






import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
			SCREAMING_SNAKE_CASE							=			Image.open(requests.get(_UpperCAmelCase				,							stream=_UpperCAmelCase).raw).convert('RGB')

			return image






def 					lowerCamelCase__					(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			[]
			# fmt: off

			# vision encoder
			rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding'))
			rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding'))
			rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight'))
			rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias'))
			rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight'))
			rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias'))

			for i in range(config.vision_config.num_hidden_layers):
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',))
						rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight'''))
						rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias'''))

			# QFormer
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight'))
			rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias'))

			# fmt: on
			return rename_keys






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			dct.pop(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			val






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			for i in range(config.vision_config.num_hidden_layers):
						# read in original q and v biases
						SCREAMING_SNAKE_CASE							=			state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias''')
						SCREAMING_SNAKE_CASE							=			state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias''')

						# next, set bias in the state dict
						SCREAMING_SNAKE_CASE							=			torch.cat((q_bias, torch.zeros_like(_UpperCAmelCase				,							requires_grad=_UpperCAmelCase), v_bias))
						SCREAMING_SNAKE_CASE							=			qkv_bias






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			364 if 'coco' in model_name else 224
			SCREAMING_SNAKE_CASE							=			BlipaVisionConfig(image_size=_UpperCAmelCase).to_dict()

			# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
			# seems like flan-T5 models don't have bos_token_id properly set?
			if "opt-2.7b" in model_name:
						SCREAMING_SNAKE_CASE							=			OPTConfig.from_pretrained('facebook/opt-2.7b'				,							eos_token_id=_UpperCAmelCase).to_dict()
			elif "opt-6.7b" in model_name:
						SCREAMING_SNAKE_CASE							=			OPTConfig.from_pretrained('facebook/opt-6.7b'				,							eos_token_id=_UpperCAmelCase).to_dict()
			elif "t5-xl" in model_name:
						SCREAMING_SNAKE_CASE							=			TaConfig.from_pretrained('google/flan-t5-xl'				,							dense_act_fn='gelu'				,							bos_token_id=1).to_dict()
			elif "t5-xxl" in model_name:
						SCREAMING_SNAKE_CASE							=			TaConfig.from_pretrained('google/flan-t5-xxl'				,							dense_act_fn='gelu'				,							bos_token_id=1).to_dict()

			SCREAMING_SNAKE_CASE							=			BlipaConfig(vision_config=_UpperCAmelCase				,							text_config=_UpperCAmelCase)

			return config, image_size






@torch.no_grad()
def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase=None				,							_UpperCAmelCase=False):
			SCREAMING_SNAKE_CASE							=			(
			    AutoTokenizer.from_pretrained('facebook/opt-2.7b')
			    if 'opt' in model_name
			    else AutoTokenizer.from_pretrained('google/flan-t5-xl')
			)
			SCREAMING_SNAKE_CASE							=			tokenizer('\n'				,							add_special_tokens=_UpperCAmelCase).input_ids[0]
			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			get_blipa_config(_UpperCAmelCase				,							eos_token_id=_UpperCAmelCase)

			SCREAMING_SNAKE_CASE							=			BlipaForConditionalGeneration(_UpperCAmelCase).eval()

			SCREAMING_SNAKE_CASE							=			{
			    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
			    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
			    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
			    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
			    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
			    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
			    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
			}

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			model_name_to_original[model_name]

			# load original model
			print('Loading original model...')
			SCREAMING_SNAKE_CASE							=			'cuda' if torch.cuda.is_available() else 'cpu'
			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			load_model_and_preprocess(
			    name=_UpperCAmelCase				,							model_type=_UpperCAmelCase				,							is_eval=_UpperCAmelCase				,							device=_UpperCAmelCase)
			original_model.eval()
			print('Done!')

			# update state dict keys
			SCREAMING_SNAKE_CASE							=			original_model.state_dict()
			SCREAMING_SNAKE_CASE							=			create_rename_keys(_UpperCAmelCase)
			for src, dest in rename_keys:
						rename_key(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						SCREAMING_SNAKE_CASE							=			state_dict.pop(_UpperCAmelCase)
						if key.startswith('Qformer.bert'):
									SCREAMING_SNAKE_CASE							=			key.replace('Qformer.bert'				,							'qformer')
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE							=			key.replace('self'				,							'attention')
						if "opt_proj" in key:
									SCREAMING_SNAKE_CASE							=			key.replace('opt_proj'				,							'language_projection')
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE							=			key.replace('t5_proj'				,							'language_projection')
						if key.startswith('opt'):
									SCREAMING_SNAKE_CASE							=			key.replace('opt'				,							'language')
						if key.startswith('t5'):
									SCREAMING_SNAKE_CASE							=			key.replace('t5'				,							'language')
						SCREAMING_SNAKE_CASE							=			val

			# read in qv biases
			read_in_q_v_bias(_UpperCAmelCase				,							_UpperCAmelCase)

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			hf_model.load_state_dict(_UpperCAmelCase				,							strict=_UpperCAmelCase)
			assert len(_UpperCAmelCase) == 0
			assert unexpected_keys == ["qformer.embeddings.position_ids"]

			SCREAMING_SNAKE_CASE							=			load_demo_image()
			SCREAMING_SNAKE_CASE							=			vis_processors['eval'](_UpperCAmelCase).unsqueeze(0).to(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			tokenizer(['\n']				,							return_tensors='pt').input_ids.to(_UpperCAmelCase)

			# create processor
			SCREAMING_SNAKE_CASE							=			BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}				,							image_mean=_UpperCAmelCase				,							image_std=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			BlipaProcessor(image_processor=_UpperCAmelCase				,							tokenizer=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			processor(images=_UpperCAmelCase				,							return_tensors='pt').pixel_values.to(_UpperCAmelCase)

			# make sure processor creates exact same pixel values
			assert torch.allclose(_UpperCAmelCase				,							_UpperCAmelCase)

			original_model.to(_UpperCAmelCase)
			hf_model.to(_UpperCAmelCase)
			with torch.no_grad():
						if "opt" in model_name:
									SCREAMING_SNAKE_CASE							=			original_model({'image': original_pixel_values, 'text_input': ['']}).logits
									SCREAMING_SNAKE_CASE							=			hf_model(_UpperCAmelCase				,							_UpperCAmelCase).logits
						else:
									SCREAMING_SNAKE_CASE							=			original_model(
									    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}).logits
									SCREAMING_SNAKE_CASE							=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id				,							-100)
									SCREAMING_SNAKE_CASE							=			hf_model(_UpperCAmelCase				,							_UpperCAmelCase				,							labels=_UpperCAmelCase).logits

			assert original_logits.shape == logits.shape
			print('First values of original logits:'				,							original_logits[0, :3, :3])
			print('First values of HF logits:'				,							logits[0, :3, :3])

			# assert values
			if model_name == "blip2-flan-t5-xl":
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]				,							device=_UpperCAmelCase)
						assert torch.allclose(logits[0, :3, :3]				,							_UpperCAmelCase				,							atol=1e-4)
			elif model_name == "blip2-flan-t5-xl-coco":
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]				,							device=_UpperCAmelCase)
			else:
						# cast to same type
						SCREAMING_SNAKE_CASE							=			logits.dtype
						assert torch.allclose(original_logits.to(_UpperCAmelCase)				,							_UpperCAmelCase				,							atol=1e-2)
			print('Looks ok!')

			print('Generating a caption...')
			SCREAMING_SNAKE_CASE							=			''
			SCREAMING_SNAKE_CASE							=			tokenizer(_UpperCAmelCase				,							return_tensors='pt').input_ids.to(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE							=			original_model.generate({'image': original_pixel_values})
			SCREAMING_SNAKE_CASE							=			hf_model.generate(
			    _UpperCAmelCase				,							_UpperCAmelCase				,							do_sample=_UpperCAmelCase				,							num_beams=5				,							max_length=30				,							min_length=1				,							top_p=0.9				,							repetition_penalty=1.0				,							length_penalty=1.0				,							temperature=1				,							)
			print('Original generation:'				,							_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			input_ids.shape[1]
			SCREAMING_SNAKE_CASE							=			processor.batch_decode(outputs[:, prompt_length:]				,							skip_special_tokens=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			[text.strip() for text in output_text]
			print('HF generation:'				,							_UpperCAmelCase)

			if pytorch_dump_folder_path is not None:
						processor.save_pretrained(_UpperCAmelCase)
						hf_model.save_pretrained(_UpperCAmelCase)

			if push_to_hub:
						processor.push_to_hub(F'''nielsr/{model_name}''')
						hf_model.push_to_hub(F'''nielsr/{model_name}''')


if __name__ == "__main__":
				a_		:    str   							=     argparse.ArgumentParser()
				a_		:    Optional[int]   							=     [
				    'blip2-opt-2.7b',
				    'blip2-opt-6.7b',
				    'blip2-opt-2.7b-coco',
				    'blip2-opt-6.7b-coco',
				    'blip2-flan-t5-xl',
				    'blip2-flan-t5-xl-coco',
				    'blip2-flan-t5-xxl',
				]
				parser.add_argument(
				    '--model_name',
				    default='blip2-opt-2.7b',
				    choices=choices,
				    type=str,
				    help='Path to hf config.json of model to convert',
				)
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument(
				    '--push_to_hub',
				    action='store_true',
				    help='Whether to push the model and processor to the hub after converting',
				)

				a_		:    int   							=     parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''







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     :  Optional[Any]											=    logging.getLogger(__name__)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






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



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
     __magic_name__           =     field(
         default="summarization"     ,       metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}     ,       )
     __magic_name__           =     field(
         default=1_0_2_4     ,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_2_8     ,       metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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``."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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."
             )
         }     ,       )
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# training examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# validation examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# test examples. -1 means use all."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Source language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Target language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "# num_beams to use for evaluation."}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}     ,       )






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				int       =							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     :				Tuple       =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
       else:
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							parser.parse_args_into_dataclasses()

       check_output_dir(_A       )

       # 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' ,    _A       )

       # 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     :				Optional[int]       =							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     :				Tuple       =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
       for p in extra_model_params:
              if getattr(_A ,    _A ,    _A       ):
                     assert hasattr(_A ,    _A       ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
                     setattr(_A ,    _A ,    getattr(_A ,    _A       )       )

       _lowerCAmelCase     :				Any       =							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     :				str       =							AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path ,    from_tf='.ckpt' in model_args.model_name_or_path ,    config=_A ,    cache_dir=model_args.cache_dir ,    )

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

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              _lowerCAmelCase     :				Union[str, Any]       =							model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(_A ,    (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(_A ,    _A       ):
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

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

       _lowerCAmelCase     :				Any       =							SeqaSeqDataset

       # Get datasets
       _lowerCAmelCase     :				List[Any]       =							(
           dataset_class(
               _A ,    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     :				Optional[Any]       =							(
           dataset_class(
               _A ,    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     :				Union[str, Any]       =							(
           dataset_class(
               _A ,    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     :				Optional[Any]       =							(
           build_compute_metrics_fn(data_args.task ,    _A       ) if training_args.predict_with_generate else None
       )
       _lowerCAmelCase     :				int       =							SeqaSeqTrainer(
           model=_A ,    args=_A ,    data_args=_A ,    train_dataset=_A ,    eval_dataset=_A ,    data_collator=SeqaSeqDataCollator(
               _A ,    _A ,    model.config.decoder_start_token_id ,    training_args.tpu_num_cores       ) ,    compute_metrics=_A ,    tokenizer=_A ,    )

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

              _lowerCAmelCase     :				Dict       =							trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
              _lowerCAmelCase     :				Any       =							train_result.metrics
              _lowerCAmelCase     :				str       =							data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

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

                     # 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     :				List[str]       =							trainer.evaluate(metric_key_prefix='val'       )
              _lowerCAmelCase     :				int       =							data_args.n_val
              _lowerCAmelCase     :				List[str]       =							round(metrics['val_loss'] ,    4       )

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

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

              _lowerCAmelCase     :				Any       =							trainer.predict(test_dataset=_A ,    metric_key_prefix='test'       )
              _lowerCAmelCase     :				Optional[int]       =							test_output.metrics
              _lowerCAmelCase     :				Optional[int]       =							data_args.n_test

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

                     if training_args.predict_with_generate:
                            _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.batch_decode(
                                test_output.predictions ,    skip_special_tokens=_A ,    clean_up_tokenization_spaces=_A       )
                            _lowerCAmelCase     :				List[Any]       =							lmap(str.strip ,    _A       )
                            write_txt_file(_A ,    os.path.join(training_args.output_dir ,    'test_generations.txt'       )       )

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

       return all_metrics






def       lowercase					(_A       ):







       """simple docstring"""







       main()


if __name__ == "__main__":
       main()

