'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

							from transformers import TFResNetForImageClassification, TFResNetModel
							from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=3		,						__A=32		,						__A=3		,						__A=10		,						__A=[10, 20, 30, 40]		,						__A=[1, 1, 2, 1]		,						__A=True		,						__A=True		,						__A="relu"		,						__A=3		,						__A=None		,						) ->     Union[str, Any]:
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=embeddings_size
								_lowerCAmelCase										=hidden_sizes
								_lowerCAmelCase										=depths
								_lowerCAmelCase										=is_training
								_lowerCAmelCase										=use_labels
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=num_labels
								_lowerCAmelCase										=scope
								_lowerCAmelCase										=len(__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								_lowerCAmelCase										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

								_lowerCAmelCase										=self.get_config()

								return config, pixel_values, labels






		def 		UpperCamelCase__       (   self				) ->     int:
								return ResNetConfig(
								    num_channels=self.num_channels		,						embeddings_size=self.embeddings_size		,						hidden_sizes=self.hidden_sizes		,						depths=self.depths		,						hidden_act=self.hidden_act		,						num_labels=self.num_labels		,						image_size=self.image_size		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Tuple:
								_lowerCAmelCase										=TFResNetModel(config=__A				)
								_lowerCAmelCase										=model(__A				)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape		,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										=self.num_labels
								_lowerCAmelCase										=TFResNetForImageClassification(__A				)
								_lowerCAmelCase										=model(__A		,						labels=__A				)
								self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.prepare_config_and_inputs()
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=config_and_inputs
								_lowerCAmelCase										={'pixel_values': pixel_values}
								return config, inputs_dict

@require_tf
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		lowercase   :      List[Any]       =							(
		    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		lowercase   :      List[str]       =							False
		lowercase   :      Optional[Any]       =							False
		lowercase   :      List[str]       =							False
		lowercase   :      Union[str, Any]       =							False
		lowercase   :      List[str]       =							False






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=TFResNetModelTester(self				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=__A		,						has_text_modality=__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								return






		@unittest.skip(reason='ResNet does not use inputs_embeds'				)
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								pass






		@unittest.skip(reason='ResNet does not support input and output embeddings'				)
		def 		UpperCamelCase__       (   self				) ->     str:
								pass






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCAmelCase										=['pixel_values']
														self.assertListEqual(arg_names[:1]		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								def check_hidden_states_output(__A		,						__A		,						__A				):
														_lowerCAmelCase										=model_class(__A				)
														_lowerCAmelCase										=model(**self._prepare_for_class(__A		,						__A				)				)

														_lowerCAmelCase										=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_lowerCAmelCase										=self.model_tester.num_stages
														self.assertEqual(len(__A				)		,						expected_num_stages + 1				)

														# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:]				)		,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,						)

								_lowerCAmelCase						,				_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
								_lowerCAmelCase										=['basic', 'bottleneck']
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				_lowerCAmelCase										=layer_type
																				_lowerCAmelCase										=True
																				check_hidden_states_output(__A		,						__A		,						__A				)

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

																				check_hidden_states_output(__A		,						__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__A				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     str:
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase										=TFResNetModel.from_pretrained(__A				)
														self.assertIsNotNone(__A				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
						return image


@require_tf
@require_vision
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     Dict:
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
								    if is_vision_available()
								    else None
								)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)

								_lowerCAmelCase										=self.default_image_processor
								_lowerCAmelCase										=prepare_img()
								_lowerCAmelCase										=image_processor(images=__A		,						return_tensors='tf'				)

								# forward pass
								_lowerCAmelCase										=model(**__A				)

								# verify the logits
								_lowerCAmelCase										=tf.TensorShape((1, 1000)				)
								self.assertEqual(outputs.logits.shape		,						__A				)

								_lowerCAmelCase										=tf.constant([-11.1_069, -9.7_877, -8.3_777]				)

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






'''simple docstring'''

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								if type(__A				) == dict:
														_lowerCAmelCase										=Namespace(**__A				)
								_lowerCAmelCase										=glue_output_modes[hparams.task]
								_lowerCAmelCase										=glue_tasks_num_labels[hparams.task]

								super().__init__(__A		,						__A		,						self.mode				)






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

								_lowerCAmelCase										=self.trainer.lr_schedulers[0]['scheduler']
								_lowerCAmelCase										={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss, "log": tensorboard_logs}






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

								for mode in ["train", "dev"]:
														_lowerCAmelCase										=self._feature_file(__A				)
														if os.path.exists(__A				) and not args.overwrite_cache:
																				logger.info('Loading features from cached file %s'		,						__A				)
														else:
																				logger.info('Creating features from dataset file at %s'		,						args.data_dir				)
																				_lowerCAmelCase										=(
																				    processor.get_dev_examples(args.data_dir				)
																				    if mode == 'dev'
																				    else processor.get_train_examples(args.data_dir				)
																				)
																				_lowerCAmelCase										=convert_examples_to_features(
																				    __A		,						self.tokenizer		,						max_length=args.max_seq_length		,						label_list=self.labels		,						output_mode=args.glue_output_mode		,						)
																				logger.info('Saving features into cached file %s'		,						__A				)
																				torch.save(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

								_lowerCAmelCase										=self._feature_file(__A				)
								logger.info('Loading features from cached file %s'		,						__A				)
								_lowerCAmelCase										=torch.load(__A				)
								_lowerCAmelCase										=torch.tensor([f.input_ids for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.attention_mask for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.token_type_ids for f in features]		,						dtype=torch.long				)
								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.long				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.float				)

								return DataLoader(
								    TensorDataset(__A		,						__A		,						__A		,						__A				)		,						batch_size=__A		,						shuffle=__A		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=outputs[:2]
								_lowerCAmelCase										=logits.detach().cpu().numpy()
								_lowerCAmelCase										=inputs['labels'].detach().cpu().numpy()

								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=np.argmax(__A		,						axis=1				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=np.squeeze(__A				)

								_lowerCAmelCase										=np.concatenate([x['target'] for x in outputs]		,						axis=0				)
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]

								_lowerCAmelCase										={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,						__A		,						__A				)}

								_lowerCAmelCase										=dict(results.items()				)
								_lowerCAmelCase										=results
								return ret, preds_list, out_label_list






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		@staticmethod
		def 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								BaseTransformer.add_model_specific_args(__A		,						__A				)
								parser.add_argument(
								    '--max_seq_length'		,						default=128		,						type=__A		,						help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )		,						)

								parser.add_argument(
								    '--task'		,						default=''		,						type=__A		,						required=__A		,						help='The GLUE task to run'		,						)
								parser.add_argument(
								    '--gpus'		,						default=0		,						type=__A		,						help='The number of GPUs allocated for this, it is by default 0 meaning none'		,						)

								parser.add_argument(
								    '--overwrite_cache'		,						action='store_true'		,						help='Overwrite the cached training and evaluation sets'				)

								return parser



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

						# If output_dir not provided, a folder will be generated in pwd
						if args.output_dir is None:
												_lowerCAmelCase										=os.path.join(
												    './results'			,      F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'      )}'''			,      )
												os.makedirs(args.output_dir      )

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

						# Optionally, predict on dev set and write to output_dir
						if args.do_predict:
												_lowerCAmelCase										=sorted(glob.glob(os.path.join(args.output_dir			,      'checkpoint-epoch=*.ckpt'      )			,      recursive=a__      )      )
												_lowerCAmelCase										=model.load_from_checkpoint(checkpoints[-1]      )
												return trainer.test(a__      )


if __name__ == "__main__":
							main()





