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   lowercase			(   a   ):




							def 		__snake_case(  self      :    Optional[Any]					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.config_class(**self.inputs_dict					)
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"hidden_sizes"					)					)
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"neck_hidden_sizes"					)					)
												self.parent.assertTrue(hasattr(_UpperCamelCase		,							"num_attention_heads"					)					)



class   lowercase			:




							def __init__(  self      :    List[str]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    List[Any]=13		,							_UpperCamelCase      :    List[Any]=32		,							_UpperCamelCase      :    Optional[Any]=2		,							_UpperCamelCase      :    Tuple=3		,							_UpperCamelCase      :    Optional[Any]=640		,							_UpperCamelCase      :    Optional[Any]=4		,							_UpperCamelCase      :    Tuple="silu"		,							_UpperCamelCase      :    List[Any]=3		,							_UpperCamelCase      :    Optional[int]=32		,							_UpperCamelCase      :    List[Any]=0.1		,							_UpperCamelCase      :    Tuple=0.1		,							_UpperCamelCase      :    Optional[int]=0.1		,							_UpperCamelCase      :    Optional[Any]=0.0_2		,							_UpperCamelCase      :    Union[str, Any]=True		,							_UpperCamelCase      :    Tuple=True		,							_UpperCamelCase      :    List[str]=10		,							_UpperCamelCase      :    int=None		,							)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				parent
												SCREAMING_SNAKE_CASE				=				batch_size
												SCREAMING_SNAKE_CASE				=				image_size
												SCREAMING_SNAKE_CASE				=				patch_size
												SCREAMING_SNAKE_CASE				=				num_channels
												SCREAMING_SNAKE_CASE				=				last_hidden_size
												SCREAMING_SNAKE_CASE				=				num_attention_heads
												SCREAMING_SNAKE_CASE				=				hidden_act
												SCREAMING_SNAKE_CASE				=				conv_kernel_size
												SCREAMING_SNAKE_CASE				=				output_stride
												SCREAMING_SNAKE_CASE				=				hidden_dropout_prob
												SCREAMING_SNAKE_CASE				=				attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE				=				classifier_dropout_prob
												SCREAMING_SNAKE_CASE				=				use_labels
												SCREAMING_SNAKE_CASE				=				is_training
												SCREAMING_SNAKE_CASE				=				num_labels
												SCREAMING_SNAKE_CASE				=				initializer_range
												SCREAMING_SNAKE_CASE				=				scope




							def 		__snake_case(  self      :    Dict					)    -> Dict:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE				=				self.get_config()

												return config, pixel_values, labels, pixel_labels




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



												'''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 		__snake_case(  self      :    Union[str, Any]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Optional[Any]					)    -> int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFMobileViTModel(config=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							training=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				SCREAMING_SNAKE_CASE				=				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 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Tuple					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.num_labels
												SCREAMING_SNAKE_CASE				=				TFMobileViTForImageClassification(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							labels=_UpperCamelCase		,							training=_UpperCamelCase					)
												self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels)					)




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    int					)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.num_labels
												SCREAMING_SNAKE_CASE				=				TFMobileViTForSemanticSegmentation(_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				SCREAMING_SNAKE_CASE				=				self.image_size // self.output_stride

												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							training=_UpperCamelCase					)
												self.parent.assertEqual(
												    result.logits.shape		,							(self.batch_size, self.num_labels, expected_height, expected_width)					)

												SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							labels=_UpperCamelCase		,							training=_UpperCamelCase					)
												self.parent.assertEqual(
												    result.logits.shape		,							(self.batch_size, self.num_labels, expected_height, expected_width)					)





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



												'''simple docstring'''

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



@require_tf
class   lowercase			(   a ,				a ,				unittest.TestCase   ):
							lowercase__  :				List[Any]									=							(
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							lowercase__  :				List[Any]									=							(
							    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							lowercase__  :				List[Any]									=							False
							lowercase__  :				Tuple									=							False
							lowercase__  :				Dict									=							False
							lowercase__  :				List[str]									=							False
							lowercase__  :				List[Any]									=							False




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFMobileViTModelTester(self					)
												SCREAMING_SNAKE_CASE				=				TFMobileViTConfigTester(self		,							config_class=_UpperCamelCase		,							has_text_modality=_UpperCamelCase					)




							def 		__snake_case(  self      :    Any					)    -> int:



												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason="MobileViT does not use inputs_embeds"					)
							def 		__snake_case(  self      :    Optional[Any]					)    -> str:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileViT does not support input and output embeddings"					)
							def 		__snake_case(  self      :    List[Any]					)    -> List[str]:



												'''simple docstring'''

												pass




							@unittest.skip(reason="MobileViT does not output attentions"					)
							def 		__snake_case(  self      :    int					)    -> List[str]:



												'''simple docstring'''

												pass




							def 		__snake_case(  self      :    int					)    -> Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs_for_common()

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

																	SCREAMING_SNAKE_CASE				=				["pixel_values"]
																	self.assertListEqual(arg_names[:1]		,							_UpperCamelCase					)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase					)




							def 		__snake_case(  self      :    Dict					)    -> Optional[int]:



												'''simple docstring'''

												def check_hidden_states_output(_UpperCamelCase      :    int		,							_UpperCamelCase      :    int		,							_UpperCamelCase      :    Union[str, Any]					):
																	SCREAMING_SNAKE_CASE				=				model_class(_UpperCamelCase					)

																	SCREAMING_SNAKE_CASE				=				model(**self._prepare_for_class(_UpperCamelCase		,							_UpperCamelCase					)					)

																	SCREAMING_SNAKE_CASE				=				outputs.hidden_states

																	SCREAMING_SNAKE_CASE				=				5
																	self.assertEqual(len(_UpperCamelCase					)		,							_UpperCamelCase					)

																	# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																	# with the width and height being successively divided by 2.
																	SCREAMING_SNAKE_CASE				=				2
																	for i in range(len(_UpperCamelCase					)					):
																						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					)

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	SCREAMING_SNAKE_CASE				=				True
																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase					)

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

																	check_hidden_states_output(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase					)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase					)




							def 		__snake_case(  self      :    Optional[Any]					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase					)




							@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 		__snake_case(  self      :    Tuple					)    -> Any:



												'''simple docstring'''

												super().test_dataset_conversion()




							def 		__snake_case(  self      :    Optional[int]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Optional[int]=2e-1		,							_UpperCamelCase      :    Union[str, Any]=2e-1					)    -> int:



												'''simple docstring'''

												self.assertTrue(np.allclose(_UpperCamelCase		,							_UpperCamelCase		,							atol=_UpperCamelCase		,							rtol=_UpperCamelCase					)					)




							@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 		__snake_case(  self      :    str					)    -> str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				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":
																						SCREAMING_SNAKE_CASE				=				model_class(_UpperCamelCase					)
																						if getattr(_UpperCamelCase		,							"hf_compute_loss"		,							_UpperCamelCase					):
																											super().test_keras_fit()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				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":
																						SCREAMING_SNAKE_CASE				=				5

																	SCREAMING_SNAKE_CASE				=				model_class(_UpperCamelCase					)
																	if getattr(_UpperCamelCase		,							"hf_compute_loss"		,							_UpperCamelCase					):
																						# The number of elements in the loss should be the same as the number of elements in the label
																						SCREAMING_SNAKE_CASE				=				self._prepare_for_class(inputs_dict.copy()		,							_UpperCamelCase		,							return_labels=_UpperCamelCase					)
																						SCREAMING_SNAKE_CASE				=				prepared_for_class[
																						    sorted(prepared_for_class.keys() - inputs_dict.keys()		,							reverse=_UpperCamelCase					)[0]
																						]
																						SCREAMING_SNAKE_CASE				=				added_label.shape.as_list()[:1]

																						# Test that model correctly compute the loss with kwargs
																						SCREAMING_SNAKE_CASE				=				self._prepare_for_class(inputs_dict.copy()		,							_UpperCamelCase		,							return_labels=_UpperCamelCase					)
																						SCREAMING_SNAKE_CASE				=				{"input_ids", "pixel_values", "input_features"}
																						SCREAMING_SNAKE_CASE				=				possible_input_names.intersection(set(_UpperCamelCase					)					).pop()
																						SCREAMING_SNAKE_CASE				=				prepared_for_class.pop(_UpperCamelCase					)

																						SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							**_UpperCamelCase					)[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
																						SCREAMING_SNAKE_CASE				=				self._prepare_for_class(inputs_dict.copy()		,							_UpperCamelCase		,							return_labels=_UpperCamelCase					)
																						SCREAMING_SNAKE_CASE				=				{"input_ids", "pixel_values", "input_features"}
																						SCREAMING_SNAKE_CASE				=				possible_input_names.intersection(set(_UpperCamelCase					)					).pop()
																						SCREAMING_SNAKE_CASE				=				prepared_for_class.pop(_UpperCamelCase					)
																						if "labels" in prepared_for_class:
																											SCREAMING_SNAKE_CASE				=				prepared_for_class["labels"].numpy()
																											if len(labels.shape					) > 1 and labels.shape[1] != 1:
																																# labels[0] = -100
																																SCREAMING_SNAKE_CASE				=				tf.convert_to_tensor(_UpperCamelCase					)
																																SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase		,							**_UpperCamelCase					)[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
																						SCREAMING_SNAKE_CASE				=				self._prepare_for_class(inputs_dict.copy()		,							_UpperCamelCase		,							return_labels=_UpperCamelCase					)
																						SCREAMING_SNAKE_CASE				=				model(_UpperCamelCase					)[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
																						SCREAMING_SNAKE_CASE				=				self._prepare_for_class(inputs_dict.copy()		,							_UpperCamelCase		,							return_labels=_UpperCamelCase					)

																						# Get keys that were added with the _prepare_for_class function
																						SCREAMING_SNAKE_CASE				=				prepared_for_class.keys() - inputs_dict.keys()
																						SCREAMING_SNAKE_CASE				=				inspect.signature(model.call					).parameters
																						SCREAMING_SNAKE_CASE				=				list(signature.keys()					)

																						# Create a dictionary holding the location of the tensors in the tuple
																						SCREAMING_SNAKE_CASE				=				{0: input_name}
																						for label_key in label_keys:
																											SCREAMING_SNAKE_CASE				=				signature_names.index(_UpperCamelCase					)
																											SCREAMING_SNAKE_CASE				=				label_key
																						SCREAMING_SNAKE_CASE				=				sorted(tuple_index_mapping.items()					)
																						# Initialize a list with their default values, update the values and convert to a tuple
																						SCREAMING_SNAKE_CASE				=				[]

																						for name in signature_names:
																											if name != "kwargs":
																																list_input.append(signature[name].default					)

																						for index, value in sorted_tuple_index_mapping:
																											SCREAMING_SNAKE_CASE				=				prepared_for_class[value]

																						SCREAMING_SNAKE_CASE				=				tuple(_UpperCamelCase					)

																						# Send to model
																						SCREAMING_SNAKE_CASE				=				model(tuple_input[:-1]					)[0]

																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)





							@slow
							def 		__snake_case(  self      :    List[Any]					)    -> Dict:



												'''simple docstring'''

												for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	SCREAMING_SNAKE_CASE				=				TFMobileViTModel.from_pretrained(_UpperCamelCase					)
																	self.assertIsNotNone(_UpperCamelCase					)





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					return image


@require_tf
class   lowercase			(   unittest.TestCase   ):




							@slow
							def 		__snake_case(  self      :    Optional[Any]					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"					)

												SCREAMING_SNAKE_CASE				=				MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"					)
												SCREAMING_SNAKE_CASE				=				prepare_img()
												SCREAMING_SNAKE_CASE				=				image_processor(images=_UpperCamelCase		,							return_tensors="tf"					)

												# forward pass
												SCREAMING_SNAKE_CASE				=				model(**_UpperCamelCase		,							training=_UpperCamelCase					)

												# verify the logits
												SCREAMING_SNAKE_CASE				=				tf.TensorShape((1, 1_000)					)
												self.assertEqual(outputs.logits.shape		,							_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				tf.constant([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]					)

												tf.debugging.assert_near(outputs.logits[0, :3]		,							_UpperCamelCase		,							atol=1e-4		,							rtol=1e-04					)





							@slow
							def 		__snake_case(  self      :    List[str]					)    -> Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

												SCREAMING_SNAKE_CASE				=				MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"					)

												SCREAMING_SNAKE_CASE				=				prepare_img()
												SCREAMING_SNAKE_CASE				=				image_processor(images=_UpperCamelCase		,							return_tensors="tf"					)

												# forward pass
												SCREAMING_SNAKE_CASE				=				model(inputs.pixel_values		,							training=_UpperCamelCase					)
												SCREAMING_SNAKE_CASE				=				outputs.logits

												# verify the logits
												SCREAMING_SNAKE_CASE				=				tf.TensorShape((1, 21, 32, 32)					)
												self.assertEqual(logits.shape		,							_UpperCamelCase					)

												SCREAMING_SNAKE_CASE				=				tf.constant(
												    [
												        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
												        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
												        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
												    ]					)

												tf.debugging.assert_near(logits[0, :3, :3, :3]		,							_UpperCamelCase		,							rtol=1e-4		,							atol=1e-4					)





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


_lowerCamelCase    :      Tuple	 =						'''
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.
'''

_lowerCamelCase    :      Optional[Any]	 =						logging.getLogger(__name__)


@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""roberta-large-mnli""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )
							lowercase__  :				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."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							field(
							    default=32 ,				metadata={"""help""": """Batch size for generating teacher predictions."""}   )
							lowercase__  :				Optional[bool]									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    } ,				)
							lowercase__  :				Optional[float]									=							field(
							    default=1.0 ,				metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}   )



@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""distilbert-base-uncased""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )



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



@dataclass
class   lowercase			(   a   ):
							lowercase__  :				Optional[str]									=							field(
							    default=a ,				metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} ,				)
							lowercase__  :				int									=							field(
							    default=32 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}   )
							lowercase__  :				int									=							field(
							    default=128 ,				metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}   )
							lowercase__  :				float									=							field(default=1.0 ,				metadata={"""help""": """Total number of training epochs to perform."""}   )
							lowercase__  :				bool									=							field(default=a ,				metadata={"""help""": """Whether to run training of student model."""}   )
							lowercase__  :				bool									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
							            """after training."""
							        )
							    } ,				)
							lowercase__  :				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   lowercase			(   a   ):




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				inputs["labels"]
												SCREAMING_SNAKE_CASE				=				model(inputs["input_ids"]		,							attention_mask=inputs["attention_mask"]					)
												SCREAMING_SNAKE_CASE				=				outputs[0]

												SCREAMING_SNAKE_CASE				=				-torch.sum(target_p * logits.log_softmax(dim=-1					)		,							axis=-1					).mean()

												if return_outputs:
																	return loss, outputs

												return loss





def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					with open(UpperCAmelCase__					,       "r"     ) as f:
										for line in f:
															SCREAMING_SNAKE_CASE				=				line.strip()
															if len(UpperCAmelCase__     ) > 0:
																				lines.append(UpperCAmelCase__     )
					return lines





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				[]
					for example in examples:
										for name in class_names:
															premises.append(UpperCAmelCase__     )
															hypotheses.append(hypothesis_template.format(UpperCAmelCase__     )     )
					return premises, hypotheses





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[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 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       ):
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				model.config
					if not no_cuda and torch.cuda.is_available():
										SCREAMING_SNAKE_CASE				=				nn.DataParallel(model.cuda()     )
										batch_size *= len(model.device_ids     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(UpperCAmelCase__					,       use_fast=UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				get_premise_hypothesis_pairs(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				[]

					for i in tqdm(range(0					,       len(UpperCAmelCase__     )					,       UpperCAmelCase__     )     ):
										SCREAMING_SNAKE_CASE				=				premises[i : i + batch_size]
										SCREAMING_SNAKE_CASE				=				hypotheses[i : i + batch_size]

										SCREAMING_SNAKE_CASE				=				tokenizer(
										    UpperCAmelCase__					,       UpperCAmelCase__					,       padding=UpperCAmelCase__					,       truncation="only_first"					,       return_tensors="pt"					,       )

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

					SCREAMING_SNAKE_CASE				=				get_entailment_id(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				-1 if entail_id == 0 else 0
					SCREAMING_SNAKE_CASE				=				torch.cat(UpperCAmelCase__					,       dim=0     )  # N*K x 3
					SCREAMING_SNAKE_CASE				=				logits.reshape(len(UpperCAmelCase__     )					,       len(UpperCAmelCase__     )					,       -1     )[..., [contr_id, entail_id]]  # N x K x 2

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

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





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				HfArgumentParser(
					    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,       description=UpperCAmelCase__					,       )

					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.
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_json_file(
										    json_file=os.path.abspath(sys.argv[1]     )     )
					else:
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					SCREAMING_SNAKE_CASE				=				None
					if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
										SCREAMING_SNAKE_CASE				=				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
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.data_file     )
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.class_names_file     )

					# 2. get teacher predictions and load into dataset
					logger.info("Generating predictions from zero-shot teacher model"     )
					SCREAMING_SNAKE_CASE				=				get_teacher_predictions(
					    teacher_args.teacher_name_or_path					,       UpperCAmelCase__					,       UpperCAmelCase__					,       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					,       )
					SCREAMING_SNAKE_CASE				=				Dataset.from_dict(
					    {
					        "text": examples,
					        "labels": teacher_soft_preds,
					    }     )

					# 3. create student
					logger.info("Initializing student model"     )
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(
					    student_args.student_name_or_path					,       num_labels=len(UpperCAmelCase__     )     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(student_args.student_name_or_path					,       use_fast=data_args.use_fast_tokenizer     )
					SCREAMING_SNAKE_CASE				=				dict(enumerate(UpperCAmelCase__     )     )
					SCREAMING_SNAKE_CASE				=				{label: i for i, label in enumerate(UpperCAmelCase__     )}

					# 4. train student on teacher predictions
					SCREAMING_SNAKE_CASE				=				dataset.map(UpperCAmelCase__					,       input_columns="text"     )
					dataset.set_format("torch"     )

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

					SCREAMING_SNAKE_CASE				=				DistillationTrainer(
					    model=UpperCAmelCase__					,       tokenizer=UpperCAmelCase__					,       args=UpperCAmelCase__					,       train_dataset=UpperCAmelCase__					,       compute_metrics=UpperCAmelCase__					,       )

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

					if training_args.do_eval:
										SCREAMING_SNAKE_CASE				=				trainer.evaluate(eval_dataset=UpperCAmelCase__     )["eval_agreement"]
										logger.info(F"Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%"     )

					trainer.save_model()


if __name__ == "__main__":
							main()




