






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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[Any]       				=  "\n".join(_A			)
     Path(_A			).open("w"			).writelines(_A			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:





     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(_A					,    f'{split}.source'			)					,    _A			)
          _dump_articles(os.path.join(_A					,    f'{split}.target'			)					,    _A			)
     return tmp_dir





class 			__a  (    UpperCAmelCase__       ):







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]							,   )
 @slow
 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






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


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  AutoTokenizer.from_pretrained(__lowercase						)
      UpperCamelCase__			:   Dict       				=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
      UpperCamelCase__			:   List[str]       				=  max(len(tokenizer.encode(__lowercase						)						) for a in ARTICLES						)
      UpperCamelCase__			:   Tuple       				=  max(len(tokenizer.encode(__lowercase						)						) for a in SUMMARIES						)
      UpperCamelCase__			:   Optional[int]       				=  4
      UpperCamelCase__			:   str       				=  LegacySeqaSeqDataset(
          __lowercase							,   data_dir=__lowercase							,   type_path="train"							,   max_source_length=20							,   max_target_length=__lowercase							,   )
      UpperCamelCase__			:   Dict       				=  DataLoader(__lowercase							,   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 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"						)

      UpperCamelCase__			:   Union[str, Any]       				=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
      UpperCamelCase__			:   List[Any]       				=  tmp_dir.joinpath("train.source"						).open().readlines()
      UpperCamelCase__			:   Union[str, Any]       				=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
      pack_data_dir(__lowercase							,   __lowercase							,   1_28							,   __lowercase						)
      UpperCamelCase__			:   Any       				=  {x.name for x in tmp_dir.iterdir()}
      UpperCamelCase__			:   Tuple       				=  {x.name for x in save_dir.iterdir()}
      UpperCamelCase__			:   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(__lowercase						) < len(__lowercase						)
      assert len(__lowercase						) == 1
      assert len(packed_examples[0]						) == sum(len(__lowercase						) for x in orig_examples						)
      assert orig_paths == new_paths







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


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  self._get_dataset(max_len=5_12						)
      UpperCamelCase__			:   Optional[Any]       				=  2
      UpperCamelCase__			:   Tuple       				=  ds.make_sortish_sampler(__lowercase							,   shuffle=__lowercase						)

      UpperCamelCase__			:   Optional[Any]       				=  DataLoader(__lowercase							,   batch_size=__lowercase							,   collate_fn=ds.collate_fn							,   num_workers=2						)
      UpperCamelCase__			:   Union[str, Any]       				=  DataLoader(__lowercase							,   batch_size=__lowercase							,   collate_fn=ds.collate_fn							,   num_workers=2							,   sampler=__lowercase						)

      UpperCamelCase__			:   List[str]       				=  tokenizer.pad_token_id

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

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







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tuple=10_00							,   SCREAMING_SNAKE_CASE					:   Dict=1_28						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Dict       				=  AutoTokenizer.from_pretrained(__lowercase						)
      UpperCamelCase__			:   Any       				=  SeqaSeqDataset(
          __lowercase							,   data_dir=__lowercase							,   type_path="train"							,   max_source_length=__lowercase							,   max_target_length=__lowercase							,   n_obs=__lowercase							,   )
      return ds, max_tokens, tokenizer







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  self._get_dataset()
      UpperCamelCase__			:   List[Any]       				=  set(DistributedSortishSampler(__lowercase							,   2_56							,   num_replicas=2							,   rank=0							,   add_extra_examples=__lowercase						)						)
      UpperCamelCase__			:   Any       				=  set(DistributedSortishSampler(__lowercase							,   2_56							,   num_replicas=2							,   rank=1							,   add_extra_examples=__lowercase						)						)
      assert idsa.intersection(__lowercase						) == set()







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]							,   )
 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  AutoTokenizer.from_pretrained(__lowercase							,   use_fast=__lowercase						)
      if tok_name == MBART_TINY:
           UpperCamelCase__			:   Any       				=  SeqaSeqDataset(
               __lowercase							,   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"							,   )
           UpperCamelCase__			:   str       				=  train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
           UpperCamelCase__			:   List[str]       				=  SeqaSeqDataset(
               __lowercase							,   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							,   )
           UpperCamelCase__			:   List[str]       				=  train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(__lowercase						) == 1 if tok_name == BART_TINY else len(__lowercase						) == 0





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowercase__				:     Dict 		=						logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__			)
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     Optional[Any]       ,	**__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							super().__init__(**__lowercase						)
							requires_backends(self       ,	"vision"						)
							requires_backends(self       ,	"torch"						)

							if self.framework != "pt":
								raise ValueError(f"The {self.__class__} is only available in PyTorch."						)

							self.check_model_type(__lowercase						)






						def        snake_case__						(						self							:     Optional[int]       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     {}
							snake_case_		    =     {}
							snake_case_		    =     {}
							# preprocess args
							if "points_per_batch" in kwargs:
								snake_case_		    =     kwargs["points_per_batch"]
							if "points_per_crop" in kwargs:
								snake_case_		    =     kwargs["points_per_crop"]
							if "crops_n_layers" in kwargs:
								snake_case_		    =     kwargs["crops_n_layers"]
							if "crop_overlap_ratio" in kwargs:
								snake_case_		    =     kwargs["crop_overlap_ratio"]
							if "crop_n_points_downscale_factor" in kwargs:
								snake_case_		    =     kwargs["crop_n_points_downscale_factor"]
							# postprocess args
							if "pred_iou_thresh" in kwargs:
								snake_case_		    =     kwargs["pred_iou_thresh"]
							if "stability_score_offset" in kwargs:
								snake_case_		    =     kwargs["stability_score_offset"]
							if "mask_threshold" in kwargs:
								snake_case_		    =     kwargs["mask_threshold"]
							if "stability_score_thresh" in kwargs:
								snake_case_		    =     kwargs["stability_score_thresh"]
							if "crops_nms_thresh" in kwargs:
								snake_case_		    =     kwargs["crops_nms_thresh"]
							if "output_rle_mask" in kwargs:
								snake_case_		    =     kwargs["output_rle_mask"]
							if "output_bboxes_mask" in kwargs:
								snake_case_		    =     kwargs["output_bboxes_mask"]
							return preprocess_kwargs, forward_params, postprocess_kwargs






						def __call__(						self							:     Optional[int]       ,	__lowercase							:     List[str]       ,	*__lowercase							:     Optional[Any]       ,	__lowercase							:     Dict=None       ,	__lowercase							:     List[str]=None       ,	**__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							return super().__call__(__lowercase       ,	*__lowercase       ,	num_workers=__lowercase       ,	batch_size=__lowercase       ,	**__lowercase						)






						def        snake_case__						(						self							:     str       ,	__lowercase							:     int       ,	__lowercase							:     List[str]=64       ,	__lowercase							:     int = 0       ,	__lowercase							:     float = 5_12 / 15_00       ,	__lowercase							:     Optional[int] = 32       ,	__lowercase							:     Optional[int] = 1       ,	):






							"""simple docstring"""






							snake_case_		    =     load_image(__lowercase						)
							snake_case_		    =     self.image_processor.size["longest_edge"]
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.generate_crop_boxes(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)
							snake_case_		    =     self.image_processor(images=__lowercase       ,	return_tensors="pt"						)

							with self.device_placement():
								if self.framework == "pt":
									snake_case_		    =     self.get_inference_context()
									with inference_context():
										snake_case_		    =     self._ensure_tensor_on_device(__lowercase       ,	device=self.device						)
										snake_case_		    =     self.model.get_image_embeddings(model_inputs.pop("pixel_values"						)						)
										snake_case_		    =     image_embeddings

							snake_case_		    =     grid_points.shape[1]
							snake_case_		    =     points_per_batch if points_per_batch is not None else n_points

							if points_per_batch <= 0:
								raise ValueError(
								    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
								    "To return all points at once, set points_per_batch to None"						)

							for i in range(0       ,	__lowercase       ,	__lowercase						):
								snake_case_		    =     grid_points[:, i : i + points_per_batch, :, :]
								snake_case_		    =     input_labels[:, i : i + points_per_batch]
								snake_case_		    =     i == n_points - points_per_batch
								yield {
								    "input_points": batched_points,
								    "input_labels": labels,
								    "input_boxes": crop_boxes,
								    "is_last": is_last,
								    **model_inputs,
								}






						def        snake_case__						(						self							:     Tuple       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     Union[str, Any]=0.88       ,	__lowercase							:     Union[str, Any]=0.95       ,	__lowercase							:     int=0       ,	__lowercase							:     int=1       ,	):






							"""simple docstring"""






							snake_case_		    =     model_inputs.pop("input_boxes"						)
							snake_case_		    =     model_inputs.pop("is_last"						)
							snake_case_		    =     model_inputs.pop("original_sizes"						).tolist()
							snake_case_		    =     model_inputs.pop("reshaped_input_sizes"						).tolist()

							snake_case_		    =     self.model(**__lowercase						)

							# post processing happens here in order to avoid CPU GPU copies of ALL the masks
							snake_case_		    =     model_outputs["pred_masks"]
							snake_case_		    =     self.image_processor.post_process_masks(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	binarize=__lowercase						)
							snake_case_		    =     model_outputs["iou_scores"]
							snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.filter_masks(
							    masks[0]       ,	iou_scores[0]       ,	original_sizes[0]       ,	input_boxes[0]       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase       ,	)
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}






						def        snake_case__						(						self							:     str       ,	__lowercase							:     Any       ,	__lowercase							:     Optional[int]=False       ,	__lowercase							:     int=False       ,	__lowercase							:     List[str]=0.7       ,	):






							"""simple docstring"""






							snake_case_		    =     []
							snake_case_		    =     []
							snake_case_		    =     []
							for model_output in model_outputs:
								all_scores.append(model_output.pop("iou_scores"						)						)
								all_masks.extend(model_output.pop("masks"						)						)
								all_boxes.append(model_output.pop("boxes"						)						)

							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_		    =     torch.cat(__lowercase						)
							snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     self.image_processor.post_process_for_mask_generation(
							    __lowercase       ,	__lowercase       ,	__lowercase       ,	__lowercase						)

							snake_case_		    =     defaultdict(__lowercase						)
							for output in model_outputs:
								for k, v in output.items():
									extra[k].append(__lowercase						)

							snake_case_		    =     {}
							if output_rle_mask:
								snake_case_		    =     rle_mask

							if output_bboxes_mask:
								snake_case_		    =     bounding_boxes

							return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
