

import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 						UpperCamelCase_(			snake_case__:  Union[str, Any]  ,    snake_case__:  List[str]=0.9_9_9  ,    snake_case__:  Dict="cosine"  ,    )       -> Tuple:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case__:  Dict   ):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(snake_case__:  Optional[int]   ):
   return math.exp(t * -1_2.0   )

 else:
  raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"   )

 UpperCAmelCase__							=				[]
 for i in range(snake_case__   ):
  UpperCAmelCase__							=				i / num_diffusion_timesteps
  UpperCAmelCase__							=				(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(snake_case__   ) / alpha_bar_fn(snake_case__   )  ,    snake_case__   )   )
 return torch.tensor(snake_case__  ,    dtype=torch.floataa   )







class     lowercase						(       _UpperCamelCase ,				_UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				[e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE												=				2




 @register_to_config
 def __init__(self       ,       __a = 1000       ,       __a = 0.0_00_85       ,       __a = 0.0_12       ,       __a = "linear"       ,       __a = None       ,       __a = "epsilon"       ,       __a = "linspace"       ,       __a = 0       ,       ) ->					Optional[int]:






  """simple docstring"""





  if trained_betas is not None:
   UpperCAmelCase__							=				torch.tensor(__a       ,       dtype=torch.floataa )
  elif beta_schedule == "linear":
   UpperCAmelCase__							=				torch.linspace(__a       ,       __a       ,       __a       ,       dtype=torch.floataa )
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   UpperCAmelCase__							=				(
       torch.linspace(beta_start**0.5       ,       beta_end**0.5       ,       __a       ,       dtype=torch.floataa ) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   UpperCAmelCase__							=				betas_for_alpha_bar(__a )
  else:
   raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

  UpperCAmelCase__							=				1.0 - self.betas
  UpperCAmelCase__							=				torch.cumprod(self.alphas       ,       dim=0 )

  #  set all values
  self.set_timesteps(__a       ,       __a       ,       __a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a=None ) ->					List[str]:






  """simple docstring"""





  if schedule_timesteps is None:
   UpperCAmelCase__							=				self.timesteps

  UpperCAmelCase__							=				(schedule_timesteps == timestep).nonzero()

  # The sigma index that is taken for the **very** first `step`
  # is always the second index (or the last index if there is only 1)
  # This way we can ensure we don't accidentally skip a sigma in
  # case we start in the middle of the denoising schedule (e.g. for image-to-image)
  if len(self._index_counter ) == 0:
   UpperCAmelCase__							=				1 if len(__a ) > 1 else 0
  else:
   UpperCAmelCase__							=				timestep.cpu().item() if torch.is_tensor(__a ) else timestep
   UpperCAmelCase__							=				self._index_counter[timestep_int]

  return indices[pos].item()




 @property
 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  if self.config.timestep_spacing in ["linspace", "trailing"]:
   return self.sigmas.max()

  return (self.sigmas.max() ** 2 + 1) ** 0.5




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       ) ->					torch.FloatTensor:






  """simple docstring"""





  UpperCAmelCase__							=				self.index_for_timestep(__a )

  if self.state_in_first_order:
   UpperCAmelCase__							=				self.sigmas[step_index]
  else:
   UpperCAmelCase__							=				self.sigmas_interpol[step_index - 1]

  UpperCAmelCase__							=				sample / ((sigma**2 + 1) ** 0.5)
  return sample




 def   UpperCamelCase__							(self       ,       __a       ,       __a = None       ,       __a = None       ,       ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				num_inference_steps

  UpperCAmelCase__							=				num_train_timesteps or self.config.num_train_timesteps

  # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  if self.config.timestep_spacing == "linspace":
   UpperCAmelCase__							=				np.linspace(0       ,       num_train_timesteps - 1       ,       __a       ,       dtype=__a )[::-1].copy()
  elif self.config.timestep_spacing == "leading":
   UpperCAmelCase__							=				num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCAmelCase__							=				(np.arange(0       ,       __a ) * step_ratio).round()[::-1].copy().astype(__a )
   timesteps += self.config.steps_offset
  elif self.config.timestep_spacing == "trailing":
   UpperCAmelCase__							=				num_train_timesteps / self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCAmelCase__							=				(np.arange(__a       ,       0       ,       -step_ratio )).round().copy().astype(__a )
   timesteps -= 1
  else:
   raise ValueError(
       F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

  UpperCAmelCase__							=				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
  UpperCAmelCase__							=				torch.from_numpy(np.log(__a ) ).to(__a )

  UpperCAmelCase__							=				np.interp(__a       ,       np.arange(0       ,       len(__a ) )       ,       __a )
  UpperCAmelCase__							=				np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
  UpperCAmelCase__							=				torch.from_numpy(__a ).to(device=__a )

  # compute up and down sigmas
  UpperCAmelCase__							=				sigmas.roll(-1 )
  UpperCAmelCase__							=				0.0
  UpperCAmelCase__							=				(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
  UpperCAmelCase__							=				(sigmas_next**2 - sigmas_up**2) ** 0.5
  UpperCAmelCase__							=				0.0

  # compute interpolated sigmas
  UpperCAmelCase__							=				sigmas.log().lerp(sigmas_down.log()       ,       0.5 ).exp()
  UpperCAmelCase__							=				0.0

  # set sigmas
  UpperCAmelCase__							=				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
  UpperCAmelCase__							=				torch.cat(
      [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
  UpperCAmelCase__							=				torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2 ), sigmas_up[-1:]] )
  UpperCAmelCase__							=				torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2 ), sigmas_down[-1:]] )

  if str(__a ).startswith('mps' ):
   # mps does not support float64
   UpperCAmelCase__							=				torch.from_numpy(__a ).to(__a       ,       dtype=torch.floataa )
  else:
   UpperCAmelCase__							=				torch.from_numpy(__a ).to(__a )

  UpperCAmelCase__							=				self.sigma_to_t(__a ).to(__a       ,       dtype=timesteps.dtype )
  UpperCAmelCase__							=				torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])       ,       dim=-1 ).flatten()

  UpperCAmelCase__							=				torch.cat([timesteps[:1], interleaved_timesteps] )

  UpperCAmelCase__							=				None

  # for exp beta schedules, such as the one for `pipeline_shap_e.py`
  # we need an index counter
  UpperCAmelCase__							=				defaultdict(__a )




 def   UpperCamelCase__							(self       ,       __a ) ->					int:






  """simple docstring"""





  UpperCAmelCase__							=				sigma.log()

  # get distribution
  UpperCAmelCase__							=				log_sigma - self.log_sigmas[:, None]

  # get sigmas range
  UpperCAmelCase__							=				dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
  UpperCAmelCase__							=				low_idx + 1

  UpperCAmelCase__							=				self.log_sigmas[low_idx]
  UpperCAmelCase__							=				self.log_sigmas[high_idx]

  # interpolate sigmas
  UpperCAmelCase__							=				(low - log_sigma) / (low - high)
  UpperCAmelCase__							=				w.clamp(0       ,       1 )

  # transform interpolation to time range
  UpperCAmelCase__							=				(1 - w) * low_idx + w * high_idx
  UpperCAmelCase__							=				t.view(sigma.shape )
  return t




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






  """simple docstring"""





  return self.sample is None




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a       ,       __a = None       ,       __a = True       ,       ) ->					Union[SchedulerOutput, Tuple]:






  """simple docstring"""





  UpperCAmelCase__							=				self.index_for_timestep(__a )

  # advance index counter by 1
  UpperCAmelCase__							=				timestep.cpu().item() if torch.is_tensor(__a ) else timestep
  self._index_counter[timestep_int] += 1

  if self.state_in_first_order:
   UpperCAmelCase__							=				self.sigmas[step_index]
   UpperCAmelCase__							=				self.sigmas_interpol[step_index]
   UpperCAmelCase__							=				self.sigmas_up[step_index]
   UpperCAmelCase__							=				self.sigmas_down[step_index - 1]
  else:
   # 2nd order / KPDM2's method
   UpperCAmelCase__							=				self.sigmas[step_index - 1]
   UpperCAmelCase__							=				self.sigmas_interpol[step_index - 1]
   UpperCAmelCase__							=				self.sigmas_up[step_index - 1]
   UpperCAmelCase__							=				self.sigmas_down[step_index - 1]

  # currently only gamma=0 is supported. This usually works best anyways.
  # We can support gamma in the future but then need to scale the timestep before
  # passing it to the model which requires a change in API
  UpperCAmelCase__							=				0
  UpperCAmelCase__							=				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

  UpperCAmelCase__							=				model_output.device
  UpperCAmelCase__							=				randn_tensor(model_output.shape       ,       dtype=model_output.dtype       ,       device=__a       ,       generator=__a )

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  if self.config.prediction_type == "epsilon":
   UpperCAmelCase__							=				sigma_hat if self.state_in_first_order else sigma_interpol
   UpperCAmelCase__							=				sample - sigma_input * model_output
  elif self.config.prediction_type == "v_prediction":
   UpperCAmelCase__							=				sigma_hat if self.state_in_first_order else sigma_interpol
   UpperCAmelCase__							=				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
       sample / (sigma_input**2 + 1)
   )
  elif self.config.prediction_type == "sample":
   raise NotImplementedError('prediction_type not implemented yet: sample' )
  else:
   raise ValueError(
       F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

  if self.state_in_first_order:
   # 2. Convert to an ODE derivative for 1st order
   UpperCAmelCase__							=				(sample - pred_original_sample) / sigma_hat
   # 3. delta timestep
   UpperCAmelCase__							=				sigma_interpol - sigma_hat

   # store for 2nd order step
   UpperCAmelCase__							=				sample
   UpperCAmelCase__							=				dt
   UpperCAmelCase__							=				sample + derivative * dt
  else:
   # DPM-Solver-2
   # 2. Convert to an ODE derivative for 2nd order
   UpperCAmelCase__							=				(sample - pred_original_sample) / sigma_interpol
   # 3. delta timestep
   UpperCAmelCase__							=				sigma_down - sigma_hat

   UpperCAmelCase__							=				self.sample
   UpperCAmelCase__							=				None

   UpperCAmelCase__							=				sample + derivative * dt
   UpperCAmelCase__							=				prev_sample + noise * sigma_up

  if not return_dict:
   return (prev_sample,)

  return SchedulerOutput(prev_sample=__a )




 def   UpperCamelCase__							(self       ,       __a       ,       __a       ,       __a       ,       ) ->					torch.FloatTensor:






  """simple docstring"""





  UpperCAmelCase__							=				self.sigmas.to(device=original_samples.device       ,       dtype=original_samples.dtype )
  if original_samples.device.type == "mps" and torch.is_floating_point(__a ):
   # mps does not support float64
   UpperCAmelCase__							=				self.timesteps.to(original_samples.device       ,       dtype=torch.floataa )
   UpperCAmelCase__							=				timesteps.to(original_samples.device       ,       dtype=torch.floataa )
  else:
   UpperCAmelCase__							=				self.timesteps.to(original_samples.device )
   UpperCAmelCase__							=				timesteps.to(original_samples.device )

  UpperCAmelCase__							=				[self.index_for_timestep(__a       ,       __a ) for t in timesteps]

  UpperCAmelCase__							=				sigmas[step_indices].flatten()
  while len(sigma.shape ) < len(original_samples.shape ):
   UpperCAmelCase__							=				sigma.unsqueeze(-1 )

  UpperCAmelCase__							=				original_samples + noise * sigma
  return noisy_samples




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






  """simple docstring"""





  return self.config.num_train_timesteps






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 ) )



