"""simple docstring"""


import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import YolosForObjectDetection, YolosModel
 from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class    a__    :


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase=13,				_UpperCAmelCase=[30, 30],				_UpperCAmelCase=2,				_UpperCAmelCase=3,				_UpperCAmelCase=True,				_UpperCAmelCase=True,				_UpperCAmelCase=32,				_UpperCAmelCase=5,				_UpperCAmelCase=4,				_UpperCAmelCase=37,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=10,				_UpperCAmelCase=0.02,				_UpperCAmelCase=3,				_UpperCAmelCase=None,				_UpperCAmelCase=8,				_UpperCAmelCase=10,				):
        '''simple docstring'''



        lowercase__				 =							parent
        lowercase__				 =							batch_size
        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							is_training
        lowercase__				 =							use_labels
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							type_sequence_label_size
        lowercase__				 =							initializer_range
        lowercase__				 =							num_labels
        lowercase__				 =							scope
        lowercase__				 =							n_targets
        lowercase__				 =							num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        lowercase__				 =							(image_size[1] // patch_size) * (image_size[0] // patch_size)
        lowercase__				 =							num_patches + 1 + self.num_detection_tokens


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]  )

        lowercase__				 =							None
        if self.use_labels:
             # labels is a list of Dict (each Dict being the labels for a given example in the batch)
             lowercase__				 =							[]
             for i in range(self.batch_size  ):
                  lowercase__				 =							{}
                  lowercase__				 =							torch.randint(
                      high=self.num_labels,				size=(self.n_targets,),				device=_UpperCAmelCase  )
                  lowercase__				 =							torch.rand(self.n_targets,				4,				device=_UpperCAmelCase  )
                  labels.append(_UpperCAmelCase  )

        lowercase__				 =							self.get_config()

        return config, pixel_values, labels


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return YolosConfig(
            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=_UpperCAmelCase,				initializer_range=self.initializer_range,				num_detection_tokens=self.num_detection_tokens,				num_labels=self.num_labels,				)


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							YolosModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowercase__				 =							model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.last_hidden_state.shape,				(self.batch_size, self.expected_seq_len, self.hidden_size)  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							YolosForObjectDetection(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()

        lowercase__				 =							model(pixel_values=_UpperCAmelCase  )
        lowercase__				 =							model(_UpperCAmelCase  )

        self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
        self.parent.assertEqual(result.pred_boxes.shape,				(self.batch_size, self.num_detection_tokens, 4)  )

        lowercase__				 =							model(pixel_values=_UpperCAmelCase,				labels=_UpperCAmelCase  )

        self.parent.assertEqual(result.loss.shape,				()  )
        self.parent.assertEqual(result.logits.shape,				(self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
        self.parent.assertEqual(result.pred_boxes.shape,				(self.batch_size, self.num_detection_tokens, 4)  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.prepare_config_and_inputs()
        lowercase__ ,						lowercase__ ,						lowercase__				 =							config_and_inputs
        lowercase__				 =							{"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class    a__    (   _a					,					_a					,					unittest.TestCase	):
   snake_case_							=      (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
   snake_case_							=      (
       {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
   )

   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      False


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=False  ):
        '''simple docstring'''



        lowercase__				 =							super()._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase,				return_labels=_UpperCAmelCase  )

        if return_labels:
             if model_class.__name__ == "YolosForObjectDetection":
                  lowercase__				 =							[]
                  for i in range(self.model_tester.batch_size  ):
                       lowercase__				 =							{}
                       lowercase__				 =							torch.ones(
                           size=(self.model_tester.n_targets,),				device=_UpperCAmelCase,				dtype=torch.long  )
                       lowercase__				 =							torch.ones(
                           self.model_tester.n_targets,				4,				device=_UpperCAmelCase,				dtype=torch.float  )
                       labels.append(_UpperCAmelCase  )
                  lowercase__				 =							labels

        return inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							YolosModelTester(self  )
        lowercase__				 =							ConfigTester(self,				config_class=_UpperCAmelCase,				has_text_modality=_UpperCAmelCase,				hidden_size=37  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        self.config_tester.run_common_tests()


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase__				 =							model_class(_UpperCAmelCase  )
             self.assertIsInstance(model.get_input_embeddings(),				(nn.Module)  )
             lowercase__				 =							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase,				nn.Linear  )  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase__				 =							["pixel_values"]
             self.assertListEqual(arg_names[:1],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

        # in YOLOS, the seq_len is different
        lowercase__				 =							self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
             lowercase__				 =							True
             lowercase__				 =							False
             lowercase__				 =							True
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                  lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )
             lowercase__				 =							outputs.attentions
             self.assertEqual(len(_UpperCAmelCase  ),				self.model_tester.num_hidden_layers  )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             lowercase__				 =							True
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                  lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )
             lowercase__				 =							outputs.attentions
             self.assertEqual(len(_UpperCAmelCase  ),				self.model_tester.num_hidden_layers  )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]  ),				[self.model_tester.num_attention_heads, seq_len, seq_len],				)
             lowercase__				 =							len(_UpperCAmelCase  )

             # Check attention is always last and order is fine
             lowercase__				 =							True
             lowercase__				 =							True
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                  lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )

             lowercase__				 =							1
             self.assertEqual(out_len + added_hidden_states,				len(_UpperCAmelCase  )  )

             lowercase__				 =							outputs.attentions

             self.assertEqual(len(_UpperCAmelCase  ),				self.model_tester.num_hidden_layers  )
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]  ),				[self.model_tester.num_attention_heads, seq_len, seq_len],				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        def check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
             lowercase__				 =							model_class(_UpperCAmelCase  )
             model.to(_UpperCAmelCase  )
             model.eval()

             with torch.no_grad():
                  lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )

             lowercase__				 =							outputs.hidden_states

             lowercase__				 =							getattr(
                 self.model_tester,				"expected_num_hidden_layers",				self.model_tester.num_hidden_layers + 1  )
             self.assertEqual(len(_UpperCAmelCase  ),				_UpperCAmelCase  )

             # YOLOS has a different seq_length
             lowercase__				 =							self.model_tester.expected_seq_len

             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]  ),				[seq_length, self.model_tester.hidden_size],				)

        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase__				 =							True
             check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )

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

             check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase  )


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase__				 =							YolosModel.from_pretrained(_UpperCAmelCase  )
             self.assertIsNotNone(_UpperCAmelCase  )







def 					__a   (		):






     '''simple docstring'''



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




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


   @cached_property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return AutoImageProcessor.from_pretrained("hustvl/yolos-small"  ) if is_vision_available() else None


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							YolosForObjectDetection.from_pretrained("hustvl/yolos-small"  ).to(_UpperCAmelCase  )

        lowercase__				 =							self.default_image_processor
        lowercase__				 =							prepare_img()
        lowercase__				 =							image_processor(images=_UpperCAmelCase,				return_tensors="pt"  ).to(_UpperCAmelCase  )

        # forward pass
        with torch.no_grad():
             lowercase__				 =							model(inputs.pixel_values  )

        # verify outputs
        lowercase__				 =							torch.Size((1, 100, 92)  )
        self.assertEqual(outputs.logits.shape,				_UpperCAmelCase  )

        lowercase__				 =							torch.tensor(
            [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]],				device=_UpperCAmelCase,				)
        lowercase__				 =							torch.tensor(
            [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]],				device=_UpperCAmelCase  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],				_UpperCAmelCase,				atol=1E-4  )  )
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3],				_UpperCAmelCase,				atol=1E-4  )  )

        # verify postprocessing
        lowercase__				 =							image_processor.post_process_object_detection(
            _UpperCAmelCase,				threshold=0.3,				target_sizes=[image.size[::-1]]  )[0]
        lowercase__				 =							torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]  ).to(_UpperCAmelCase  )
        lowercase__				 =							[75, 75, 17, 63, 17]
        lowercase__				 =							torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]  ).to(_UpperCAmelCase  )

        self.assertEqual(len(results["scores"]  ),				5  )
        self.assertTrue(torch.allclose(results["scores"],				_UpperCAmelCase,				atol=1E-4  )  )
        self.assertSequenceEqual(results["labels"].tolist(),				_UpperCAmelCase  )
        self.assertTrue(torch.allclose(results["boxes"][0, :],				_UpperCAmelCase  )  )





"""simple docstring"""


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase_:					List[str]     =					1_6
lowerCAmelCase_:					Optional[Any]     =					3_2







def 					__a   (		A    ,				A = 16    ,				A = "bert-base-cased"      ):






     '''simple docstring'''



     lowercase__				 =							AutoTokenizer.from_pretrained(A      )
     lowercase__				 =							load_dataset("glue"    ,				"mrpc"      )

     def tokenize_function(A      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase__				 =							tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=A    ,				max_length=A      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     lowercase__				 =							datasets.map(
         A    ,				batched=A    ,				remove_columns=["idx", "sentence1", "sentence2"]    ,				load_from_cache_file=A      )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowercase__				 =							tokenized_datasets.rename_column("label"    ,				"labels"      )

     def collate_fn(A      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(A    ,				padding="max_length"    ,				max_length=1_28    ,				return_tensors="pt"      )
          return tokenizer.pad(A    ,				padding="longest"    ,				return_tensors="pt"      )

     # Instantiate dataloaders.
     lowercase__				 =							DataLoader(
         tokenized_datasets["train"]    ,				shuffle=A    ,				collate_fn=A    ,				batch_size=A      )
     lowercase__				 =							DataLoader(
         tokenized_datasets["validation"]    ,				shuffle=A    ,				collate_fn=A    ,				batch_size=A      )

     return train_dataloader, eval_dataloader







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase__				 =							config["lr"]
     lowercase__				 =							int(config["num_epochs"]      )
     lowercase__				 =							int(config["seed"]      )
     lowercase__				 =							int(config["batch_size"]      )
     lowercase__				 =							args.model_name_or_path

     set_seed(A      )
     lowercase__ ,						lowercase__				 =							get_dataloaders(A    ,				A    ,				A      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase__				 =							AutoModelForSequenceClassification.from_pretrained(A    ,				return_dict=A      )

     # Instantiate optimizer
     lowercase__				 =							(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase__				 =							optimizer_cls(params=model.parameters()    ,				lr=A      )

     if accelerator.state.deepspeed_plugin is not None:
          lowercase__				 =							accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          lowercase__				 =							1
     lowercase__				 =							(len(A      ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowercase__				 =							get_linear_schedule_with_warmup(
              optimizer=A    ,				num_warmup_steps=0    ,				num_training_steps=A    ,				)
     else:
          lowercase__				 =							DummyScheduler(A    ,				total_num_steps=A    ,				warmup_num_steps=0      )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__				 =							accelerator.prepare(
         A    ,				A    ,				A    ,				A    ,				A      )

     # We need to keep track of how many total steps we have iterated over
     lowercase__				 =							0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase__				 =							0

     # Now we train the model
     lowercase__				 =							evaluate.load("glue"    ,				"mrpc"      )
     lowercase__				 =							0
     lowercase__				 =							{}
     for epoch in range(A    ,				A      ):
          model.train()
          for step, batch in enumerate(A      ):
               lowercase__				 =							model(**A      )
               lowercase__				 =							outputs.loss
               lowercase__				 =							loss / gradient_accumulation_steps
               accelerator.backward(A      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          lowercase__				 =							0
          for step, batch in enumerate(A      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    lowercase__				 =							model(**A      )
               lowercase__				 =							outputs.logits.argmax(dim=-1      )
               # It is slightly faster to call this once, than multiple times
               lowercase__ ,						lowercase__				 =							accelerator.gather(
                   (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(A      ) - 1:
                         lowercase__				 =							predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                         lowercase__				 =							references[: len(eval_dataloader.dataset      ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=A    ,				references=A    ,				)

          lowercase__				 =							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''    ,				A      )
          lowercase__				 =							eval_metric["accuracy"]

          if best_performance < eval_metric["accuracy"]:
               lowercase__				 =							eval_metric["accuracy"]

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir    ,				"all_results.json"      )    ,				"w"      ) as f:
               json.dump(A    ,				A      )







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
     parser.add_argument(
         "--model_name_or_path"    ,				type=A    ,				default="bert-base-cased"    ,				help="Path to pretrained model or model identifier from huggingface.co/models."    ,				required=A    ,				)
     parser.add_argument(
         "--output_dir"    ,				type=A    ,				default="."    ,				help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,				)
     parser.add_argument(
         "--performance_lower_bound"    ,				type=A    ,				default=A    ,				help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."    ,				)
     parser.add_argument(
         "--num_epochs"    ,				type=A    ,				default=3    ,				help="Number of train epochs."    ,				)
     lowercase__				 =							parser.parse_args()
     lowercase__				 =							{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(A    ,				A      )


if __name__ == "__main__":
 main()




