



import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.config_class(**self.inputs_dict    )
           self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			"tf_padding"    )    )
           self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE				,			"depth_multiplier"    )    )





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a				,			__a=13				,			__a=3				,			__a=32				,			__a=0.25				,			__a=8				,			__a=True				,			__a=10_24				,			__a=32				,			__a="relu6"				,			__a=0.1				,			__a=0.02				,			__a=True				,			__a=True				,			__a=10				,			__a=None				,			):






           '''simple docstring'''



           lowerCamelCase								=		parent
           lowerCamelCase								=		batch_size
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		image_size
           lowerCamelCase								=		depth_multiplier
           lowerCamelCase								=		min_depth
           lowerCamelCase								=		tf_padding
           lowerCamelCase								=		int(last_hidden_size * depth_multiplier    )
           lowerCamelCase								=		output_stride
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		classifier_dropout_prob
           lowerCamelCase								=		use_labels
           lowerCamelCase								=		is_training
           lowerCamelCase								=		num_labels
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		scope







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

           lowerCamelCase								=		None
           lowerCamelCase								=		None
           if self.use_labels:
                 lowerCamelCase								=		ids_tensor([self.batch_size]				,			self.num_labels    )
                 lowerCamelCase								=		ids_tensor([self.batch_size, self.image_size, self.image_size]				,			self.num_labels    )

           lowerCamelCase								=		self.get_config()

           return config, pixel_values, labels, pixel_labels







     def 						_a      (self    ):






           '''simple docstring'''



           return MobileNetVaConfig(
               num_channels=self.num_channels				,			image_size=self.image_size				,			depth_multiplier=self.depth_multiplier				,			min_depth=self.min_depth				,			tf_padding=self.tf_padding				,			hidden_act=self.hidden_act				,			classifier_dropout_prob=self.classifier_dropout_prob				,			initializer_range=self.initializer_range				,			)







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		MobileNetVaModel(config=_SCREAMING_SNAKE_CASE    )
           model.to(_SCREAMING_SNAKE_CASE    )
           model.eval()
           lowerCamelCase								=		model(_SCREAMING_SNAKE_CASE    )
           self.parent.assertEqual(
               result.last_hidden_state.shape				,			(
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )				,			)







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.num_labels
           lowerCamelCase								=		MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE    )
           model.to(_SCREAMING_SNAKE_CASE    )
           model.eval()
           lowerCamelCase								=		model(_SCREAMING_SNAKE_CASE				,			labels=_SCREAMING_SNAKE_CASE    )
           self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)    )







     def 						_a      (self    ):






           '''simple docstring'''



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





@require_torch
class    lowerCamelCase__  (						UpperCAmelCase_       ,    UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
     _A       =			(
         {'feature-extraction': MobileNetVaModel, 'image-classification': MobileNetVaForImageClassification}
         if is_torch_available()
         else {}
     )

     _A       =			False
     _A       =			False
     _A       =			False
     _A       =			False







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		MobileNetVaModelTester(self    )
           lowerCamelCase								=		MobileNetVaConfigTester(self				,			config_class=_SCREAMING_SNAKE_CASE				,			has_text_modality=_SCREAMING_SNAKE_CASE    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     @unittest.skip(reason="MobileNetV1 does not use inputs_embeds"    )
     def 						_a      (self    ):






           '''simple docstring'''



           pass







     @unittest.skip(reason="MobileNetV1 does not support input and output embeddings"    )
     def 						_a      (self    ):






           '''simple docstring'''



           pass







     @unittest.skip(reason="MobileNetV1 does not output attentions"    )
     def 						_a      (self    ):






           '''simple docstring'''



           pass







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase			,							lowerCamelCase								=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase								=		["pixel_values"]
                 self.assertListEqual(arg_names[:1]				,			_SCREAMING_SNAKE_CASE    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )







     def 						_a      (self    ):






           '''simple docstring'''



           def check_hidden_states_output(__a				,			__a				,			__a    ):
                 lowerCamelCase								=		model_class(_SCREAMING_SNAKE_CASE    )
                 model.to(_SCREAMING_SNAKE_CASE    )
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase								=		model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE    )    )

                 lowerCamelCase								=		outputs.hidden_states

                 lowerCamelCase								=		26
                 self.assertEqual(len(_SCREAMING_SNAKE_CASE    )				,			_SCREAMING_SNAKE_CASE    )

           lowerCamelCase			,							lowerCamelCase								=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase								=		True
                 check_hidden_states_output(_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE    )

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

                 check_hidden_states_output(_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE				,			_SCREAMING_SNAKE_CASE    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE    )







     @slow
     def 						_a      (self    ):






           '''simple docstring'''



           for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase								=		MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
                 self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )



def     __lowercase(	):


      """simple docstring"""



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






@require_torch
@require_vision
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     @cached_property
     def 						_a      (self    ):






           '''simple docstring'''



           return (
               MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"    ) if is_vision_available() else None
           )







     @slow
     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"    ).to(_SCREAMING_SNAKE_CASE    )

           lowerCamelCase								=		self.default_image_processor
           lowerCamelCase								=		prepare_img()
           lowerCamelCase								=		image_processor(images=_SCREAMING_SNAKE_CASE				,			return_tensors="pt"    ).to(_SCREAMING_SNAKE_CASE    )

           # forward pass
           with torch.no_grad():
                 lowerCamelCase								=		model(**_SCREAMING_SNAKE_CASE    )

           # verify the logits
           lowerCamelCase								=		torch.Size((1, 10_01)    )
           self.assertEqual(outputs.logits.shape				,			_SCREAMING_SNAKE_CASE    )

           lowerCamelCase								=		torch.tensor([-4.1739, -1.1233, 3.1205]    ).to(_SCREAMING_SNAKE_CASE    )

           self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			_SCREAMING_SNAKE_CASE				,			atol=1E-4    )    )




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


a_						:     Any	     =		1_6
a_						:     Any	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16   ,   UpperCAmelCase__ = "bert-base-cased" ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

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

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

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

      def collate_fn(UpperCAmelCase__ ):
            # 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(UpperCAmelCase__   ,   padding="max_length"   ,   max_length=128   ,   return_tensors="pt" )
            return tokenizer.pad(UpperCAmelCase__   ,   padding="longest"   ,   return_tensors="pt" )

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

      return train_dataloader, eval_dataloader

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		Accelerator()

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

      set_seed(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

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

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

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

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

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

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

                  overall_step += 1

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

            lowerCamelCase								=		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""   ,   UpperCAmelCase__ )
            lowerCamelCase								=		eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase								=		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(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
      parser.add_argument(
          "--model_name_or_path"   ,   type=UpperCAmelCase__   ,   default="bert-base-cased"   ,   help="Path to pretrained model or model identifier from huggingface.co/models."   ,   required=UpperCAmelCase__   ,   )
      parser.add_argument(
          "--output_dir"   ,   type=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   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=UpperCAmelCase__   ,   default=3   ,   help="Number of train epochs."   ,   )
      lowerCamelCase								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()