






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,
)


lowerCamelCase			:					List[Any]         =	logging.getLogger(__name__)



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



@dataclass
class 							_UpperCamelCase			:
      snake_case_  =							field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}			)
      snake_case_  =							field(
          default="""summarization"""							,		metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}							,		)
      snake_case_  =							field(
          default=1024							,		metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }							,		)
      snake_case_  =							field(
          default=128							,		metadata={
              """help""": (
                  """The maximum total sequence length for target text after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }							,		)
      snake_case_  =							field(
          default=142							,		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``."""
              )
          }							,		)
      snake_case_  =							field(
          default=142							,		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."""
              )
          }							,		)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# training examples. -1 means use all."""}			)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# validation examples. -1 means use all."""}			)
      snake_case_  =							field(default=-1							,		metadata={"""help""": """# test examples. -1 means use all."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Source language id for translation."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """Target language id for translation."""}			)
      snake_case_  =							field(default=a_							,		metadata={"""help""": """# num_beams to use for evaluation."""}			)
      snake_case_  =							field(
          default=a_							,		metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}							,		)





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

     __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(__snake_case       )

     # 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"					, __snake_case       )

     # 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(__snake_case					, __snake_case					, __snake_case       ):
               assert hasattr(__snake_case					, __snake_case       ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
               setattr(__snake_case					, __snake_case					, getattr(__snake_case					, __snake_case       )       )

     __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=__snake_case					, cache_dir=model_args.cache_dir					, )

     # use task specific params
     use_task_specific_params(__snake_case					, 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(__snake_case					, (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(__snake_case					, __snake_case       ):
               __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(__snake_case       )
     if model_args.freeze_encoder:
          freeze_params(model.get_encoder()       )
          assert_all_frozen(model.get_encoder()       )

     __lowerCAmelCase								=					SeqaSeqDataset

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

     __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"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

               # 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"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

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

          __lowerCAmelCase								=					trainer.predict(test_dataset=__snake_case					, 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"					, __snake_case					, training_args.output_dir       )
               all_metrics.update(__snake_case       )

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

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

     return all_metrics





def       __lowerCAmelCase							(						__snake_case       ):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()














import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					tempfile.mkdtemp()

           # fmt: off
           __lowerCAmelCase								=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
           # fmt: on
           __lowerCAmelCase								=					os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES["vocab_file"]	)
           with open(self.vocab_file					,						"w"					,						encoding="utf-8"	) as vocab_writer:
                vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

           __lowerCAmelCase								=					{
               "do_resize": True,
               "size": {"height": 1_8, "width": 1_8},
               "do_normalize": True,
               "image_mean": [0.5, 0.5, 0.5],
               "image_std": [0.5, 0.5, 0.5],
           }
           __lowerCAmelCase								=					os.path.join(self.tmpdirname					,						__UpperCamelCase	)
           with open(self.image_processor_file					,						"w"					,						encoding="utf-8"	) as fp:
                json.dump(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Optional[Any]:
           return BertTokenizer.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Any:
           return ViTImageProcessor.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           shutil.rmtree(self.tmpdirname	)


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					[np.random.randint(2_5_5					,						size=(3, 3_0, 4_0_0)					,						dtype=np.uinta	)]

           __lowerCAmelCase								=					[Image.fromarray(np.moveaxis(__UpperCamelCase					,						0					,						-1	)	) for x in image_inputs]

           return image_inputs


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           __lowerCAmelCase								=					self.get_tokenizer()
           __lowerCAmelCase								=					self.get_image_processor()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           processor.save_pretrained(self.tmpdirname	)
           __lowerCAmelCase								=					VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

           self.assertEqual(processor.tokenizer.get_vocab()					,						tokenizer.get_vocab()	)
           self.assertIsInstance(processor.tokenizer					,						(BertTokenizer, BertTokenizerFast)	)

           self.assertEqual(processor.image_processor.to_json_string()					,						image_processor.to_json_string()	)
           self.assertIsInstance(processor.image_processor					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	str:
           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(
               tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           __lowerCAmelCase								=					self.get_tokenizer(bos_token="(BOS)"					,						eos_token="(EOS)"	)
           __lowerCAmelCase								=					self.get_image_processor(do_normalize=__UpperCamelCase					,						padding_value=1.0	)

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor.from_pretrained(
               self.tmpdirname					,						bos_token="(BOS)"					,						eos_token="(EOS)"					,						do_normalize=__UpperCamelCase					,						padding_value=1.0	)

           self.assertEqual(processor.tokenizer.get_vocab()					,						tokenizer_add_kwargs.get_vocab()	)
           self.assertIsInstance(processor.tokenizer					,						(BertTokenizer, BertTokenizerFast)	)

           self.assertEqual(processor.image_processor.to_json_string()					,						image_processor_add_kwargs.to_json_string()	)
           self.assertIsInstance(processor.image_processor					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					image_processor(__UpperCamelCase					,						return_tensors="np"	)
           __lowerCAmelCase								=					processor(images=__UpperCamelCase					,						return_tensors="np"	)

           for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()					,						input_processor[key].sum()					,						delta=1e-2	)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"

           __lowerCAmelCase								=					processor(text=__UpperCamelCase	)

           __lowerCAmelCase								=					tokenizer(__UpperCamelCase	)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]					,						encoded_processor[key]	)


      def  __UpperCAmelCase		(	self	)->	List[str]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"
           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					processor(text=__UpperCamelCase					,						images=__UpperCamelCase	)

           self.assertListEqual(list(inputs.keys()	)					,						["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

           # test if it raises when no input is passed
           with self.assertRaises(__UpperCamelCase	):
                processor()


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __lowerCAmelCase								=					processor.batch_decode(__UpperCamelCase	)
           __lowerCAmelCase								=					tokenizer.batch_decode(__UpperCamelCase	)

           self.assertListEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					VisionTextDualEncoderProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"
           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					processor(text=__UpperCamelCase					,						images=__UpperCamelCase	)

           self.assertListEqual(list(inputs.keys()	)					,						processor.model_input_names	)






