



'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowercase_      :				Any   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any]						):
						if isinstance(lowercase_ ,  (list, tuple)						) and isinstance(videos[0] ,  (list, tuple)						) and is_valid_image(videos[0][0]						):
												return videos

						elif isinstance(lowercase_ ,  (list, tuple)						) and is_valid_image(videos[0]						):
												return [videos]

						elif is_valid_image(lowercase_						):
												return [[videos]]

						raise ValueError(F"""Could not make batched video from {videos}"""						)




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					['''pixel_values''']







	def __init__(    self ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = PILImageResampling.BILINEAR ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = True ,				_lowerCAmelCase = 1 / 255 ,				_lowerCAmelCase = True ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    None:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)
							lowercase        =							size if size is not None else {"""shortest_edge""": 256}
							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							lowercase        =							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowercase        =							get_size_dict(_lowerCAmelCase ,				param_name="""crop_size"""						)

							lowercase        =							do_resize
							lowercase        =							size
							lowercase        =							do_center_crop
							lowercase        =							crop_size
							lowercase        =							resample
							lowercase        =							do_rescale
							lowercase        =							rescale_factor
							lowercase        =							offset
							lowercase        =							do_normalize
							lowercase        =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase        =							image_std if image_std is not None else IMAGENET_STANDARD_STD







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = PILImageResampling.BILINEAR ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							if "shortest_edge" in size:
													lowercase        =							get_resize_output_image_size(_lowerCAmelCase ,				size["""shortest_edge"""] ,				default_to_square=_lowerCAmelCase						)
							elif "height" in size and "width" in size:
													lowercase        =							(size["""height"""], size["""width"""])
							else:
													raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""						)
							return resize(_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							lowercase        =							get_size_dict(_lowerCAmelCase						)
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""						)
							return center_crop(_lowerCAmelCase ,				size=(size["""height"""], size["""width"""]) ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = True ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							image.astype(np.floataa						)
							if offset:
													lowercase        =							image - (scale / 2)
							return rescale(_lowerCAmelCase ,				scale=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				**_lowerCAmelCase ,				)    ->    np.ndarray:
							'''simple docstring'''






							return normalize(_lowerCAmelCase ,				mean=_lowerCAmelCase ,				std=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				**_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = ChannelDimension.FIRST ,				)    ->    np.ndarray:
							'''simple docstring'''






							if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True."""						)

							if do_center_crop and crop_size is None:
													raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

							if do_rescale and rescale_factor is None:
													raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

							if offset and not do_rescale:
													raise ValueError("""For offset, do_rescale must also be set to True."""						)

							# All transformations expect numpy arrays.
							lowercase        =							to_numpy_array(_lowerCAmelCase						)

							if do_resize:
													lowercase        =							self.resize(image=_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase						)

							if do_center_crop:
													lowercase        =							self.center_crop(_lowerCAmelCase ,				size=_lowerCAmelCase						)

							if do_rescale:
													lowercase        =							self.rescale(image=_lowerCAmelCase ,				scale=_lowerCAmelCase ,				offset=_lowerCAmelCase						)

							if do_normalize:
													lowercase        =							self.normalize(image=_lowerCAmelCase ,				mean=_lowerCAmelCase ,				std=_lowerCAmelCase						)

							lowercase        =							to_channel_dimension_format(_lowerCAmelCase ,				_lowerCAmelCase						)
							return image







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = None ,				_lowerCAmelCase = ChannelDimension.FIRST ,				**_lowerCAmelCase ,				)    ->    PIL.Image.Image:
							'''simple docstring'''






							lowercase        =							do_resize if do_resize is not None else self.do_resize
							lowercase        =							resample if resample is not None else self.resample
							lowercase        =							do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase        =							do_rescale if do_rescale is not None else self.do_rescale
							lowercase        =							rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase        =							offset if offset is not None else self.offset
							lowercase        =							do_normalize if do_normalize is not None else self.do_normalize
							lowercase        =							image_mean if image_mean is not None else self.image_mean
							lowercase        =							image_std if image_std is not None else self.image_std

							lowercase        =							size if size is not None else self.size
							lowercase        =							get_size_dict(_lowerCAmelCase ,				default_to_square=_lowerCAmelCase						)
							lowercase        =							crop_size if crop_size is not None else self.crop_size
							lowercase        =							get_size_dict(_lowerCAmelCase ,				param_name="""crop_size"""						)

							if not valid_images(_lowerCAmelCase						):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray."""						)

							lowercase        =							make_batched(_lowerCAmelCase						)

							lowercase        =							[
							    [
							        self._preprocess_image(
							            image=_lowerCAmelCase ,				do_resize=_lowerCAmelCase ,				size=_lowerCAmelCase ,				resample=_lowerCAmelCase ,				do_center_crop=_lowerCAmelCase ,				crop_size=_lowerCAmelCase ,				do_rescale=_lowerCAmelCase ,				rescale_factor=_lowerCAmelCase ,				offset=_lowerCAmelCase ,				do_normalize=_lowerCAmelCase ,				image_mean=_lowerCAmelCase ,				image_std=_lowerCAmelCase ,				data_format=_lowerCAmelCase ,				)
							        for img in video
							    ]
							    for video in videos
							]

							lowercase        =							{"""pixel_values""": videos}
							return BatchFeature(data=_lowerCAmelCase ,				tensor_type=_lowerCAmelCase						)







'''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,
)


lowercase_      :				Tuple   					=    logging.getLogger(__name__)




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




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







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







def      SCREAMING_SNAKE_CASE				(				):
						# 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.

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

						check_output_dir(lowercase_						)

						# 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""" ,  lowercase_						)

						# 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.

						lowercase        =							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 ,  )

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

						lowercase        =							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 ,  )
						lowercase        =							AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path ,  from_tf=""".ckpt""" in model_args.model_name_or_path ,  config=lowercase_ ,  cache_dir=model_args.cache_dir ,  )

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

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

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

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

						lowercase        =							SeqaSeqDataset

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

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

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

												trainer.save_model()  # this also saves the tokenizer

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

																		# 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 ***"""						)

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

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

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

												lowercase        =							trainer.predict(test_dataset=lowercase_ ,  metric_key_prefix="""test"""						)
												lowercase        =							test_output.metrics
												lowercase        =							data_args.n_test

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

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

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

						return all_metrics







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()


