code: 
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  				import numpy as np
  				import tensorflow as tf
  
  				from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
  				from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import MobileViTImageProcessor
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_class(**self.inputs_dict       )
  									self.parent.assertTrue(hasattr(lowerCamelCase__       ,     """hidden_sizes"""       )       )
  									self.parent.assertTrue(hasattr(lowerCamelCase__       ,     """neck_hidden_sizes"""       )       )
  									self.parent.assertTrue(hasattr(lowerCamelCase__       ,     """num_attention_heads"""       )       )
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=13       ,     lowerCamelCase__=32       ,     lowerCamelCase__=2       ,     lowerCamelCase__=3       ,     lowerCamelCase__=640       ,     lowerCamelCase__=4       ,     lowerCamelCase__="silu"       ,     lowerCamelCase__=3       ,     lowerCamelCase__=32       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=10       ,     lowerCamelCase__=None       ,     )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       image_size
  									lowercase__							 =       patch_size
  									lowercase__							 =       num_channels
  									lowercase__							 =       last_hidden_size
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       hidden_act
  									lowercase__							 =       conv_kernel_size
  									lowercase__							 =       output_stride
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       classifier_dropout_prob
  									lowercase__							 =       use_labels
  									lowercase__							 =       is_training
  									lowercase__							 =       num_labels
  									lowercase__							 =       initializer_range
  									lowercase__							 =       scope
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
  
  									lowercase__							 =       None
  									lowercase__							 =       None
  									if self.use_labels:
  															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.num_labels       )
  															lowercase__							 =       ids_tensor([self.batch_size, self.image_size, self.image_size]       ,     self.num_labels       )
  
  									lowercase__							 =       self.get_config()
  
  									return config, pixel_values, labels, pixel_labels
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return MobileViTConfig(
  									    image_size=self.image_size       ,     patch_size=self.patch_size       ,     num_channels=self.num_channels       ,     num_attention_heads=self.num_attention_heads       ,     hidden_act=self.hidden_act       ,     conv_kernel_size=self.conv_kernel_size       ,     output_stride=self.output_stride       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     classifier_dropout_prob=self.classifier_dropout_prob       ,     initializer_range=self.initializer_range       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMobileViTModel(config=lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     training=lowerCamelCase__       )
  									lowercase__							 =       lowercase__							 =       self.image_size // self.output_stride
  									self.parent.assertEqual(
  									    result.last_hidden_state.shape       ,     (self.batch_size, self.last_hidden_size, expected_height, expected_width)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       TFMobileViTForImageClassification(lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     labels=lowerCamelCase__       ,     training=lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       TFMobileViTForSemanticSegmentation(lowerCamelCase__       )
  									lowercase__							 =       lowercase__							 =       self.image_size // self.output_stride
  
  									lowercase__							 =       model(lowerCamelCase__       ,     training=lowerCamelCase__       )
  									self.parent.assertEqual(
  									    result.logits.shape       ,     (self.batch_size, self.num_labels, expected_height, expected_width)       )
  
  									lowercase__							 =       model(lowerCamelCase__       ,     labels=lowerCamelCase__       ,     training=lowerCamelCase__       )
  									self.parent.assertEqual(
  									    result.logits.shape       ,     (self.batch_size, self.num_labels, expected_height, expected_width)       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""pixel_values""": pixel_values}
  									return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Dict       		=  (
  			    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
  			    if is_tf_available()
  			    else ()
  			)
  			lowerCamelCase							:							Optional[int]       		=  (
  			    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
  			    if is_tf_available()
  			    else {}
  			)
  
  			lowerCamelCase							:							List[str]       		=  False
  			lowerCamelCase							:							List[Any]       		=  False
  			lowerCamelCase							:							Union[str, Any]       		=  False
  			lowerCamelCase							:							Optional[int]       		=  False
  			lowerCamelCase							:							Union[str, Any]       		=  False
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMobileViTModelTester(self       )
  									lowercase__							 =       TFMobileViTConfigTester(self       ,     config_class=lowerCamelCase__       ,     has_text_modality=lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			@unittest.skip(reason="""MobileViT does not use inputs_embeds"""       )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@unittest.skip(reason="""MobileViT does not support input and output embeddings"""       )
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@unittest.skip(reason="""MobileViT does not output attentions"""       )
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															lowercase__							 =       inspect.signature(model.call       )
  															# signature.parameters is an OrderedDict => so arg_names order is deterministic
  															lowercase__							 =       [*signature.parameters.keys()]
  
  															lowercase__							 =       ["""pixel_values"""]
  															self.assertListEqual(arg_names[:1]       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									def check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       model_class(lowerCamelCase__       )
  
  															lowercase__							 =       model(**self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       )       )
  
  															lowercase__							 =       outputs.hidden_states
  
  															lowercase__							 =       5
  															self.assertEqual(len(lowerCamelCase__       )       ,     lowerCamelCase__       )
  
  															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
  															# with the width and height being successively divided by 2.
  															lowercase__							 =       2
  															for i in range(len(lowerCamelCase__       )       ):
  																					self.assertListEqual(
  																					    list(hidden_states[i].shape[-2:]       )       ,     [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,     )
  																					divisor *= 2
  
  															self.assertEqual(self.model_tester.output_stride       ,     divisor // 2       )
  
  									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(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  															# check that output_hidden_states also work using config
  															del inputs_dict["output_hidden_states"]
  															lowercase__							 =       True
  
  															check_hidden_states_output(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )
  
  
  
  			@unittest.skipIf(
  			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0       ,     reason="""TF does not support backprop for grouped convolutions on CPU."""       ,     )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().test_dataset_conversion()
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__=2e-1       ,     lowerCamelCase__=2e-1       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.assertTrue(np.allclose(lowerCamelCase__       ,     lowerCamelCase__       ,     atol=lowerCamelCase__       ,     rtol=lowerCamelCase__       )       )
  
  
  
  			@unittest.skipIf(
  			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0       ,     reason="""TF does not support backprop for grouped convolutions on CPU."""       ,     )
  			@slow
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
  															if model_class.__name__ != "TFMobileViTModel":
  																					lowercase__							 =       model_class(lowerCamelCase__       )
  																					if getattr(lowerCamelCase__       ,     """hf_compute_loss"""       ,     lowerCamelCase__       ):
  																											super().test_keras_fit()
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  									for model_class in self.all_model_classes:
  															# set an ignore index to correctly test the masked loss used in
  															# `TFMobileViTForSemanticSegmentation`.
  															if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
  																					lowercase__							 =       5
  
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															if getattr(lowerCamelCase__       ,     """hf_compute_loss"""       ,     lowerCamelCase__       ):
  																					# The number of elements in the loss should be the same as the number of elements in the label
  																					lowercase__							 =       self._prepare_for_class(inputs_dict.copy()       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  																					lowercase__							 =       prepared_for_class[
  																					    sorted(prepared_for_class.keys() - inputs_dict.keys()       ,     reverse=lowerCamelCase__       )[0]
  																					]
  																					lowercase__							 =       added_label.shape.as_list()[:1]
  
  																					# Test that model correctly compute the loss with kwargs
  																					lowercase__							 =       self._prepare_for_class(inputs_dict.copy()       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  																					lowercase__							 =       {"""input_ids""", """pixel_values""", """input_features"""}
  																					lowercase__							 =       possible_input_names.intersection(set(lowerCamelCase__       )       ).pop()
  																					lowercase__							 =       prepared_for_class.pop(lowerCamelCase__       )
  
  																					lowercase__							 =       model(lowerCamelCase__       ,     **lowerCamelCase__       )[0]
  																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
  
  																					# Test that model correctly compute the loss when we mask some positions
  																					lowercase__							 =       self._prepare_for_class(inputs_dict.copy()       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  																					lowercase__							 =       {"""input_ids""", """pixel_values""", """input_features"""}
  																					lowercase__							 =       possible_input_names.intersection(set(lowerCamelCase__       )       ).pop()
  																					lowercase__							 =       prepared_for_class.pop(lowerCamelCase__       )
  																					if "labels" in prepared_for_class:
  																											lowercase__							 =       prepared_for_class["""labels"""].numpy()
  																											if len(labels.shape       ) > 1 and labels.shape[1] != 1:
  																																	# labels[0] = -100
  																																	lowercase__							 =       tf.convert_to_tensor(lowerCamelCase__       )
  																																	lowercase__							 =       model(lowerCamelCase__       ,     **lowerCamelCase__       )[0]
  																																	self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
  																																	self.assertTrue(not np.any(np.isnan(loss.numpy()       )       )       )
  
                  # Test that model correctly compute the loss with a dict
  																					lowercase__							 =       self._prepare_for_class(inputs_dict.copy()       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  																					lowercase__							 =       model(lowerCamelCase__       )[0]
  																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
  
  																					# Test that model correctly compute the loss with a tuple
  																					lowercase__							 =       self._prepare_for_class(inputs_dict.copy()       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  
  																					# Get keys that were added with the _prepare_for_class function
  																					lowercase__							 =       prepared_for_class.keys() - inputs_dict.keys()
  																					lowercase__							 =       inspect.signature(model.call       ).parameters
  																					lowercase__							 =       list(signature.keys()       )
  
  																					# Create a dictionary holding the location of the tensors in the tuple
  																					lowercase__							 =       {0: input_name}
  																					for label_key in label_keys:
  																											lowercase__							 =       signature_names.index(lowerCamelCase__       )
  																											lowercase__							 =       label_key
  																					lowercase__							 =       sorted(tuple_index_mapping.items()       )
  																					# Initialize a list with their default values, update the values and convert to a tuple
  																					lowercase__							 =       []
  
  																					for name in signature_names:
  																											if name != "kwargs":
  																																	list_input.append(signature[name].default       )
  
  																					for index, value in sorted_tuple_index_mapping:
  																											lowercase__							 =       prepared_for_class[value]
  
  																					lowercase__							 =       tuple(lowerCamelCase__       )
  
  																					# Send to model
  																					lowercase__							 =       model(tuple_input[:-1]       )[0]
  
  																					self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]       )
  
  
  
  			@slow
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															lowercase__							 =       TFMobileViTModel.from_pretrained(lowerCamelCase__       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  
  
  def  _A    (							):
  						lowercase__							 =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  						return image
  
  
  
  
  
  
  
  @require_tf
  class 			A							(       unittest.TestCase				):
  
  
  
  			@slow
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""       )
  
  									lowercase__							 =       MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""       )
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""tf"""       )
  
  									# forward pass
  									lowercase__							 =       model(**lowerCamelCase__       ,     training=lowerCamelCase__       )
  
  									# verify the logits
  									lowercase__							 =       tf.TensorShape((1, 1_000)       )
  									self.assertEqual(outputs.logits.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       tf.constant([-1.93_64, -1.23_27, -0.46_53]       )
  
  									tf.debugging.assert_near(outputs.logits[0, :3]       ,     lowerCamelCase__       ,     atol=1e-4       ,     rtol=1e-04       )
  
  
  
  			@slow
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )
  
  									lowercase__							 =       MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""tf"""       )
  
  									# forward pass
  									lowercase__							 =       model(inputs.pixel_values       ,     training=lowerCamelCase__       )
  									lowercase__							 =       outputs.logits
  
  									# verify the logits
  									lowercase__							 =       tf.TensorShape((1, 21, 32, 32)       )
  									self.assertEqual(logits.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       tf.constant(
  									    [
  									        [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
  									        [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
  									        [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
  									    ]       )
  
  									tf.debugging.assert_near(logits[0, :3, :3, :3]       ,     lowerCamelCase__       ,     rtol=1e-4       ,     atol=1e-4       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  __A          =    torch.cuda.is_available()
  __A          =    [" Sam ate lunch today.", "Sams lunch ingredients."]
  __A          =    ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __A          =    {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       """\n""".join(lowercase__	)
  						Path(lowercase__	).open("""w"""	).writelines(lowercase__	)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						for split in ["train", "val", "test"]:
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.source'''	)		,		lowercase__	)
  												_dump_articles(os.path.join(lowercase__		,		f'''{split}.target'''	)		,		lowercase__	)
  						return tmp_dir
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls
  
  
  
  			@require_torch_multi_gpu
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """no_teacher""": True,
  									    """freeze_encoder""": True,
  									    """gpus""": 2,
  									    """overwrite_output_dir""": True,
  									    """sortish_sampler""": True,
  									}
  									self._test_distiller_cli_fork(lowerCamelCase__       ,     check_contents=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__=True       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """label_smoothing""": 0.0,
  									    """early_stopping_patience""": -1,
  									    """train_batch_size""": 1,
  									    """eval_batch_size""": 2,
  									    """max_epochs""": 2,
  									    """alpha_mlm""": 0.2,
  									    """alpha_ce""": 0.8,
  									    """do_predict""": True,
  									    """model_name_or_path""": """sshleifer/tinier_bart""",
  									    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
  									    """val_check_interval""": 0.5,
  									}
  									default_updates.update(lowerCamelCase__       )
  									lowercase__							 =       CHEAP_ARGS.copy()
  									lowercase__							 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
  									lowercase__							 =       self.get_auto_remove_tmp_dir()
  									args_d.update(data_dir=lowerCamelCase__       ,     output_dir=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									def convert(lowerCamelCase__       ,     lowerCamelCase__       ):
  															if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
  																					return ""
  															if v is False or v is None:
  																					return ""
  															if v is True:  # or len(str(v))==0:
  																					return F'''--{k}'''
  															return F'''--{k}={v}'''
  
  									lowercase__							 =       [x for x in (convert(lowerCamelCase__       ,     lowerCamelCase__       ) for k, v in args_d.items()) if len(lowerCamelCase__       )]
  									lowercase__							 =       [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
  									execute_subprocess_async(lowerCamelCase__       ,     env=self.get_env()       )
  
  									lowercase__							 =       os.listdir(lowerCamelCase__       )
  									lowercase__							 =       {os.path.basename(lowerCamelCase__       ) for p in contents}
  									lowercase__							 =       [p for p in contents if p.endswith("""ckpt"""       )]
  									assert len(lowerCamelCase__       ) > 0
  
  									self.assertIn("""test_generations.txt"""       ,     lowerCamelCase__       )
  									self.assertIn("""test_results.txt"""       ,     lowerCamelCase__       )
  
  									# get the following from the module, (we don't have access to `model` here)
  									lowercase__							 =       os.path.join(lowerCamelCase__       ,     """metrics.json"""       )
  									lowercase__							 =       """rouge2"""
  
  									lowercase__							 =       load_json(lowerCamelCase__       )
  									# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
  									print(lowerCamelCase__       )
  									lowercase__							 =       metrics["""val"""][-1]
  									self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]       ,     0.01       )
  									self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']       ,     lowerCamelCase__       )
  									self.assertEqual(len(metrics["""test"""]       )       ,     1       )
  									lowercase__							 =       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1       )
  									self.assertEqual(len(metrics["""val"""]       )       ,     lowerCamelCase__       )
  
  
  
  
  
  
  