




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
	from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DPTImageProcessor


class       UpperCAmelCase__							:



	"""simple docstring"""


	def __init__(				self		:	Tuple				,    lowerCAmelCase		:	Tuple				,    lowerCAmelCase		:	List[str]=2				,    lowerCAmelCase		:	int=32				,    lowerCAmelCase		:	Union[str, Any]=16				,    lowerCAmelCase		:	str=3				,    lowerCAmelCase		:	Tuple=True				,    lowerCAmelCase		:	Tuple=True				,    lowerCAmelCase		:	int=32				,    lowerCAmelCase		:	Optional[Any]=4				,    lowerCAmelCase		:	Optional[Any]=[0, 1, 2, 3]				,    lowerCAmelCase		:	Optional[Any]=4				,    lowerCAmelCase		:	Any=37				,    lowerCAmelCase		:	List[Any]="gelu"				,    lowerCAmelCase		:	Dict=0.1				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	Optional[int]=0.02				,    lowerCAmelCase		:	List[Any]=3				,    lowerCAmelCase		:	List[str]=False				,    lowerCAmelCase		:	Any=None				,    )  ->					Tuple:







						"""simple docstring"""







						__UpperCamelCase				:  Any								=			parent
						__UpperCamelCase				:  Dict								=			batch_size
						__UpperCamelCase				:  List[str]								=			image_size
						__UpperCamelCase				:  List[str]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			num_channels
						__UpperCamelCase				:  List[str]								=			is_training
						__UpperCamelCase				:  List[str]								=			use_labels
						__UpperCamelCase				:  Union[str, Any]								=			hidden_size
						__UpperCamelCase				:  Optional[int]								=			num_hidden_layers
						__UpperCamelCase				:  Optional[int]								=			backbone_out_indices
						__UpperCamelCase				:  Optional[int]								=			num_attention_heads
						__UpperCamelCase				:  int								=			intermediate_size
						__UpperCamelCase				:  Optional[int]								=			hidden_act
						__UpperCamelCase				:  Any								=			hidden_dropout_prob
						__UpperCamelCase				:  int								=			attention_probs_dropout_prob
						__UpperCamelCase				:  int								=			initializer_range
						__UpperCamelCase				:  Any								=			num_labels
						__UpperCamelCase				:  int								=			scope
						__UpperCamelCase				:  int								=			is_hybrid
						# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
						__UpperCamelCase				:  int								=			(image_size // patch_size) ** 2
						__UpperCamelCase				:  Tuple								=			num_patches + 1






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					List[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						__UpperCamelCase				:  Optional[Any]								=			None
						if self.use_labels:
											__UpperCamelCase				:  int								=			ids_tensor([self.batch_size, self.image_size, self.image_size]				,    self.num_labels  )

						__UpperCamelCase				:  Tuple								=			self.get_config()

						return config, pixel_values, labels






	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Any:







						"""simple docstring"""







						return DPTConfig(
						    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				,    backbone_out_indices=self.backbone_out_indices				,    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=__snake_case				,    initializer_range=self.initializer_range				,    is_hybrid=self.is_hybrid				,    )






	def 				lowerCamelCase__    (				self		:	Dict				,    lowerCAmelCase		:	List[Any]				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Any  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  Union[str, Any]								=			DPTModel(config=__snake_case  )
						model.to(__snake_case  )
						model.eval()
						__UpperCamelCase				:  Union[str, Any]								=			model(__snake_case  )
						self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)  )






	def 				lowerCamelCase__    (				self		:	Optional[Any]				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Any				,    lowerCAmelCase		:	int  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Dict								=			self.num_labels
						__UpperCamelCase				:  Tuple								=			DPTForDepthEstimation(__snake_case  )
						model.to(__snake_case  )
						model.eval()
						__UpperCamelCase				:  Union[str, Any]								=			model(__snake_case  )
						self.parent.assertEqual(result.predicted_depth.shape				,    (self.batch_size, self.image_size, self.image_size)  )






	def 				lowerCamelCase__    (				self		:	Union[str, Any]				,    lowerCAmelCase		:	List[Any]				,    lowerCAmelCase		:	List[str]				,    lowerCAmelCase		:	List[str]  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			self.num_labels
						__UpperCamelCase				:  Any								=			DPTForSemanticSegmentation(__snake_case  )
						model.to(__snake_case  )
						model.eval()
						__UpperCamelCase				:  Optional[Any]								=			model(__snake_case				,    labels=__snake_case  )
						self.parent.assertEqual(
						    result.logits.shape				,    (self.batch_size, self.num_labels, self.image_size, self.image_size)  )






	def 				lowerCamelCase__    (				self		:	Union[str, Any]  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  str								=			self.prepare_config_and_inputs()
						__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  List[str]								=			config_and_inputs
						__UpperCamelCase				:  Optional[Any]								=			{"""pixel_values""": pixel_values}
						return config, inputs_dict






@require_torch
class       UpperCAmelCase__							(					snake_case__  ,     snake_case__  ,     unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					Dict			  =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	__magic_name__      :					Any			  =		(
	    {
	        'depth-estimation': DPTForDepthEstimation,
	        'feature-extraction': DPTModel,
	        'image-segmentation': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__      :					Tuple			  =		False
	__magic_name__      :					int			  =		False
	__magic_name__      :					Tuple			  =		False






	def 				lowerCamelCase__    (				self		:	int  )  ->					List[Any]:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			DPTModelTester(self  )
						__UpperCamelCase				:  Optional[int]								=			ConfigTester(self				,    config_class=__snake_case				,    has_text_modality=__snake_case				,    hidden_size=37  )






	def 				lowerCamelCase__    (				self		:	Any  )  ->					str:







						"""simple docstring"""







						self.config_tester.run_common_tests()






	@unittest.skip(reason="""DPT does not use inputs_embeds"""  )
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					int:







						"""simple docstring"""







						pass






	def 				lowerCamelCase__    (				self		:	Dict  )  ->					List[Any]:







						"""simple docstring"""







						__UpperCamelCase			, __UpperCamelCase				:  Dict								=			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__UpperCamelCase				:  int								=			model_class(__snake_case  )
											self.assertIsInstance(model.get_input_embeddings()				,    (nn.Module)  )
											__UpperCamelCase				:  Union[str, Any]								=			model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__snake_case				,    nn.Linear  )  )






	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase			, __UpperCamelCase				:  Union[str, Any]								=			self.model_tester.prepare_config_and_inputs_for_common()

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

											__UpperCamelCase				:  Tuple								=			["""pixel_values"""]
											self.assertListEqual(arg_names[:1]				,    __snake_case  )






	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  str								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case  )






	def 				lowerCamelCase__    (				self		:	int  )  ->					str:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*__snake_case  )






	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					List[str]:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[int]								=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__snake_case  )






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					Tuple:







						"""simple docstring"""







						for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
																continue

											__UpperCamelCase			, __UpperCamelCase				:  List[str]								=			self.model_tester.prepare_config_and_inputs_for_common()
											__UpperCamelCase				:  Optional[int]								=			True

											if model_class in get_values(__snake_case  ):
																continue

											__UpperCamelCase				:  Optional[Any]								=			model_class(__snake_case  )
											model.to(__snake_case  )
											model.train()
											__UpperCamelCase				:  Tuple								=			self._prepare_for_class(__snake_case				,    __snake_case				,    return_labels=__snake_case  )
											__UpperCamelCase				:  Dict								=			model(**__snake_case  ).loss
											loss.backward()






	def 				lowerCamelCase__    (				self		:	Union[str, Any]  )  ->					Tuple:







						"""simple docstring"""







						for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
																continue

											__UpperCamelCase			, __UpperCamelCase				:  List[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()
											__UpperCamelCase				:  Optional[Any]								=			False
											__UpperCamelCase				:  Optional[Any]								=			True

											if model_class in get_values(__snake_case  ) or not model_class.supports_gradient_checkpointing:
																continue
											__UpperCamelCase				:  int								=			model_class(__snake_case  )
											model.to(__snake_case  )
											model.gradient_checkpointing_enable()
											model.train()
											__UpperCamelCase				:  Any								=			self._prepare_for_class(__snake_case				,    __snake_case				,    return_labels=__snake_case  )
											__UpperCamelCase				:  Dict								=			model(**__snake_case  ).loss
											loss.backward()






	def 				lowerCamelCase__    (				self		:	Union[str, Any]  )  ->					List[str]:







						"""simple docstring"""







						__UpperCamelCase			, __UpperCamelCase				:  int								=			self.model_tester.prepare_config_and_inputs_for_common()

						__UpperCamelCase				:  Any								=			_config_zero_init(__snake_case  )
						for model_class in self.all_model_classes:
											__UpperCamelCase				:  List[str]								=			model_class(config=__snake_case  )
											# Skip the check for the backbone
											__UpperCamelCase				:  Union[str, Any]								=			[]
											for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																					__UpperCamelCase				:  List[Any]								=			[F'''{name}.{key}''' for key in module.state_dict().keys()]
																					break

											for name, param in model.named_parameters():
																if param.requires_grad:
																					if name in backbone_params:
																										continue
																					self.assertIn(
																					    ((param.data.mean() * 1E9).round() / 1E9).item()				,    [0.0, 1.0]				,    msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,    )






	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					List[str]:







						"""simple docstring"""







						pass






	@slow
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					Optional[Any]:







						"""simple docstring"""







						for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__UpperCamelCase				:  Optional[Any]								=			DPTModel.from_pretrained(__snake_case  )
											self.assertIsNotNone(__snake_case  )






def 							A__   ()    ->	List[Any]:
					__UpperCamelCase				:  List[Any]								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
					return image







@require_torch
@require_vision
@slow
class       UpperCAmelCase__							(					unittest.TestCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	List[str]  )  ->					int:







						"""simple docstring"""







						__UpperCamelCase				:  Any								=			DPTImageProcessor.from_pretrained("""Intel/dpt-large"""  )
						__UpperCamelCase				:  List[Any]								=			DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""  ).to(__snake_case  )

						__UpperCamelCase				:  Union[str, Any]								=			prepare_img()
						__UpperCamelCase				:  Optional[Any]								=			image_processor(images=__snake_case				,    return_tensors="""pt"""  ).to(__snake_case  )

						# forward pass
						with torch.no_grad():
											__UpperCamelCase				:  str								=			model(**__snake_case  )
											__UpperCamelCase				:  List[Any]								=			outputs.predicted_depth

						# verify the predicted depth
						__UpperCamelCase				:  Union[str, Any]								=			torch.Size((1, 384, 384)  )
						self.assertEqual(predicted_depth.shape				,    __snake_case  )

						__UpperCamelCase				:  Union[str, Any]								=			torch.tensor(
						    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]  ).to(__snake_case  )

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






	def 				lowerCamelCase__    (				self		:	Union[str, Any]  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""  )
						__UpperCamelCase				:  Optional[int]								=			DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""  ).to(__snake_case  )

						__UpperCamelCase				:  Optional[Any]								=			prepare_img()
						__UpperCamelCase				:  List[Any]								=			image_processor(images=__snake_case				,    return_tensors="""pt"""  ).to(__snake_case  )

						# forward pass
						with torch.no_grad():
											__UpperCamelCase				:  str								=			model(**__snake_case  )

						# verify the logits
						__UpperCamelCase				:  str								=			torch.Size((1, 150, 480, 480)  )
						self.assertEqual(outputs.logits.shape				,    __snake_case  )

						__UpperCamelCase				:  Tuple								=			torch.tensor(
						    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]  ).to(__snake_case  )

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






	def 				lowerCamelCase__    (				self		:	str  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  Tuple								=			DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""  )
						__UpperCamelCase				:  int								=			DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""  ).to(__snake_case  )

						__UpperCamelCase				:  Tuple								=			prepare_img()
						__UpperCamelCase				:  str								=			image_processor(images=__snake_case				,    return_tensors="""pt"""  ).to(__snake_case  )

						# forward pass
						with torch.no_grad():
											__UpperCamelCase				:  Tuple								=			model(**__snake_case  )

						__UpperCamelCase				:  Union[str, Any]								=			outputs.logits.detach().cpu()

						__UpperCamelCase				:  Optional[int]								=			image_processor.post_process_semantic_segmentation(outputs=__snake_case				,    target_sizes=[(500, 300)]  )
						__UpperCamelCase				:  Optional[int]								=			torch.Size((500, 300)  )
						self.assertEqual(segmentation[0].shape				,    __snake_case  )

						__UpperCamelCase				:  str								=			image_processor.post_process_semantic_segmentation(outputs=__snake_case  )
						__UpperCamelCase				:  Optional[Any]								=			torch.Size((480, 480)  )
						self.assertEqual(segmentation[0].shape				,    __snake_case  )












import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a__		    =    '''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

a__		    =    logging.getLogger(__name__)


@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					Optional[str]			  =		field(
	    default='roberta-large-mnli'  ,     metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}      )
	__magic_name__      :					Optional[str]			  =		field(
	    default='This example is {}.'  ,     metadata={
	        'help': (
	            'Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'
	            'where class name is inserted.'
	        )
	    }  ,     )
	__magic_name__      :					Optional[int]			  =		field(
	    default=32  ,     metadata={'help': 'Batch size for generating teacher predictions.'}      )
	__magic_name__      :					Optional[bool]			  =		field(
	    default=_UpperCamelCase  ,     metadata={
	        'help': (
	            'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
	            'multi-class multi-label classification).'
	        )
	    }  ,     )
	__magic_name__      :					Optional[float]			  =		field(
	    default=1.0  ,     metadata={'help': 'Temperature applied to teacher softmax for distillation.'}      )






@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					Optional[str]			  =		field(
	    default='distilbert-base-uncased'  ,     metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}      )






@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					str			  =		field(metadata={'help': 'Text file with one unlabeled instance per line.'}      )
	__magic_name__      :					str			  =		field(metadata={'help': 'Text file with one class name per line.'}      )
	__magic_name__      :					bool			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'}  ,     )






@dataclass
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[str]			  =		field(
	    default=_UpperCamelCase  ,     metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}  ,     )
	__magic_name__      :					int			  =		field(
	    default=32  ,     metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'}      )
	__magic_name__      :					int			  =		field(
	    default=128  ,     metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'}      )
	__magic_name__      :					float			  =		field(default=1.0  ,     metadata={'help': 'Total number of training epochs to perform.'}      )
	__magic_name__      :					bool			  =		field(default=_UpperCamelCase  ,     metadata={'help': 'Whether to run training of student model.'}      )
	__magic_name__      :					bool			  =		field(
	    default=_UpperCamelCase  ,     metadata={
	        'help': (
	            'Whether to evaluate the agreement of the final student predictions and the teacher predictions'
	            'after training.'
	        )
	    }  ,     )
	__magic_name__      :					Optional[int]			  =		field(
	    default=0  ,     metadata={
	        'help': (
	            'Limit the total amount of checkpoints.'
	            'Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'
	        )
	    }  ,     )






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	Any				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Union[str, Any]				,    lowerCAmelCase		:	Tuple=False  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			inputs["""labels"""]
						__UpperCamelCase				:  List[Any]								=			model(inputs["""input_ids"""]				,    attention_mask=inputs["""attention_mask"""]  )
						__UpperCamelCase				:  Optional[Any]								=			outputs[0]

						__UpperCamelCase				:  List[str]								=			-torch.sum(target_p * logits.log_softmax(dim=-1  )				,    axis=-1  ).mean()

						if return_outputs:
											return loss, outputs

						return loss






def 							A__   (snake_case      :		Optional[int]							)    ->	List[Any]:
					__UpperCamelCase				:  Union[str, Any]								=			[]
					with open(snake_case		,  """r"""							) as f:
										for line in f:
															__UpperCamelCase				:  Tuple								=			line.strip()
															if len(snake_case							) > 0:
																				lines.append(snake_case							)
					return lines





def 							A__   (snake_case      :		Optional[int]		,  snake_case      :		Optional[Any]		,  snake_case      :		str							)    ->	str:
					__UpperCamelCase				:  Union[str, Any]								=			[]
					__UpperCamelCase				:  List[str]								=			[]
					for example in examples:
										for name in class_names:
															premises.append(snake_case							)
															hypotheses.append(hypothesis_template.format(snake_case							)							)
					return premises, hypotheses





def 							A__   (snake_case      :		Optional[Any]							)    ->	int:
					for label, ind in config.labelaid.items():
										if label.lower().startswith("""entail"""							):
															return ind
					logger.warning("""Could not identify entailment dimension from teacher config label2id. Setting to -1."""							)
					return -1





def 							A__   (snake_case      :		str		,  snake_case      :		List[str]		,  snake_case      :		List[str]		,  snake_case      :		str		,  snake_case      :		int		,  snake_case      :		float		,  snake_case      :		bool		,  snake_case      :		bool		,  snake_case      :		bool		,  snake_case      :		bool		,  )    ->	List[Any]:
					__UpperCamelCase				:  Any								=			AutoModelForSequenceClassification.from_pretrained(snake_case							)
					__UpperCamelCase				:  str								=			model.config
					if not no_cuda and torch.cuda.is_available():
										__UpperCamelCase				:  List[Any]								=			nn.DataParallel(model.cuda()							)
										batch_size *= len(model.device_ids							)
					__UpperCamelCase				:  Optional[int]								=			AutoTokenizer.from_pretrained(snake_case		,  use_fast=snake_case							)

					__UpperCamelCase			, __UpperCamelCase				:  int								=			get_premise_hypothesis_pairs(snake_case		,  snake_case		,  snake_case							)
					__UpperCamelCase				:  Any								=			[]

					for i in tqdm(range(0		,  len(snake_case							)		,  snake_case							)							):
										__UpperCamelCase				:  List[str]								=			premises[i : i + batch_size]
										__UpperCamelCase				:  Dict								=			hypotheses[i : i + batch_size]

										__UpperCamelCase				:  Any								=			tokenizer(
										    snake_case		,  snake_case		,  padding=snake_case		,  truncation="""only_first"""		,  return_tensors="""pt"""		,  )

										with torch.cuda.amp.autocast(enabled=snake_case							):
															with torch.no_grad():
																				__UpperCamelCase				:  Tuple								=			model(**snake_case							)
										logits.append(outputs.logits.detach().cpu().float()							)

					__UpperCamelCase				:  Tuple								=			get_entailment_id(snake_case							)
					__UpperCamelCase				:  List[Any]								=			-1 if entail_id == 0 else 0
					__UpperCamelCase				:  List[Any]								=			torch.cat(snake_case		,  dim=0							)  # N*K x 3
					__UpperCamelCase				:  str								=			logits.reshape(len(snake_case							)		,  len(snake_case							)		,  -1							)[..., [contr_id, entail_id]]  # N x K x 2

					if multi_label:
										# softmax over (contr, entail) logits for each class independently
										__UpperCamelCase				:  Optional[int]								=			(nli_logits / temperature).softmax(-1							)
					else:
										# softmax over entail logits across classes s.t. class probabilities sum to 1.
										__UpperCamelCase				:  Any								=			(nli_logits / temperature).softmax(1							)

					return nli_prob[..., 1]  # N x K





def 							A__   ()    ->	Optional[Any]:
					__UpperCamelCase				:  Dict								=			HfArgumentParser(
					    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)		,  description=snake_case		,  )

					if len(sys.argv							) == 2 and sys.argv[1].endswith(""".json"""							):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  Tuple								=			parser.parse_json_file(
										    json_file=os.path.abspath(sys.argv[1]							)							)
					else:
										__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  List[Any]								=			parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					__UpperCamelCase				:  Union[str, Any]								=			None
					if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
										__UpperCamelCase				:  Union[str, Any]								=			get_last_checkpoint(training_args.output_dir							)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
															raise ValueError(
															    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
															    """Use --overwrite_output_dir to overcome."""							)
										elif last_checkpoint is not None:
															logger.info(
															    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
															    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""							)

    # Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,  datefmt="""%m/%d/%Y %H:%M:%S"""		,  handlers=[logging.StreamHandler(sys.stdout							)]		,  )
					logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

					# Log on each process the small summary:
					logger.warning(
					    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
					    + F'''distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'''							)
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank							):
										utils.logging.set_verbosity_info()
										utils.logging.enable_default_handler()
										utils.logging.enable_explicit_format()

					if training_args.local_rank != -1:
										raise ValueError("""Distributed training is not currently supported."""							)
					if training_args.tpu_num_cores is not None:
										raise ValueError("""TPU acceleration is not currently supported."""							)

					logger.info(F'''Training/evaluation parameters {training_args}'''							)

					# Set seed before initializing model.
					set_seed(training_args.seed							)

					# 1. read in data
					__UpperCamelCase				:  Union[str, Any]								=			read_lines(data_args.data_file							)
					__UpperCamelCase				:  int								=			read_lines(data_args.class_names_file							)

					# 2. get teacher predictions and load into dataset
					logger.info("""Generating predictions from zero-shot teacher model"""							)
					__UpperCamelCase				:  List[Any]								=			get_teacher_predictions(
					    teacher_args.teacher_name_or_path		,  snake_case		,  snake_case		,  teacher_args.hypothesis_template		,  teacher_args.teacher_batch_size		,  teacher_args.temperature		,  teacher_args.multi_label		,  data_args.use_fast_tokenizer		,  training_args.no_cuda		,  training_args.fpaa		,  )
					__UpperCamelCase				:  Any								=			Dataset.from_dict(
					    {
					        """text""": examples,
					        """labels""": teacher_soft_preds,
					    }							)

					# 3. create student
					logger.info("""Initializing student model"""							)
					__UpperCamelCase				:  List[str]								=			AutoModelForSequenceClassification.from_pretrained(
					    student_args.student_name_or_path		,  num_labels=len(snake_case							)							)
					__UpperCamelCase				:  Tuple								=			AutoTokenizer.from_pretrained(student_args.student_name_or_path		,  use_fast=data_args.use_fast_tokenizer							)
					__UpperCamelCase				:  int								=			dict(enumerate(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			{label: i for i, label in enumerate(snake_case							)}

					# 4. train student on teacher predictions
					__UpperCamelCase				:  Tuple								=			dataset.map(snake_case		,  input_columns="""text"""							)
					dataset.set_format("""torch"""							)

					def compute_metrics(snake_case      :		List[Any]		,  snake_case      :		Optional[int]=False							):
										__UpperCamelCase				:  Any								=			p.predictions.argmax(-1							)
										__UpperCamelCase				:  List[str]								=			p.label_ids.argmax(-1							)  # "label_ids" are actually distributions
										return {"agreement": (preds == proxy_labels).mean().item()}

					__UpperCamelCase				:  List[str]								=			DistillationTrainer(
					    model=snake_case		,  tokenizer=snake_case		,  args=snake_case		,  train_dataset=snake_case		,  compute_metrics=snake_case		,  )

					if training_args.do_train:
										logger.info("""Training student model on teacher predictions"""							)
										trainer.train()

					if training_args.do_eval:
										__UpperCamelCase				:  List[str]								=			trainer.evaluate(eval_dataset=snake_case							)["""eval_agreement"""]
										logger.info(F'''Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%'''							)

					trainer.save_model()


if __name__ == "__main__":
	main()






