






import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMSNForImageClassification, ViTMSNModel
				from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class 		_snake_case  :






			def __init__(       self		,					a		,					a=13		,					a=30		,					a=2		,					a=3		,					a=True		,					a=True		,					a=32		,					a=5		,					a=4		,					a=37		,					a="gelu"		,					a=0.1		,					a=0.1		,					a=10		,					a=0.02		,					a=None		,					)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= patch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= intermediate_size
						SCREAMING_SNAKE_CASE						= hidden_act
						SCREAMING_SNAKE_CASE						= hidden_dropout_prob
						SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE						= type_sequence_label_size
						SCREAMING_SNAKE_CASE						= initializer_range
						SCREAMING_SNAKE_CASE						= scope

						# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						SCREAMING_SNAKE_CASE						= (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE						= num_patches + 1






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

						SCREAMING_SNAKE_CASE						= self.get_config()

						return config, pixel_values, labels






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return ViTMSNConfig(
						    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		,					initializer_range=self.initializer_range		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= ViTMSNModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.type_sequence_label_size
						SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification(a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a		,					labels=a)
						print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
						print('Labels: {labels}')
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size))

						# test greyscale images
						SCREAMING_SNAKE_CASE						= 1
						SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {'pixel_values': pixel_values}
						return config, inputs_dict



@require_torch
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    Tuple			     =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
			_lowercase		:    Tuple			     =							(
			    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_lowercase		:    str			     =							False
			_lowercase		:    List[Any]			     =							False
			_lowercase		:    Any			     =							False
			_lowercase		:    str			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= ViTMSNModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					has_text_modality=a		,					hidden_size=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						self.config_tester.run_common_tests()






			@unittest.skip(reason='ViTMSN does not use inputs_embeds')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)
									self.assertIsInstance(model.get_input_embeddings()		,					(nn.Module))
									SCREAMING_SNAKE_CASE						= model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(a		,					nn.Linear))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE						= ['pixel_values']
									self.assertListEqual(arg_names[:1]		,					a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*a)







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= ViTMSNModel.from_pretrained(a)
									self.assertIsNotNone(a)






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







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






			@cached_property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						torch.manual_seed(2)
						SCREAMING_SNAKE_CASE						= ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(a)

						SCREAMING_SNAKE_CASE						= self.default_image_processor
						SCREAMING_SNAKE_CASE						= prepare_img()
						SCREAMING_SNAKE_CASE						= image_processor(images=a		,					return_tensors='pt').to(a)

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= model(**a)

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

						SCREAMING_SNAKE_CASE						= torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(a)

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












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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


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

@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default=A__   ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,    )
			_lowercase		:    bool			     =							field(default=A__   ,    metadata={'''help''': '''Whether tp freeze the encoder.'''}					)
			_lowercase		:    bool			     =							field(default=A__   ,    metadata={'''help''': '''Whether  to freeze the embeddings.'''}					)



@dataclass
class 		_snake_case  :
			_lowercase		:    str			     =							field(
			    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}					)
			_lowercase		:    Optional[str]			     =							field(
			    default='''summarization'''   ,    metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=10_24   ,    metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_28   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded. '''
			            '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
			            '''during ``evaluate`` and ``predict``.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(
			    default=1_42   ,    metadata={
			        '''help''': (
			            '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }   ,    )
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# training examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# validation examples. -1 means use all.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=-1   ,    metadata={'''help''': '''# test examples. -1 means use all.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Source language id for translation.'''}					)
			_lowercase		:    Optional[str]			     =							field(default=A__   ,    metadata={'''help''': '''Target language id for translation.'''}					)
			_lowercase		:    Optional[int]			     =							field(default=A__   ,    metadata={'''help''': '''# num_beams to use for evaluation.'''}					)
			_lowercase		:    bool			     =							field(
			    default=A__   ,    metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}   ,    )






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			logger.info(F'''***** {split} metrics *****''')
			for key in sorted(metrics.keys()):
						logger.info(F'''  {key} = {metrics[key]}''')
			save_json(_UpperCAmelCase							,       os.path.join(_UpperCAmelCase							,       F'''{split}_results.json'''))






def 				lowerCamelCase__				():
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			SCREAMING_SNAKE_CASE						= HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments))

			if len(sys.argv) == 2 and sys.argv[1].endswith('.json'):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= parser.parse_args_into_dataclasses()

			check_output_dir(_UpperCAmelCase)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,       )
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,       training_args.local_rank							,       training_args.device							,       training_args.n_gpu							,       bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED)							,       training_args.fpaa							,       )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank):
						transformers.utils.logging.set_verbosity_info()
			logger.info('Training/evaluation parameters %s'							,       _UpperCAmelCase)

			# Set seed
			set_seed(training_args.seed)

			# 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						= AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       cache_dir=model_args.cache_dir							,       )

			SCREAMING_SNAKE_CASE						= ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
			for p in extra_model_params:
						if getattr(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
									assert hasattr(_UpperCAmelCase							,       _UpperCAmelCase), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
									setattr(_UpperCAmelCase							,       _UpperCAmelCase							,       getattr(_UpperCAmelCase							,       _UpperCAmelCase))

			SCREAMING_SNAKE_CASE						= 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						= AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path							,       from_tf='.ckpt' in model_args.model_name_or_path							,       config=_UpperCAmelCase							,       cache_dir=model_args.cache_dir							,       )

			# use task specific params
			use_task_specific_params(_UpperCAmelCase							,       data_args.task)

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						SCREAMING_SNAKE_CASE						= model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(_UpperCAmelCase							,       (MBartTokenizer, MBartTokenizerFast)):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(_UpperCAmelCase							,       _UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									SCREAMING_SNAKE_CASE						= tokenizer.convert_tokens_to_ids(data_args.tgt_lang)

			if model_args.freeze_embeds:
						freeze_embeds(_UpperCAmelCase)
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder())
						assert_all_frozen(model.get_encoder())

			SCREAMING_SNAKE_CASE						= SeqaSeqDataset

			# Get datasets
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='train'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_train							,       max_target_length=data_args.max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_train
			    else None
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='val'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_val							,       max_target_length=data_args.val_max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			SCREAMING_SNAKE_CASE						= (
			    dataset_class(
			        _UpperCAmelCase							,       type_path='test'							,       data_dir=data_args.data_dir							,       n_obs=data_args.n_test							,       max_target_length=data_args.test_max_target_length							,       max_source_length=data_args.max_source_length							,       prefix=model.config.prefix or ''							,       )
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE						= (
			    build_compute_metrics_fn(data_args.task							,       _UpperCAmelCase) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE						= SeqaSeqTrainer(
			    model=_UpperCAmelCase							,       args=_UpperCAmelCase							,       data_args=_UpperCAmelCase							,       train_dataset=_UpperCAmelCase							,       eval_dataset=_UpperCAmelCase							,       data_collator=SeqaSeqDataCollator(
			        _UpperCAmelCase							,       _UpperCAmelCase							,       model.config.decoder_start_token_id							,       training_args.tpu_num_cores)							,       compute_metrics=_UpperCAmelCase							,       tokenizer=_UpperCAmelCase							,       )

			SCREAMING_SNAKE_CASE						= {}
			# Training
			if training_args.do_train:
						logger.info('*** Train ***')

						SCREAMING_SNAKE_CASE						= trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
						SCREAMING_SNAKE_CASE						= train_result.metrics
						SCREAMING_SNAKE_CASE						= data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics('train'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir							,       'trainer_state.json'))

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
			if training_args.do_eval:
						logger.info('*** Evaluate ***')

						SCREAMING_SNAKE_CASE						= trainer.evaluate(metric_key_prefix='val')
						SCREAMING_SNAKE_CASE						= data_args.n_val
						SCREAMING_SNAKE_CASE						= round(metrics['val_loss']							,       4)

						if trainer.is_world_process_zero():
									handle_metrics('val'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

			if training_args.do_predict:
						logger.info('*** Predict ***')

						SCREAMING_SNAKE_CASE						= trainer.predict(test_dataset=_UpperCAmelCase							,       metric_key_prefix='test')
						SCREAMING_SNAKE_CASE						= test_output.metrics
						SCREAMING_SNAKE_CASE						= data_args.n_test

						if trainer.is_world_process_zero():
									SCREAMING_SNAKE_CASE						= round(metrics['test_loss']							,       4)
									handle_metrics('test'							,       _UpperCAmelCase							,       training_args.output_dir)
									all_metrics.update(_UpperCAmelCase)

									if training_args.predict_with_generate:
												SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(
												    test_output.predictions							,       skip_special_tokens=_UpperCAmelCase							,       clean_up_tokenization_spaces=_UpperCAmelCase)
												SCREAMING_SNAKE_CASE						= lmap(str.strip							,       _UpperCAmelCase)
												write_txt_file(_UpperCAmelCase							,       os.path.join(training_args.output_dir							,       'test_generations.txt'))

			if trainer.is_world_process_zero():
						save_json(_UpperCAmelCase							,       os.path.join(training_args.output_dir							,       'all_results.json'))

			return all_metrics






def 				lowerCamelCase__				(_UpperCAmelCase):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
				main()




