"""simple docstring"""


import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
	from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase=13      ,					_lowerCamelCase=64      ,					_lowerCamelCase=2      ,					_lowerCamelCase=3      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=32      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=37      ,					_lowerCamelCase="gelu"      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=10      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=[1, 16, 4, 4]      ,					_lowerCamelCase=None      ,					):
										lowerCamelCase__   =parent
										lowerCamelCase__   =batch_size
										lowerCamelCase__   =image_size
										lowerCamelCase__   =patch_size
										lowerCamelCase__   =num_channels
										lowerCamelCase__   =is_training
										lowerCamelCase__   =use_labels
										lowerCamelCase__   =hidden_size
										lowerCamelCase__   =num_hidden_layers
										lowerCamelCase__   =num_attention_heads
										lowerCamelCase__   =intermediate_size
										lowerCamelCase__   =hidden_act
										lowerCamelCase__   =hidden_dropout_prob
										lowerCamelCase__   =attention_probs_dropout_prob
										lowerCamelCase__   =type_sequence_label_size
										lowerCamelCase__   =initializer_range
										lowerCamelCase__   =scope
										lowerCamelCase__   =backbone_featmap_shape

										# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										# the number of patches is based on the feature map of the backbone, which by default uses an output stride
										# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
										lowerCamelCase__   =(self.image_size // 32) ** 2
										lowerCamelCase__   =num_patches + 1

				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.type_sequence_label_size )

										lowerCamelCase__   =self.get_config()

										return config, pixel_values, labels

				def 						_a      ( self ):
										lowerCamelCase__   ={
										    "global_padding": "same",
										    "layer_type": "bottleneck",
										    "depths": [3, 4, 9],
										    "out_features": ["stage1", "stage2", "stage3"],
										    "embedding_dynamic_padding": True,
										    "hidden_sizes": [4, 8, 16, 32],
										    "num_groups": 2,
										}

										return ViTHybridConfig(
										    image_size=self.image_size      ,					patch_size=self.patch_size      ,					num_channels=self.num_channels      ,					hidden_size=self.hidden_size      ,					num_hidden_layers=self.num_hidden_layers      ,					num_attention_heads=self.num_attention_heads      ,					intermediate_size=self.intermediate_size      ,					hidden_act=self.hidden_act      ,					hidden_dropout_prob=self.hidden_dropout_prob      ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,					is_decoder=A_      ,					initializer_range=self.initializer_range      ,					backbone_featmap_shape=self.backbone_featmap_shape      ,					backbone_config=A_      ,					)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =ViTHybridModel(config=A_ )
										model.to(A_ )
										model.eval()
										lowerCamelCase__   =model(A_ )
										self.parent.assertEqual(result.last_hidden_state.shape      ,					(self.batch_size, self.seq_length, self.hidden_size) )

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =self.type_sequence_label_size
										lowerCamelCase__   =ViTHybridForImageClassification(A_ )
										model.to(A_ )
										model.eval()
										lowerCamelCase__   =model(A_      ,					labels=A_ )
										self.parent.assertEqual(result.logits.shape      ,					(self.batch_size, self.type_sequence_label_size) )






				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_torch
class   __UpperCAmelCase		(      __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase      ):
				A__ :  Tuple    				=			(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
				A__ :  Any    				=			(
				    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
				    if is_torch_available()
				    else {}
				)
				A__ :  Union[str, Any]    				=			False
				A__ :  Optional[Any]    				=			False
				A__ :  int    				=			False

				def 						_a      ( self ):
										lowerCamelCase__   =ViTHybridModelTester(self )
										lowerCamelCase__   =ConfigTester(self      ,					config_class=A_      ,					has_text_modality=A_      ,					hidden_size=37 )

				def 						_a      ( self ):
										self.config_tester.run_common_tests()

				@unittest.skip(reason="ViT does not use inputs_embeds" )
				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_ )
																self.assertIsInstance(model.get_input_embeddings()      ,					(nn.Module) )
																lowerCamelCase__   =model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(A_      ,					nn.Linear ) )

				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.forward )
																# 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 ):
										lowerCamelCase__   =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_ )

				def 						_a      ( self ):
										lowerCamelCase__   , lowerCamelCase__   =self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase__   =_config_zero_init(A_ )
										for model_class in self.all_model_classes:
																lowerCamelCase__   =model_class(config=A_ )
																# Skip the check for the backbone
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																												lowerCamelCase__   =[F'''{name}.{key}''' for key in module.state_dict().keys()]
																												break

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






				@slow
				def 						_a      ( self ):

										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowerCamelCase__   =ViTHybridModel.from_pretrained(A_ )
																self.assertIsNotNone(A_ )





def      lowerCamelCase_					(   )				->   Optional[Any]:




						'''simple docstring'''



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






@require_torch
@require_vision
class   __UpperCAmelCase		(      unittest.TestCase      ):

				@cached_property
				def 						_a      ( self ):
										return (
										    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
										    if is_vision_available()
										    else None
										)

				@slow
				def 						_a      ( self ):
										lowerCamelCase__   =ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
										    A_ )

										lowerCamelCase__   =self.default_image_processor
										lowerCamelCase__   =prepare_img()
										lowerCamelCase__   =image_processor(images=A_      ,					return_tensors="pt" ).to(A_ )

										# forward pass
										with torch.no_grad():
																lowerCamelCase__   =model(**A_ )

										# verify the logits
										lowerCamelCase__   =torch.Size((1, 1000) )
										self.assertEqual(outputs.logits.shape      ,					A_ )

										lowerCamelCase__   =torch.tensor([-1.9_0_9_0, -0.4_9_9_3, -0.2_3_8_9] ).to(A_ )

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






				@slow
				@require_accelerate
				def 						_a      ( self ):
										lowerCamelCase__   =ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384" )
										lowerCamelCase__   =ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"      ,					device_map="auto" )

										lowerCamelCase__   =prepare_img()

										lowerCamelCase__   =image_processor(images=A_      ,					return_tensors="pt" )
										lowerCamelCase__   =model(**A_ )
										lowerCamelCase__   =outputs.logits
										# model predicts one of the 1000 ImageNet classes
										lowerCamelCase__   =logits.argmax(-1 ).item()

										self.assertTrue(model.config.idalabel[predicted_class_idx]      ,					"tabby, tabby cat" )









import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_A					:				List[str]				=			logging.getLogger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	"""token-classification"""



    def __init__(       self   ,				A_     ):





        '''simple docstring'''

        if type(A_     ) == dict:
            SCREAMING_SNAKE_CASE__											=			Namespace(**A_     )
        SCREAMING_SNAKE_CASE__											=			import_module('''tasks'''     )
        try:
            SCREAMING_SNAKE_CASE__											=			getattr(A_   ,				hparams.task_type     )
            SCREAMING_SNAKE_CASE__											=			token_classification_task_clazz()
        except AttributeError:
            raise ValueError(
                f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
                f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''     )
        SCREAMING_SNAKE_CASE__											=			self.token_classification_task.get_labels(hparams.labels     )
        SCREAMING_SNAKE_CASE__											=			CrossEntropyLoss().ignore_index
        super().__init__(A_   ,				len(self.labels     )   ,				self.mode     )



    def  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

        return self.model(**A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__											=			outputs[0]
        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.hparams
        for mode in ["train", "dev", "test"]:
            SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
            if os.path.exists(A_     ) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s'''   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
            else:
                logger.info('''Creating features from dataset file at %s'''   ,				args.data_dir     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.read_examples_from_file(args.data_dir   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			self.token_classification_task.convert_examples_to_features(
                    A_   ,				self.labels   ,				args.max_seq_length   ,				self.tokenizer   ,				cls_token_at_end=bool(self.config.model_type in ['''xlnet''']     )   ,				cls_token=self.tokenizer.cls_token   ,				cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0   ,				sep_token=self.tokenizer.sep_token   ,				sep_token_extra=A_   ,				pad_on_left=bool(self.config.model_type in ['''xlnet''']     )   ,				pad_token=self.tokenizer.pad_token_id   ,				pad_token_segment_id=self.tokenizer.pad_token_type_id   ,				pad_token_label_id=self.pad_token_label_id   ,				)
                logger.info('''Saving features into cached file %s'''   ,				A_     )
                torch.save(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = False     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._feature_file(A_     )
        logger.info('''Loading features from cached file %s'''   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			torch.load(A_     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.input_ids for f in features]   ,				dtype=torch.long     )
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.attention_mask for f in features]   ,				dtype=torch.long     )
        if features[0].token_type_ids is not None:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([f.token_type_ids for f in features]   ,				dtype=torch.long     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.tensor([0 for f in features]   ,				dtype=torch.long     )
            # HACK(we will not use this anymore soon)
        SCREAMING_SNAKE_CASE__											=			torch.tensor([f.label_ids for f in features]   ,				dtype=torch.long     )
        return DataLoader(
            TensorDataset(A_   ,				A_   ,				A_   ,				A_     )   ,				batch_size=A_     )



    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        """Compute validation""" ""
        SCREAMING_SNAKE_CASE__											=			{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            SCREAMING_SNAKE_CASE__											=			(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        SCREAMING_SNAKE_CASE__											=			self(**A_     )
        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			outputs[:2]
        SCREAMING_SNAKE_CASE__											=			logits.detach().cpu().numpy()
        SCREAMING_SNAKE_CASE__											=			inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			torch.stack([x['''val_loss'''] for x in outputs]     ).mean()
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''pred'''] for x in outputs]   ,				axis=0     )
        SCREAMING_SNAKE_CASE__											=			np.argmax(A_   ,				axis=2     )
        SCREAMING_SNAKE_CASE__											=			np.concatenate([x['''target'''] for x in outputs]   ,				axis=0     )

        SCREAMING_SNAKE_CASE__											=			dict(enumerate(self.labels     )     )
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]
        SCREAMING_SNAKE_CASE__											=			[[] for _ in range(out_label_ids.shape[0]     )]

        for i in range(out_label_ids.shape[0]     ):
            for j in range(out_label_ids.shape[1]     ):
                if out_label_ids[i, j] != self.pad_token_label_id:
                    out_label_list[i].append(label_map[out_label_ids[i][j]]     )
                    preds_list[i].append(label_map[preds[i][j]]     )

        SCREAMING_SNAKE_CASE__											=			{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(A_   ,				A_     ),
            '''precision''': precision_score(A_   ,				A_     ),
            '''recall''': recall_score(A_   ,				A_     ),
            '''f1''': fa_score(A_   ,				A_     ),
        }

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )
        SCREAMING_SNAKE_CASE__											=			ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self._eval_end(A_     )

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       A_   ,				A_     ):





        '''simple docstring'''

        BaseTransformer.add_model_specific_args(A_   ,				A_     )
        parser.add_argument(
            '''--task_type'''   ,				default='''NER'''   ,				type=A_   ,				help='''Task type to fine tune in training (e.g. NER, POS, etc)'''     )
        parser.add_argument(
            '''--max_seq_length'''   ,				default=1_28   ,				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(
            '''--labels'''   ,				default=''''''   ,				type=A_   ,				help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''   ,				)
        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


if __name__ == "__main__":
  _A					:				List[str]				=			argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A					:				List[Any]				=			NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A					:				Optional[Any]				=			parser.parse_args()
  _A					:				Any				=			NERTransformer(args)
  _A					:				Any				=			generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    _A					:				Optional[int]				=			sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A					:				Optional[int]				=			model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)
