
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  =	logging.getLogger(__name__)





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





@dataclass
class       _lowerCAmelCase   :
				_lowercase     						=field(
				    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
				_lowercase     						=field(
				    default='''summarization'''   , metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}   , )
				_lowercase     						=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     						=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     						=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     						=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     						=field(default=-1   , metadata={'''help''': '''# training examples. -1 means use all.'''}			)
				_lowercase     						=field(default=-1   , metadata={'''help''': '''# validation examples. -1 means use all.'''}			)
				_lowercase     						=field(default=-1   , metadata={'''help''': '''# test examples. -1 means use all.'''}			)
				_lowercase     						=field(default=__a   , metadata={'''help''': '''Source language id for translation.'''}			)
				_lowercase     						=field(default=__a   , metadata={'''help''': '''Target language id for translation.'''}			)
				_lowercase     						=field(default=__a   , metadata={'''help''': '''# num_beams to use for evaluation.'''}			)
				_lowercase     						=field(
				    default=__a   , metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}   , )






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


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






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      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.
						lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
			else:
						lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      parser.parse_args_into_dataclasses()

			check_output_dir(__lowerCAmelCase							)

			# 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"       ,				__lowerCAmelCase							)

			# 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.

			lowerCAmelCase_         =      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       ,				)

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

			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_         =      AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path       ,				from_tf=".ckpt" in model_args.model_name_or_path       ,				config=__lowerCAmelCase       ,				cache_dir=model_args.cache_dir       ,				)

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

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

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase       ,				(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(__lowerCAmelCase       ,				__lowerCAmelCase							):
									lowerCAmelCase_         =      tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									lowerCAmelCase_         =      tokenizer.convert_tokens_to_ids(data_args.tgt_lang							)

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

			lowerCAmelCase_         =      SeqaSeqDataset

			# Get datasets
			lowerCAmelCase_         =      (
			    dataset_class(
			        __lowerCAmelCase       ,				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
			)
			lowerCAmelCase_         =      (
			    dataset_class(
			        __lowerCAmelCase       ,				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
			)
			lowerCAmelCase_         =      (
			    dataset_class(
			        __lowerCAmelCase       ,				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
			lowerCAmelCase_         =      (
			    build_compute_metrics_fn(data_args.task       ,				__lowerCAmelCase							) if training_args.predict_with_generate else None
			)
			lowerCAmelCase_         =      SeqaSeqTrainer(
			    model=__lowerCAmelCase       ,				args=__lowerCAmelCase       ,				data_args=__lowerCAmelCase       ,				train_dataset=__lowerCAmelCase       ,				eval_dataset=__lowerCAmelCase       ,				data_collator=SeqaSeqDataCollator(
			        __lowerCAmelCase       ,				__lowerCAmelCase       ,				model.config.decoder_start_token_id       ,				training_args.tpu_num_cores							)       ,				compute_metrics=__lowerCAmelCase       ,				tokenizer=__lowerCAmelCase       ,				)

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

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

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics("train"       ,				__lowerCAmelCase       ,				training_args.output_dir							)
									all_metrics.update(__lowerCAmelCase							)

									# 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 ***"							)

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

						if trainer.is_world_process_zero():
									handle_metrics("val"       ,				__lowerCAmelCase       ,				training_args.output_dir							)
									all_metrics.update(__lowerCAmelCase							)

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

						lowerCAmelCase_         =      trainer.predict(test_dataset=__lowerCAmelCase       ,				metric_key_prefix="test"							)
						lowerCAmelCase_         =      test_output.metrics
						lowerCAmelCase_         =      data_args.n_test

						if trainer.is_world_process_zero():
									lowerCAmelCase_         =      round(metrics["test_loss"]       ,				4							)
									handle_metrics("test"       ,				__lowerCAmelCase       ,				training_args.output_dir							)
									all_metrics.update(__lowerCAmelCase							)

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

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

			return all_metrics






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			main()


if __name__ == "__main__":
			main()





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       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=30							,							_UpperCamelCase=2							,							_UpperCamelCase=3							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=32							,							_UpperCamelCase=5							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=10							,							_UpperCamelCase=0.02							,							_UpperCamelCase=None							,							)							-> List[Any]:
							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

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




				def 	__a		(   self	)							-> Union[str, Any]:
							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	)							-> Optional[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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      ViTMSNModel(config=_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.type_sequence_label_size
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()
							lowerCAmelCase_         =      model(_UpperCamelCase							,							labels=_UpperCamelCase	)
							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
							lowerCAmelCase_         =      1
							lowerCAmelCase_         =      ViTMSNForImageClassification(_UpperCamelCase	)
							model.to(_UpperCamelCase	)
							model.eval()

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      config_and_inputs
							lowerCAmelCase_         =      {"pixel_values": pixel_values}
							return config, inputs_dict





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

				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      ViTMSNModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							has_text_modality=_UpperCamelCase							,							hidden_size=37	)




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




				@unittest.skip(reason="ViTMSN does not use inputs_embeds"	)
				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_      ,    lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

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




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

							for model_class in self.all_model_classes:
										lowerCAmelCase_         =      model_class(_UpperCamelCase	)
										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]							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      ViTMSNModel.from_pretrained(_UpperCamelCase	)
										self.assertIsNotNone(_UpperCamelCase	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


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





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




				@cached_property
				def 	__a		(   self	)							-> List[str]:
							return ViTImageProcessor.from_pretrained("facebook/vit-msn-small"	) if is_vision_available() else None




				@slow
				def 	__a		(   self	)							-> int:
							torch.manual_seed(2	)
							lowerCAmelCase_         =      ViTMSNForImageClassification.from_pretrained("facebook/vit-msn-small"	).to(_UpperCamelCase	)

							lowerCAmelCase_         =      self.default_image_processor
							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_UpperCamelCase							,							return_tensors="pt"	).to(_UpperCamelCase	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(**_UpperCamelCase	)

							# verify the logits
							lowerCAmelCase_         =      torch.Size((1, 1_000)	)
							self.assertEqual(outputs.logits.shape							,							_UpperCamelCase	)

							lowerCAmelCase_         =      torch.tensor([-0.0803, -0.4454, -0.2375]	).to(_UpperCamelCase	)

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



