





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


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






def lowerCamelCase__   (							snake_case_     :  Path	,				snake_case_     :  list	)				->    Dict:
			__snake_case								=       '''\n'''.join(snake_case_	)
			Path(snake_case_	).open('''w'''	).writelines(snake_case_	)






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







class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]    ,      )
						@slow
						def        a			(self	:				Union[str, Any]    ,      a__	:				Optional[int]				):





									"""simple docstring"""



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





									"""simple docstring"""



									__snake_case								=       AutoTokenizer.from_pretrained(a__				)
									__snake_case								=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
									__snake_case								=       max(len(tokenizer.encode(a__				)				) for a in ARTICLES				)
									__snake_case								=       max(len(tokenizer.encode(a__				)				) for a in SUMMARIES				)
									__snake_case								=       4
									__snake_case								=       LegacySeqaSeqDataset(
									    a__    ,      data_dir=a__    ,      type_path='''train'''    ,      max_source_length=20    ,      max_target_length=a__    ,      )
									__snake_case								=       DataLoader(a__    ,      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        a			(self	:				int				):





									"""simple docstring"""



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

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


						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,      reason='''This test requires fairseq'''				)
						def        a			(self	:				Any				):





									"""simple docstring"""



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


						def        a			(self	:				List[str]				):





									"""simple docstring"""



									__snake_case	,     __snake_case	,     __snake_case								=       self._get_dataset(max_len=512				)
									__snake_case								=       2
									__snake_case								=       ds.make_sortish_sampler(a__    ,      shuffle=a__				)

									__snake_case								=       DataLoader(a__    ,      batch_size=a__    ,      collate_fn=ds.collate_fn    ,      num_workers=2				)
									__snake_case								=       DataLoader(a__    ,      batch_size=a__    ,      collate_fn=ds.collate_fn    ,      num_workers=2    ,      sampler=a__				)

									__snake_case								=       tokenizer.pad_token_id

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

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


						def        a			(self	:				int    ,      a__	:				Optional[Any]=1000    ,      a__	:				Optional[Any]=128				):





									"""simple docstring"""



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

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


						def        a			(self	:				List[str]				):





									"""simple docstring"""



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


						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]    ,      )
						def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





									"""simple docstring"""



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








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

