import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


lowerCamelCase_				:		Optional[int]       						=       logging.getLogger(__name__)
def 							lowerCAmelCase(	__lowerCamelCase						):
					random.seed(snake_case__						)
					np.random.seed(snake_case__						)
					torch.manual_seed(snake_case__						)
					torch.cuda.manual_seed_all(snake_case__						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					model.eval()
					__a							=   next(model.parameters()						).device
					__a							=   1
					__a							=   torch.zeros((eval_batch_size, context_len)      ,				dtype=torch.long      ,				device=snake_case__						)
					__a							=   DataLoader(snake_case__      ,				shuffle=snake_case__      ,				batch_size=snake_case__						)
					__a							=   torch.zeros(1      ,				device=snake_case__						)
					__a							=   0
					for batch in eval_dataloader:
										batch.to(snake_case__						)
										# pad
										context.zero_()
										for i in range(snake_case__						):
															__a							=   batch[i]
										__a							=   model(snake_case__      ,				labels=snake_case__						)
										eval_loss += outputs[0].sum().item()
										nb_eval_examples += batch.size(0						)
					__a							=   eval_loss / nb_eval_examples
					__a							=   torch.exp(snake_case__						)
					model.train()
					return perplexity
def 							lowerCAmelCase(	__lowerCamelCase="gpt2"						):
					__a							=   GPTaLMHeadModel.from_pretrained(snake_case__      ,				output_hidden_states=snake_case__						)
					torch.save(model.state_dict()      ,				model_name + 'local.pt'						)
					return model
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   copy.deepcopy(snake_case__						)
					model.to(snake_case__						)

					__a							=   ['bias', 'LayerNorm.weight']
					__a							=   [
					    {
					        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
					        'weight_decay': 0.0,
					    },
					    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)], 'weight_decay': 0.0},
					]
					__a							=   AdamW(snake_case__      ,				lr=5e-5      ,				eps=1e-8						)
					__a							=   get_linear_schedule_with_warmup(snake_case__      ,				0      ,				snake_case__						)
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   np.array(snake_case__						).mean()
					__a							=   np.array(snake_case__						).std()
					__a							=   (real_perps - avg) / std
					__a							=   list(zip(snake_case__      ,				snake_case__      ,				snake_case__						)						)
					joblib.dump(snake_case__      ,				snake_case__						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase="dev.jbl"      ,				__lowerCamelCase=recopy_gpta      ,				):
					__a							=   []
					__a							=   []
					__a							=   []

					# Initialize the transformer model
					__a							=   copy.deepcopy(snake_case__						)
					orig_model.to(device='cpu'						)
					torch.cuda.empty_cache()

					# Compute perplexity of initial transformer model for comparison
					model.train()
					__a					,      __a					,      __a							=   recopy_model(snake_case__      ,				snake_case__      ,				snake_case__						)

					for step in tqdm(range(snake_case__						)						):
										__a							=   torch.zeros((1, context_len)      ,				dtype=torch.long      ,				device=snake_case__						)
										__a							=   random.choice(snake_case__						)
										__a							=   random.randint(0      ,				len(story[0]						) - context_len - 1						)
										__a							=   story[0][start : start + context_len]
										lm_optimizer.zero_grad()
										__a							=   model(snake_case__      ,				labels=snake_case__						)
										__a							=   outputs[0]
										__a							=   compute_perplexity(snake_case__      ,				snake_case__      ,				snake_case__						)
										model.train()
										lm_loss.backward()
										# Do LM backprop
										torch.nn.utils.clip_grad_norm_(model.parameters()      ,				3.0						)
										lm_optimizer.step()
										lm_scheduler.step()  # Update learning rate schedule

										# Compute perplexity after back-propagating on the selected context
										__a							=   compute_perplexity(snake_case__      ,				snake_case__      ,				snake_case__						)

										# Periodically save the stored (X, IG(X)) pairs
										if step % 1000 == 0 and step > 1:
															intermittent_save(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__						)

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										__a					,      __a					,      __a							=   recopy_model(snake_case__      ,				snake_case__      ,				snake_case__						)

										past_perps.append(past_perp.item()						)
										real_perps.append(orig_perp - real_perp.item()						)
										contexts.append(np.array(context.cpu()						)						)

					intermittent_save(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__						)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl"      ,				__lowerCamelCase=100      ,				__lowerCamelCase=1026      ,				__lowerCamelCase=True						):
					__a							=   joblib.load(snake_case__						)
					print('data loaded'						)
					__a							=   []
					if trim:
										for i, example in enumerate(snake_case__						):
															if len(example[0]						) > min_len:
																				__a							=   random.randint(0      ,				len(example[0]						) - context_len - 1						)
																				objective_set.append(example[0, start : start + context_len]						)
															if len(snake_case__						) >= number:
																				break
										__a							=   []
										for j in range(i + 1      ,				len(snake_case__						)						):
															if len(data[j][0]						) > min_len:
																				train_data.append(data[j]						)
					else:
										__a							=   data[0:number]
										__a							=   data[number:]

					joblib.dump(snake_case__      ,				'objective_set.jbl'						)
					print('objective set saved'						)
					return train_data, objective_set
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase=50      ,				__lowerCamelCase="secondary_learner.pt"						):
					__a							=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'						)
					# We will use the first 512 pairs from our dataset as a test set for
					# our secondary learner and the rest to train
					__a							=   train_dataset[:512]
					__a							=   train_dataset[512:]
					__a							=   DataLoader(snake_case__      ,				shuffle=snake_case__      ,				batch_size=snake_case__						)
					__a							=   DataLoader(snake_case__      ,				shuffle=snake_case__      ,				batch_size=snake_case__						)

					# secondary learner model set up
					__a							=   nn.MSELoss()
					__a							=   nn.MSELoss(reduction='sum'						)
					secondary_learner.to(snake_case__						)
					__a							=   torch.optim.Adam(secondary_learner.parameters()      ,				lr=0.0_00_01						)
					secondary_learner.train()

					# TODO in original code this is written as number of actual batches seen
					# not number of items seen but other places it is number of items instead.
					# improve consistency! changed this to epochs for clarity
					__a							=   float('inf'						)
					# Iterate through batches until we've used max_steps batches
					for epoch in range(int(snake_case__						)						):
										__a							=   0.0
										secondary_learner.train()
										for step, batch in enumerate(snake_case__						):
															__a							=   batch[0].to(snake_case__						)
															__a							=   batch[1].to(snake_case__						)
															__a							=   secondary_learner(snake_case__						)
															q_optimizer.zero_grad()
															__a							=   loss(snake_case__      ,				real_q.float()						)
															q_loss.backward()
															q_optimizer.step()
															tr_q_loss += q_loss.item()

															# model trains fairly quickly so we won't wait for a full epoch
															# eval is triggered at eval_freq and end of epochs
															if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(snake_case__						)):
																				__a							=   tr_q_loss / (step + 1)

																				secondary_learner.eval()
																				__a							=   0.0
																				__a							=   0.0
																				__a							=   []
																				__a							=   []
																				# Compute performance of the secondary learner after this batch
																				for stepa, batcha in enumerate(snake_case__						):
																									__a							=   batcha[0].to(snake_case__						)
																									__a							=   batcha[1].to(snake_case__						)
																									__a							=   secondary_learner(snake_case__						)
																									q_lossa += test_loss(snake_case__      ,				snake_case__						).item()
																									sum_qa += torch.sum(snake_case__						).item()
																									for ei, i in enumerate(predicted_qa.cpu().detach().numpy()						):
																														predicted.append(i.item()						)
																									for ei, i in enumerate(real_qa.cpu().detach().numpy()						):
																														actual.append(i.item()						)

																				q_lossa /= len(snake_case__						)
																				print(
																				    'Epoch: '      ,				snake_case__      ,				'step: '      ,				snake_case__      ,				'Avg. q:'      ,				sum_qa / len(snake_case__						)      ,				'Train Loss: '      ,				snake_case__      ,				'Test Loss: '      ,				snake_case__      ,				)
																				if q_lossa < best_test_loss:
																									joblib.dump((predicted, actual)      ,				'pred_vs_actual.jbl'						)
																									torch.save(secondary_learner.state_dict()      ,				snake_case__						)
																									__a							=   q_lossa

															secondary_learner.train()
					return secondary_learner



class 						a__							(   nn.Module		):



			def __init__(						self      ,						UpperCAmelCase    ) ->    List[str]:
								super(__a      ,						self    ).__init__()
								__a							=   model.transformer.wte
								__a							=   copy.deepcopy(model.transformer.wte.weight    )
								__a							=   nn.Convad(self.embeddings.weight.size(1    )      ,						2_5_6      ,						3      ,						padding=1    )
								__a							=   nn.Sequential(nn.Linear(2_5_6      ,						3_2    )      ,						nn.Dropout(p=0.1    )      ,						nn.Linear(3_2      ,						3_2    )      ,						nn.Linear(3_2      ,						1    )    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    Tuple:
								__a							=   torch.max(self.conv(self.embeddings(__a    ).squeeze(1    ).transpose(1      ,						2    )    )      ,						2    )[0]
								__a							=   self.fc(__a    )
								return qs.squeeze(1    )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								__a							=   cls(__a    )  # this calls __init__
								__a							=   torch.load(__a    )
								secondary_learner.load_state_dict(__a    )
								__a							=   model.transformer.wte
								__a							=   copy.deepcopy(model.transformer.wte.weight    )
								return secondary_learner







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 ) )



