





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     str							,	a				:     Optional[Any]=2							,	a				:     Dict=True							,	a				:     Optional[int]=False							,	a				:     Any=10							,	a				:     str=3							,	a				:     Tuple=32 * 8							,	a				:     Optional[Any]=32 * 8							,	a				:     str=4							,	a				:     Tuple=64							,	)	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					parent
													SCREAMING_SNAKE_CASE__     :				int   =					batch_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					is_training
													SCREAMING_SNAKE_CASE__     :				int   =					use_auxiliary_loss
													SCREAMING_SNAKE_CASE__     :				Any   =					num_queries
													SCREAMING_SNAKE_CASE__     :				List[str]   =					num_channels
													SCREAMING_SNAKE_CASE__     :				int   =					min_size
													SCREAMING_SNAKE_CASE__     :				int   =					max_size
													SCREAMING_SNAKE_CASE__     :				Tuple   =					num_labels
													SCREAMING_SNAKE_CASE__     :				Tuple   =					hidden_dim
													SCREAMING_SNAKE_CASE__     :				int   =					hidden_dim

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
													    a    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.ones([self.batch_size, self.min_size, self.max_size]							,	device=a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,	device=a    ) > 0.5
													).float()
													SCREAMING_SNAKE_CASE__     :				Any   =					(torch.rand((self.batch_size, self.num_labels)							,	device=a    ) > 0.5).long()

													SCREAMING_SNAKE_CASE__     :				str   =					self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels

							def      A_  (      self				:     str    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					MaskaFormerConfig(
													    hidden_size=self.hidden_dim							,	)
													SCREAMING_SNAKE_CASE__     :				Any   =					self.num_queries
													SCREAMING_SNAKE_CASE__     :				str   =					self.num_labels

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[1, 1, 1, 1]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.num_channels

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					64
													SCREAMING_SNAKE_CASE__     :				int   =					1_28
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.hidden_dim
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.hidden_dim
													SCREAMING_SNAKE_CASE__     :				Any   =					self.hidden_dim
													return config

							def      A_  (      self				:     int    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
													return config, inputs_dict

							def      A_  (      self				:     List[str]							,	a				:     List[Any]							,	a				:     Optional[Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				int   =					output.encoder_hidden_states
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					output.pixel_decoder_hidden_states
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					output.transformer_decoder_hidden_states

													self.parent.assertTrue(len(a    )							,	len(config.backbone_config.depths    )    )
													self.parent.assertTrue(len(a    )							,	len(config.backbone_config.depths    )    )
													self.parent.assertTrue(len(a    )							,	config.decoder_layers    )

							def      A_  (      self				:     Tuple							,	a				:     Optional[Any]							,	a				:     int							,	a				:     List[str]							,	a				:     Any=False    )	->str:
													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					MaskaFormerModel(config=a    )
																			model.to(a    )
																			model.eval()

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model(pixel_values=a							,	pixel_mask=a    )
																			SCREAMING_SNAKE_CASE__     :				int   =					model(a							,	output_hidden_states=a    )

													self.parent.assertEqual(
													    output.transformer_decoder_last_hidden_state.shape							,	(self.batch_size, self.num_queries, self.hidden_dim)							,	)
													# let's ensure the other two hidden state exists
													self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
													self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

													if output_hidden_states:
																			self.check_output_hidden_state(a							,	a    )






							def      A_  (      self				:     Tuple							,	a				:     Optional[Any]							,	a				:     List[Any]							,	a				:     Tuple							,	a				:     Union[str, Any]							,	a				:     Union[str, Any]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				int   =					MaskaFormerForUniversalSegmentation(config=a    )
													model.to(a    )
													model.eval()

													def comm_check_on_output(a				:     Optional[Any]    ):
																			# let's still check that all the required stuff is there
																			self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
																			self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
																			self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
																			# okay, now we need to check the logits shape
																			# due to the encoder compression, masks have a //4 spatial size
																			self.parent.assertEqual(
																			    result.masks_queries_logits.shape							,	(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,	)
																			# + 1 for null class
																			self.parent.assertEqual(
																			    result.class_queries_logits.shape							,	(self.batch_size, self.num_queries, self.num_labels + 1)    )

													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model(pixel_values=a							,	pixel_mask=a    )
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(a    )

																			comm_check_on_output(a    )

																			SCREAMING_SNAKE_CASE__     :				int   =					model(
																			    pixel_values=a							,	pixel_mask=a							,	mask_labels=a							,	class_labels=a    )

													comm_check_on_output(a    )

													self.parent.assertTrue(result.loss is not None    )
													self.parent.assertEqual(result.loss.shape							,	torch.Size([1]    )    )







@require_torch
class    _a     (				_UpperCamelCase					,							_UpperCamelCase					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							snake_case_							 =						{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

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

							def      A_  (      self				:     Optional[int]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				str   =					MaskaFormerModelTester(self    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					ConfigTester(self							,	config_class=a							,	has_text_modality=a    )

							def      A_  (      self				:     int    )	->Optional[int]:
													self.config_tester.run_common_tests()

							def      A_  (      self				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(a							,	**a							,	output_hidden_states=a    )

							def      A_  (      self				:     int    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*a    )

							@unittest.skip(reason="Mask2Former does not use inputs_embeds"    )
							def      A_  (      self				:     List[Any]    )	->List[str]:
													pass

							@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"    )
							def      A_  (      self				:     List[str]    )	->List[Any]:
													pass

							@unittest.skip(reason="Mask2Former is not a generative model"    )
							def      A_  (      self				:     Optional[Any]    )	->Union[str, Any]:
													pass

							@unittest.skip(reason="Mask2Former does not use token embeddings"    )
							def      A_  (      self				:     Optional[int]    )	->List[str]:
													pass

							@require_torch_multi_gpu
							@unittest.skip(
							    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"    )
							def      A_  (      self				:     int    )	->Optional[int]:
													pass

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

							def      A_  (      self				:     int    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model_class(a    )
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					inspect.signature(model.forward    )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[*signature.parameters.keys()]

																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					["pixel_values"]
																			self.assertListEqual(arg_names[:1]							,	a    )

							@slow
							def      A_  (      self				:     Any    )	->str:
													for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					MaskaFormerModel.from_pretrained(a    )
																			self.assertIsNotNone(a    )

							def      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(self.model_tester.min_size,) * 2
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					{
													    "pixel_values": torch.randn((2, 3, *size)							,	device=a    ),
													    "mask_labels": torch.randn((2, 10, *size)							,	device=a    ),
													    "class_labels": torch.zeros(2							,	10							,	device=a    ).long(),
													}
													SCREAMING_SNAKE_CASE__     :				Any   =					self.model_tester.get_config()

													SCREAMING_SNAKE_CASE__     :				Any   =					MaskaFormerForUniversalSegmentation(a    ).to(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					model(**a    )
													self.assertTrue(outputs.loss is not None    )

							def      A_  (      self				:     int    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(a							,	**a							,	output_hidden_states=a    )

							def      A_  (      self				:     Optional[Any]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					model_class(a    ).to(a    )
																			SCREAMING_SNAKE_CASE__     :				Any   =					model(**a							,	output_attentions=a    )
																			self.assertTrue(outputs.attentions is not None    )

							def      A_  (      self				:     Union[str, Any]    )	->List[str]:
													if not self.model_tester.is_training:
																			return

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.all_model_classes[1]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.model_tester.prepare_config_and_inputs()

													SCREAMING_SNAKE_CASE__     :				Tuple   =					model_class(a    )
													model.to(a    )
													model.train()

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model(a							,	mask_labels=a							,	class_labels=a    ).loss
													loss.backward()






							def      A_  (      self				:     Optional[Any]    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Any   =					self.all_model_classes[1]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.model_tester.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE__     :				int   =					True
													SCREAMING_SNAKE_CASE__     :				Tuple   =					True

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model_class(a    ).to(a    )
													model.train()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(a							,	mask_labels=a							,	class_labels=a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													SCREAMING_SNAKE_CASE__     :				Dict   =					outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()

													SCREAMING_SNAKE_CASE__     :				Any   =					outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=a    )

													self.assertIsNotNone(encoder_hidden_states.grad    )
													self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
													self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
													self.assertIsNotNone(attentions.grad    )







__lowercase       :Union[str, Any]       =  1e-4







def       UpperCAmelCase							(     ):





						'''simple docstring'''


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



@require_vision
@slow
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@cached_property
							def      A_  (      self				:     Dict    )	->int:
													return "facebook/mask2former-swin-small-coco-instance"

							@cached_property
							def      A_  (      self				:     Tuple    )	->Optional[int]:
													return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints    ) if is_vision_available() else None

							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					MaskaFormerModel.from_pretrained(self.model_checkpoints    ).to(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.default_image_processor
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					prepare_img()
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processor(a							,	return_tensors="pt"    ).to(a    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					inputs["pixel_values"].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
													# check size
													self.assertEqual(a							,	(1, 3, 3_84, 3_84)    )

													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(**a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					torch.tensor(
													    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]    ).to(a    )
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]							,	a							,	atol=a    )    )

													SCREAMING_SNAKE_CASE__     :				Tuple   =					torch.tensor(
													    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]    ).to(a    )
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,	a							,	atol=a    )    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.tensor(
													    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]    ).to(a    )
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,	a							,	atol=a    )    )

							def      A_  (      self				:     str    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Any   =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(a    ).eval()
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.default_image_processor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					prepare_img()
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_processor(a							,	return_tensors="pt"    ).to(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					inputs["pixel_values"].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
													# check size
													self.assertEqual(a							,	(1, 3, 3_84, 3_84)    )

													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				int   =					model(**a    )
													# masks_queries_logits
													SCREAMING_SNAKE_CASE__     :				List[str]   =					outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape							,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[
													    [-8.7839, -9.0056, -8.8121],
													    [-7.4104, -7.0313, -6.5401],
													    [-6.6105, -6.3427, -6.4675],
													]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.tensor(a    ).to(a    )
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,	a							,	atol=a    )    )
													# class_queries_logits
													SCREAMING_SNAKE_CASE__     :				str   =					outputs.class_queries_logits
													self.assertEqual(class_queries_logits.shape							,	(1, model.config.num_queries, model.config.num_labels + 1)    )
													SCREAMING_SNAKE_CASE__     :				str   =					torch.tensor(
													    [
													        [1.8324, -8.0835, -4.1922],
													        [0.8450, -9.0050, -3.6053],
													        [0.3045, -7.7293, -3.0275],
													    ]    ).to(a    )
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,	a							,	atol=a    )    )






							def      A_  (      self				:     Union[str, Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(a    ).eval()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.default_image_processor

													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processor(
													    [np.zeros((3, 8_00, 13_33)    ), np.zeros((3, 8_00, 13_33)    )]							,	segmentation_maps=[np.zeros((3_84, 3_84)    ).astype(np.floataa    ), np.zeros((3_84, 3_84)    ).astype(np.floataa    )]							,	return_tensors="pt"							,	)

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					inputs["pixel_values"].to(a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[el.to(a    ) for el in inputs["mask_labels"]]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[el.to(a    ) for el in inputs["class_labels"]]

													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				str   =					model(**a    )

													self.assertTrue(outputs.loss is not None    )






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  PreTrainedTokenizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Optional[int] = None			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					{}

						if train_file is not None:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[train_file]
						if eval_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[eval_file]
						if test_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[test_file]

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					datasets.load_dataset("csv"			,   data_files=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					list(ds[list(files.keys()     )[0]].features.keys()     )
						SCREAMING_SNAKE_CASE__     :				int   =					features_name.pop(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(set(ds[list(files.keys()     )[0]][label_name]     )     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{label: i for i, label in enumerate(_lowerCamelCase     )}
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.model_input_names
						SCREAMING_SNAKE_CASE__     :				Any   =					{}

						if len(_lowerCamelCase     ) == 1:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        example[features_name[0]]			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"     )			,   batched=_lowerCamelCase			,   )
						elif len(_lowerCamelCase     ) == 2:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				Any   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"			,   )			,   batched=_lowerCamelCase			,   )

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						SCREAMING_SNAKE_CASE__     :				Tuple   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Any   =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Dict   =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

						return train_ds, val_ds, test_ds, labelaid


__lowercase       :List[Any]       =  logging.getLogger(__name__)



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(metadata={"help": "Which column contains the label"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the training file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the development file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the test file"}							)
							snake_case_							 =						field(
							    default=1_28					,							metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }					,							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Overwrite the cached training and evaluation sets"}							)







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "Set this flag to use fast tokenization."}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,							)







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir     )
						    and os.listdir(training_args.output_dir     )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    " --overwrite_output_dir to overcome."     )

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,   datefmt="%m/%d/%Y %H:%M:%S"			,   level=logging.INFO			,   )
						logger.info(
						    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1     )}, """
						    f"""16-bits training: {training_args.fpaa}"""     )
						logger.info(f"""Training/evaluation parameters {training_args}"""     )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						SCREAMING_SNAKE_CASE__     :				Any   =					AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,   cache_dir=model_args.cache_dir			,   )

						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_tfds(
						    train_file=data_args.train_file			,   eval_file=data_args.dev_file			,   test_file=data_args.test_file			,   tokenizer=_lowerCamelCase			,   label_column_id=data_args.label_column_id			,   max_seq_length=data_args.max_seq_length			,   )

						SCREAMING_SNAKE_CASE__     :				str   =					AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,   num_labels=len(_lowerCamelCase     )			,   labelaid=_lowerCamelCase			,   idalabel={id: label for label, id in labelaid.items()}			,   finetuning_task="text-classification"			,   cache_dir=model_args.cache_dir			,   )

						with training_args.strategy.scope():
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path			,   from_pt=bool(".bin" in model_args.model_name_or_path     )			,   config=_lowerCamelCase			,   cache_dir=model_args.cache_dir			,   )

						def compute_metrics(_lowerCamelCase      :  EvalPrediction     ) -> Dict:
												SCREAMING_SNAKE_CASE__     :				Dict   =					np.argmax(p.predictions			,   axis=1     )

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE__     :				str   =					TFTrainer(
						    model=_lowerCamelCase			,   args=_lowerCamelCase			,   train_dataset=_lowerCamelCase			,   eval_dataset=_lowerCamelCase			,   compute_metrics=_lowerCamelCase			,   )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir     )

						# Evaluation
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"     )
												SCREAMING_SNAKE_CASE__     :				str   =					trainer.evaluate()
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					os.path.join(training_args.output_dir			,   "eval_results.txt"     )

												with open(_lowerCamelCase			,   "w"     ) as writer:
																		logger.info("***** Eval results *****"     )

																		for key, value in result.items():
																								logger.info(f"""  {key} = {value}"""     )
																								writer.write(f"""{key} = {value}\n"""     )

																		results.update(_lowerCamelCase     )

						return results


if __name__ == "__main__":
				main()