





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      '''bert-base-cased'''
_SCREAMING_SNAKE_CASE   =      '''google/pegasus-xsum'''
_SCREAMING_SNAKE_CASE   =      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_SCREAMING_SNAKE_CASE   =      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_SCREAMING_SNAKE_CASE   =      '''patrickvonplaten/t5-tiny-random'''
_SCREAMING_SNAKE_CASE   =      '''sshleifer/bart-tiny-random'''
_SCREAMING_SNAKE_CASE   =      '''sshleifer/tiny-mbart'''
_SCREAMING_SNAKE_CASE   =      '''sshleifer/tiny-marian-en-de'''




def    _lowerCAmelCase  (			lowerCamelCase_     :					Path						,    lowerCamelCase_     :					list			):
			__lowercase			  =							'''\n'''.join(lowerCamelCase_			)
			Path(lowerCamelCase_			).open('''w'''			).writelines(lowerCamelCase_			)




def    _lowerCAmelCase  (			lowerCamelCase_     :					List[Any]			):
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(lowerCamelCase_						,    f"{split}.source"			)						,    lowerCamelCase_			)
						_dump_articles(os.path.join(lowerCamelCase_						,    f"{split}.target"			)						,    lowerCamelCase_			)
			return tmp_dir






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

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







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase	)
				__lowercase			  =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in ARTICLES	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in SUMMARIES	)
				__lowercase			  =							4
				__lowercase			  =							8
				assert max_len_target > max_src_len  # Will be truncated
				assert max_len_source > max_src_len  # Will be truncated
				__lowercase  ,						__lowercase			  =							'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
				__lowercase			  =							SeqaSeqDataset(
				    _lowerCamelCase       ,data_dir=_lowerCamelCase       ,type_path='''train'''       ,max_source_length=_lowerCamelCase       ,max_target_length=_lowerCamelCase       ,src_lang=_lowerCamelCase       ,tgt_lang=_lowerCamelCase       ,)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=2       ,collate_fn=train_dataset.collate_fn	)
				for batch in dataloader:
							assert isinstance(_lowerCamelCase       ,_lowerCamelCase	)
							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
							__lowercase			  =							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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase	)
				__lowercase			  =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in ARTICLES	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in SUMMARIES	)
				__lowercase			  =							4
				__lowercase			  =							LegacySeqaSeqDataset(
				    _lowerCamelCase       ,data_dir=_lowerCamelCase       ,type_path='''train'''       ,max_source_length=20       ,max_target_length=_lowerCamelCase       ,)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,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   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''	)

				__lowercase			  =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				__lowercase			  =							tmp_dir.joinpath('''train.source'''	).open().readlines()
				__lowercase			  =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				pack_data_dir(_lowerCamelCase       ,_lowerCamelCase       ,128       ,_lowerCamelCase	)
				__lowercase			  =							{x.name for x in tmp_dir.iterdir()}
				__lowercase			  =							{x.name for x in save_dir.iterdir()}
				__lowercase			  =							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(_lowerCamelCase	) < len(_lowerCamelCase	)
				assert len(_lowerCamelCase	) == 1
				assert len(packed_examples[0]	) == sum(len(_lowerCamelCase	) for x in orig_examples	)
				assert orig_paths == new_paths


	@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,reason='''This test requires fairseq'''	)
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._get_dataset(max_len=512	)
				__lowercase			  =							2
				__lowercase			  =							ds.make_sortish_sampler(_lowerCamelCase       ,shuffle=_lowerCamelCase	)

				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=_lowerCamelCase       ,collate_fn=ds.collate_fn       ,num_workers=2	)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=_lowerCamelCase       ,collate_fn=ds.collate_fn       ,num_workers=2       ,sampler=_lowerCamelCase	)

				__lowercase			  =							tokenizer.pad_token_id

				def count_pad_tokens(_lowerCamelCase       ,_lowerCamelCase="input_ids"	):
							return [batch[k].eq(_lowerCamelCase	).sum().item() for batch in data_loader]

				assert sum(count_pad_tokens(_lowerCamelCase       ,k='''labels'''	)	) < sum(count_pad_tokens(_lowerCamelCase       ,k='''labels'''	)	)
				assert sum(count_pad_tokens(_lowerCamelCase	)	) < sum(count_pad_tokens(_lowerCamelCase	)	)
				assert len(_lowerCamelCase	) == len(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=1000       ,_lowerCamelCase=128	)							->							int:







				'''simple docstring'''





				if os.getenv('''USE_REAL_DATA'''       ,_lowerCamelCase	):
							__lowercase			  =							'''examples/seq2seq/wmt_en_ro'''
							__lowercase			  =							max_len * 2 * 64
							if not Path(_lowerCamelCase	).joinpath('''train.len'''	).exists():
										save_len_file(_lowerCamelCase       ,_lowerCamelCase	)
				else:
							__lowercase			  =							'''examples/seq2seq/test_data/wmt_en_ro'''
							__lowercase			  =							max_len * 4
							save_len_file(_lowerCamelCase       ,_lowerCamelCase	)

				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase	)
				__lowercase			  =							SeqaSeqDataset(
				    _lowerCamelCase       ,data_dir=_lowerCamelCase       ,type_path='''train'''       ,max_source_length=_lowerCamelCase       ,max_target_length=_lowerCamelCase       ,n_obs=_lowerCamelCase       ,)
				return ds, max_tokens, tokenizer


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._get_dataset()
				__lowercase			  =							set(DistributedSortishSampler(_lowerCamelCase       ,256       ,num_replicas=2       ,rank=0       ,add_extra_examples=_lowerCamelCase	)	)
				__lowercase			  =							set(DistributedSortishSampler(_lowerCamelCase       ,256       ,num_replicas=2       ,rank=1       ,add_extra_examples=_lowerCamelCase	)	)
				assert idsa.intersection(_lowerCamelCase	) == set()


	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]       ,)
	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase       ,use_fast=_lowerCamelCase	)
				if tok_name == MBART_TINY:
							__lowercase			  =							SeqaSeqDataset(
							    _lowerCamelCase       ,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'''       ,)
							__lowercase			  =							train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
							__lowercase			  =							SeqaSeqDataset(
							    _lowerCamelCase       ,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       ,)
							__lowercase			  =							train_dataset.dataset_kwargs
							assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
							assert len(_lowerCamelCase	) == 1 if tok_name == BART_TINY else len(_lowerCamelCase	) == 0












'''simple docstring'''



from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
	import torch


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			int     =    ["pixel_values"]


	def __init__(self       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = PILImageResampling.BILINEAR       ,_lowerCamelCase = True       ,_lowerCamelCase = None       ,_lowerCamelCase = True       ,_lowerCamelCase = 1 / 255       ,_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			  =							resample
				__lowercase			  =							do_center_crop
				__lowercase			  =							crop_size
				__lowercase			  =							do_rescale
				__lowercase			  =							rescale_factor
				__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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = PILImageResampling.BICUBIC       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				if "shortest_edge" not in size:
							raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
				__lowercase			  =							get_resize_output_image_size(_lowerCamelCase       ,size=size['''shortest_edge''']       ,default_to_square=_lowerCamelCase	)
				return resize(_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (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"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"	)
				return center_crop(_lowerCamelCase       ,size=(size['''height'''], size['''width'''])       ,data_format=_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase	)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = None       ,**_lowerCamelCase       ,)							->							np.ndarray:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (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       ,**_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							do_resize if do_resize is not None else self.do_resize
				__lowercase			  =							size if size is not None else self.size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,default_to_square=_lowerCamelCase	)
				__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			  =							crop_size if crop_size is not None else self.crop_size
				__lowercase			  =							get_size_dict(_lowerCamelCase       ,param_name='''crop_size'''	)
				__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			  =							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			  =							make_list_of_images(_lowerCamelCase	)

				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.'''	)

				if do_resize and size is None:
							raise ValueError('''Size 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.'''	)

				# All transformations expect numpy arrays.
				__lowercase			  =							[to_numpy_array(_lowerCamelCase	) for image in images]

				if do_resize:
							__lowercase			  =							[self.resize(image=_lowerCamelCase       ,size=_lowerCamelCase       ,resample=_lowerCamelCase	) for image in images]

				if do_center_crop:
							__lowercase			  =							[self.center_crop(image=_lowerCamelCase       ,size=_lowerCamelCase	) for image in images]

				if do_rescale:
							__lowercase			  =							[self.rescale(image=_lowerCamelCase       ,scale=_lowerCamelCase	) for image in images]

				if do_normalize:
							__lowercase			  =							[self.normalize(image=_lowerCamelCase       ,mean=_lowerCamelCase       ,std=_lowerCamelCase	) for image in images]

				__lowercase			  =							[to_channel_dimension_format(_lowerCamelCase       ,_lowerCamelCase	) for image in images]

				__lowercase			  =							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase       ,tensor_type=_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
							if len(_lowerCamelCase	) != len(_lowerCamelCase	):
										raise ValueError(
										    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''	)

							if is_torch_tensor(_lowerCamelCase	):
										__lowercase			  =							target_sizes.numpy()

							__lowercase			  =							[]

							for idx in range(len(_lowerCamelCase	)	):
										__lowercase			  =							torch.nn.functional.interpolate(
										    logits[idx].unsqueeze(dim=0	)       ,size=target_sizes[idx]       ,mode='''bilinear'''       ,align_corners=_lowerCamelCase	)
										__lowercase			  =							resized_logits[0].argmax(dim=0	)
										semantic_segmentation.append(_lowerCamelCase	)
				else:
							__lowercase			  =							logits.argmax(dim=1	)
							__lowercase			  =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

				return semantic_segmentation





