       num_remaining -= 1
               if len(_SCREAMING_SNAKE_CASE ) > 0:
                   yield batch_idx
                   num_remaining -= 1
           assert num_remaining == 0

       def __len__(      self )		->     Optional[int]:
           return (len(self.sampler ) + self.batch_size - 1) // self.batch_size






