




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


_lowerCamelCase      :  List[Any]      						=						logging.get_logger(__name__)
class        __snake_case  (_a		):
				lowerCAmelCase__				=     ["pixel_values"]



				def __init__(      self      :		int		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Union[int, float] = 1 / 255		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		bool = True		,	**_UpperCAmelCase      :		Optional[int]		,	)  ->    None:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      size if size is not None else {"""height""": 384, """width""": 384}
								_lowerCAmelCase    :    Optional[int]										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )

								_lowerCAmelCase    :    int										=      do_resize
								_lowerCAmelCase    :    Dict										=      size
								_lowerCAmelCase    :    Optional[Any]										=      resample
								_lowerCAmelCase    :    Dict										=      do_rescale
								_lowerCAmelCase    :    int										=      rescale_factor
								_lowerCAmelCase    :    List[str]										=      do_normalize
								_lowerCAmelCase    :    str										=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_lowerCAmelCase    :    List[Any]										=      image_std if image_std is not None else OPENAI_CLIP_STD
								_lowerCAmelCase    :    Tuple										=      do_convert_rgb



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Dict[str, int]		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Dict		,	)  ->    np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )
								if "height" not in size or "width" not in size:
												raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"      )
								_lowerCAmelCase    :    List[str]										=      (size["""height"""], size["""width"""])
								return resize(_UpperCAmelCase		,	size=_UpperCAmelCase		,	resample=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[int, float]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		str		,	)  ->    int:





								'''simple docstring'''




								return rescale(_UpperCAmelCase		,	scale=_UpperCAmelCase		,	data_format=_UpperCAmelCase		,	**_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Union[str, Any]		,	)  ->    np.ndarray:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		ImageInput		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Dict[str, int]] = None		,	_UpperCAmelCase      :		PILImageResampling = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[float] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[str, TensorType]] = None		,	_UpperCAmelCase      :		bool = None		,	_UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST		,	**_UpperCAmelCase      :		str		,	)  ->    PIL.Image.Image:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase    :    Optional[Any]										=      resample if resample is not None else self.resample
								_lowerCAmelCase    :    Union[str, Any]										=      do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase    :    Union[str, Any]										=      rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase    :    int										=      do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase    :    Tuple										=      image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase    :    Dict										=      image_std if image_std is not None else self.image_std
								_lowerCAmelCase    :    Any										=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_lowerCAmelCase    :    List[Any]										=      size if size is not None else self.size
								_lowerCAmelCase    :    List[str]										=      get_size_dict(_UpperCAmelCase		,	default_to_square=_UpperCAmelCase      )

								_lowerCAmelCase    :    int										=      make_list_of_images(_UpperCAmelCase      )

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

								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_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."""      )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												_lowerCAmelCase    :    str										=      [convert_to_rgb(_UpperCAmelCase      ) for image in images]

								# All transformations expect numpy arrays.
								_lowerCAmelCase    :    Any										=      [to_numpy_array(_UpperCAmelCase      ) for image in images]

								if do_resize:
												_lowerCAmelCase    :    Dict										=      [self.resize(image=_UpperCAmelCase		,	size=_UpperCAmelCase		,	resample=_UpperCAmelCase      ) for image in images]

								if do_rescale:
												_lowerCAmelCase    :    Tuple										=      [self.rescale(image=_UpperCAmelCase		,	scale=_UpperCAmelCase      ) for image in images]

								if do_normalize:
												_lowerCAmelCase    :    Any										=      [self.normalize(image=_UpperCAmelCase		,	mean=_UpperCAmelCase		,	std=_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    str										=      [to_channel_dimension_format(_UpperCAmelCase		,	_UpperCAmelCase      ) for image in images]

								_lowerCAmelCase    :    str										=      BatchFeature(data={"""pixel_values""": images}		,	tensor_type=_UpperCAmelCase      )

								return encoded_outputs







import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_lowerCamelCase      :  int      						=						"bert-base-cased"
_lowerCamelCase      :  Union[str, Any]      						=						"google/pegasus-xsum"
_lowerCamelCase      :  Optional[int]      						=						[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCamelCase      :  Dict      						=						["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCamelCase      :  Any      						=						"patrickvonplaten/t5-tiny-random"
_lowerCamelCase      :  Dict      						=						"sshleifer/bart-tiny-random"
_lowerCamelCase      :  int      						=						"sshleifer/tiny-mbart"
_lowerCamelCase      :  List[Any]      						=						"sshleifer/tiny-marian-en-de"




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : list       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      """\n""".join(UpperCamelCase_       )
				Path(UpperCamelCase_       ).open("""w"""       ).writelines(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.source"       )      ,    UpperCamelCase_       )
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.target"       )      ,    UpperCamelCase_       )
				return tmp_dir





class        __snake_case  (_a		):



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Any										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    Dict										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    List[Any]										=      8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								_lowerCAmelCase    :    Tuple										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    int										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_src_len
												# show that targets are the same len
												assert batch["labels"].shape[1] == max_tgt_len
												if tok_name != MBART_TINY:
																continue
												# check language codes in correct place
												_lowerCAmelCase    :    Dict										=      shift_tokens_right(batch["""labels"""]		,	tokenizer.pad_token_id      )
												assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
												assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

												break  # No need to test every batch



				@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Optional[int]										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    str										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    Union[str, Any]										=      4
								_lowerCAmelCase    :    Dict										=      LegacySeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=20		,	max_target_length=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_len_source
												assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
												# show that targets were truncated
												assert batch["labels"].shape[1] == trunc_target  # Truncated
												assert max_len_target > trunc_target  # Truncated
												break  # No need to test every batch



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""      )

								_lowerCAmelCase    :    List[str]										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								_lowerCAmelCase    :    List[Any]										=      tmp_dir.joinpath("""train.source"""      ).open().readlines()
								_lowerCAmelCase    :    Tuple										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(_UpperCAmelCase		,	_UpperCAmelCase		,	128		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      {x.name for x in tmp_dir.iterdir()}
								_lowerCAmelCase    :    List[Any]										=      {x.name for x in save_dir.iterdir()}
								_lowerCAmelCase    :    Optional[Any]										=      save_dir.joinpath("""train.source"""      ).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(_UpperCAmelCase      ) < len(_UpperCAmelCase      )
								assert len(_UpperCAmelCase      ) == 1
								assert len(packed_examples[0]      ) == sum(len(_UpperCAmelCase      ) for x in orig_examples      )
								assert orig_paths == new_paths



				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE		,	reason="""This test requires fairseq"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




								if not FAIRSEQ_AVAILABLE:
												return
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    int										=      self._get_dataset(max_len=64      )
								_lowerCAmelCase    :    Optional[Any]										=      64
								_lowerCAmelCase    :    Union[str, Any]										=      ds.make_dynamic_sampler(_UpperCAmelCase		,	required_batch_size_multiple=_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      [len(_UpperCAmelCase      ) for x in batch_sampler]
								assert len(set(_UpperCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(_UpperCAmelCase      ) == len(_UpperCAmelCase      )  # no dropped or added examples
								_lowerCAmelCase    :    List[Any]										=      DataLoader(_UpperCAmelCase		,	batch_sampler=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    List[Any]										=      []
								_lowerCAmelCase    :    Tuple										=      []
								for batch in data_loader:
												_lowerCAmelCase    :    Dict										=      batch["""input_ids"""].shape
												_lowerCAmelCase    :    Optional[int]										=      src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												_lowerCAmelCase    :    Dict										=      np.product(batch["""input_ids"""].shape      )
												num_src_per_batch.append(_UpperCAmelCase      )
												if num_src_tokens > (max_tokens * 1.1):
																failures.append(_UpperCAmelCase      )
								assert num_src_per_batch[0] == max(_UpperCAmelCase      )
								if failures:
												raise AssertionError(f"too many tokens in {len(_UpperCAmelCase      )} batches"      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      self._get_dataset(max_len=512      )
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      ds.make_sortish_sampler(_UpperCAmelCase		,	shuffle=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2		,	sampler=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      tokenizer.pad_token_id

								def count_pad_tokens(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]="input_ids"      ):
												return [batch[k].eq(_UpperCAmelCase      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      ) < sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      )
								assert sum(count_pad_tokens(_UpperCAmelCase      )      ) < sum(count_pad_tokens(_UpperCAmelCase      )      )
								assert len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str=1000		,	_UpperCAmelCase      :		Dict=128      )  ->    Dict:





								'''simple docstring'''




								if os.getenv("""USE_REAL_DATA"""		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      """examples/seq2seq/wmt_en_ro"""
												_lowerCAmelCase    :    Optional[int]										=      max_len * 2 * 64
												if not Path(_UpperCAmelCase      ).joinpath("""train.len"""      ).exists():
																save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    int										=      """examples/seq2seq/test_data/wmt_en_ro"""
												_lowerCAmelCase    :    int										=      max_len * 4
												save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	n_obs=_UpperCAmelCase		,	)
								return ds, max_tokens, tokenizer



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self._get_dataset()
								_lowerCAmelCase    :    Optional[Any]										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Any										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_UpperCAmelCase      )      )
								assert idsa.intersection(_UpperCAmelCase      ) == set()



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase		,	use_fast=_UpperCAmelCase      )
								if tok_name == MBART_TINY:
												_lowerCAmelCase    :    Optional[Any]										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	src_lang="""EN"""		,	tgt_lang="""FR"""		,	)
												_lowerCAmelCase    :    Tuple										=      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	)
												_lowerCAmelCase    :    Optional[Any]										=      train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase      ) == 0

