

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=3					,						_A=3_2					,						_A=3					,						_A=1_0					,						_A=[1_0, 2_0, 3_0, 4_0]					,						_A=[1, 1, 2, 1]					,						_A=True					,						_A=True					,						_A="relu"					,						_A=3					,						_A=None					,						):
												__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        A__  ( self							):
												__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        A__  ( self							):
												return RegNetConfig(
												    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					,						)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							TFRegNetModel(config=_A							)
												__lowerCAmelCase					=							model(_A					,						training=_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 // 3_2, self.image_size // 3_2)					,						)




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							self.num_labels
												__lowerCAmelCase					=							TFRegNetForImageClassification(_A							)
												__lowerCAmelCase					=							model(_A					,						labels=_A					,						training=_A							)
												self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





@require_tf
class     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
							__UpperCAmelCase       =(
							    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TFRegNetModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												return




							@unittest.skip(reason='RegNet does not use inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices('GPU'							)							) == 0					,						reason='TF does not support backprop for grouped convolutions on CPU.'					,						)
							@slow
							def        A__  ( self							):
												super().test_keras_fit()




							@unittest.skip(reason='RegNet does not support input and output embeddings'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__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        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A							)




							def        A__  ( self							):
												def check_hidden_states_output(_A					,						_A					,						_A							):
																	__lowerCAmelCase					=							model_class(_A							)
																	__lowerCAmelCase					=							model(**self._prepare_for_class(_A					,						_A							)					,						training=_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							)

																	# RegNet'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 // 2, self.model_tester.image_size // 2]					,						)

												__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        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												def check_equivalence(_A					,						_A					,						_A					,						_A={}							):
																	__lowerCAmelCase					=							model(_A					,						return_dict=_A					,						**_A							)
																	__lowerCAmelCase					=							model(_A					,						return_dict=_A					,						**_A							).to_tuple()

																	def recursive_check(_A					,						_A							):
																						if isinstance(_A					,						(List, Tuple)							):
																											for tuple_iterable_value, dict_iterable_value in zip(_A					,						_A							):
																																recursive_check(_A					,						_A							)
																						elif tuple_object is None:
																											return
																						else:
																											self.assertTrue(
																											    all(tf.equal(_A					,						_A							)							)					,						msg=(
																											        'Tuple and dict output are not equal. Difference:'
																											        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object							)							)}"""
																											    )					,						)

																						recursive_check(_A					,						_A							)

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	check_equivalence(_A					,						_A					,						_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	check_equivalence(_A					,						_A					,						_A							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
																	check_equivalence(_A					,						_A					,						_A					,						{'output_hidden_states': True}							)

																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A					,						return_labels=_A							)
																	check_equivalence(_A					,						_A					,						_A					,						{'output_hidden_states': True}							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_A							)




							@slow
							def        A__  ( self							):
												for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__lowerCAmelCase					=							TFRegNetModel.from_pretrained(_A							)
																	self.assertIsNotNone(_A							)


def        __lowercase    (  ):
					__lowerCAmelCase					=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
					return image





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


							"""simple docstring"""





							@cached_property
							def        A__  ( self							):
												return (
												    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
												    if is_vision_available()
												    else None
												)




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							TFRegNetForImageClassification.from_pretrained(TF_REGNET_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					,						training=_A							)

												# verify the logits
												__lowerCAmelCase					=							tf.TensorShape((1, 1_0_0_0)							)
												self.assertEqual(outputs.logits.shape					,						_A							)

												__lowerCAmelCase					=							tf.constant([-0.4180, -1.5051, -3.4836]							)

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









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__ = None		,					):

					"""simple docstring"""






					__lowerCAmelCase					=							{}

					if train_file is not None:
										__lowerCAmelCase					=							[train_file]
					if eval_file is not None:
										__lowerCAmelCase					=							[eval_file]
					if test_file is not None:
										__lowerCAmelCase					=							[test_file]

					__lowerCAmelCase					=							datasets.load_dataset('csv'		,					data_files=UpperCAmelCase__      )
					__lowerCAmelCase					=							list(ds[list(files.keys()      )[0]].features.keys()      )
					__lowerCAmelCase					=							features_name.pop(UpperCAmelCase__      )
					__lowerCAmelCase					=							list(set(ds[list(files.keys()      )[0]][label_name]      )      )
					__lowerCAmelCase					=							{label: i for i, label in enumerate(UpperCAmelCase__      )}
					__lowerCAmelCase					=							tokenizer.model_input_names
					__lowerCAmelCase					=							{}

					if len(UpperCAmelCase__      ) == 1:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        example[features_name[0]]		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'      )		,					batched=UpperCAmelCase__		,					)
					elif len(UpperCAmelCase__      ) == 2:
										for k in files.keys():
															__lowerCAmelCase					=							ds[k].map(
															    lambda UpperCAmelCase__      : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])		,					truncation=UpperCAmelCase__		,					max_length=UpperCAmelCase__		,					padding='max_length'		,					)		,					batched=UpperCAmelCase__		,					)

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

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

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

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

					__lowerCAmelCase					=							(
					    tf.data.Dataset.from_generator(
					        UpperCAmelCase__		,					({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:
										__lowerCAmelCase					=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

					return train_ds, val_ds, test_ds, labelaid


lowerCamelCase      				=     logging.getLogger(__name__)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(metadata={"""help""": """Which column contains the label"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the training file"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the development file"""}							)
							__UpperCAmelCase       =field(default=_a			, metadata={"""help""": """The path of the test file"""}							)
							__UpperCAmelCase       =field(
							    default=128			, metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }			, )
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)





@dataclass
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
							__UpperCAmelCase       =field(default=_a			, 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.
							__UpperCAmelCase       =field(
							    default=_a			, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			, )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							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.

					__lowerCAmelCase					=							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		,					)

					__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							get_tfds(
					    train_file=data_args.train_file		,					eval_file=data_args.dev_file		,					test_file=data_args.test_file		,					tokenizer=UpperCAmelCase__		,					label_column_id=data_args.label_column_id		,					max_seq_length=data_args.max_seq_length		,					)

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

					with training_args.strategy.scope():
										__lowerCAmelCase					=							TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path		,					from_pt=bool('.bin' in model_args.model_name_or_path      )		,					config=UpperCAmelCase__		,					cache_dir=model_args.cache_dir		,					)

					def compute_metrics(UpperCAmelCase__      ) -> Dict:
										__lowerCAmelCase					=							np.argmax(p.predictions		,					axis=1      )

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

					# Initialize our Trainer
					__lowerCAmelCase					=							TFTrainer(
					    model=UpperCAmelCase__		,					args=UpperCAmelCase__		,					train_dataset=UpperCAmelCase__		,					eval_dataset=UpperCAmelCase__		,					compute_metrics=UpperCAmelCase__		,					)

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

					# Evaluation
					__lowerCAmelCase					=							{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'      )
										__lowerCAmelCase					=							trainer.evaluate()
										__lowerCAmelCase					=							os.path.join(training_args.output_dir		,					'eval_results.txt'      )

										with open(UpperCAmelCase__		,					'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(UpperCAmelCase__      )

					return results


if __name__ == "__main__":
		main()






