
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case      :							Any     	=    get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
						from transformers.models.mbart.modeling_mbart import shift_tokens_right

_snake_case      :							Union[str, Any]     	=    25_00_04
_snake_case      :							str     	=    25_00_20





@require_sentencepiece
@require_tokenizers
class   UpperCamelCase_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					MBartTokenizer
						UpperCamelCase      :  str				   =					MBartTokenizerFast
						UpperCamelCase      :  Tuple				   =					True
						UpperCamelCase      :  Union[str, Any]				   =					True





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Tuple:
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowercase         =      MBartTokenizer(lowerCAmelCase__		,    keep_accents=lowerCAmelCase__     )
										tokenizer.save_pretrained(self.tmpdirname     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[int]:
										lowercase         =      MBartTokenizer(lowerCAmelCase__		,    keep_accents=lowerCAmelCase__     )

										lowercase         =      tokenizer.tokenize("This is a test"     )
										self.assertListEqual(lowerCAmelCase__		,    ["▁This", "▁is", "▁a", "▁t", "est"]     )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )		,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,    )

										lowercase         =      tokenizer.tokenize("I was born in 92000, and this is falsé."     )
										self.assertListEqual(
										    lowerCAmelCase__		,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]		,    )
										lowercase         =      tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )
										self.assertListEqual(
										    lowerCAmelCase__		,    [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]		,    )

										lowercase         =      tokenizer.convert_ids_to_tokens(lowerCAmelCase__     )
										self.assertListEqual(
										    lowerCAmelCase__		,    [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[Any]:
										if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

										lowercase         =      (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																		lowercase         =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )
																		lowercase         =      self.tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )

																		lowercase         =      tempfile.mkdtemp()

																		lowercase         =      tokenizer_r.save_pretrained(lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.save_pretrained(lowerCAmelCase__     )

																		# Checks it save with the same files + the tokenizer.json file for the fast one
																		self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
																		lowercase         =      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
																		self.assertSequenceEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

																		# Checks everything loads correctly in the same way
																		lowercase         =      tokenizer_r.from_pretrained(lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.from_pretrained(lowerCAmelCase__     )

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(lowerCAmelCase__		,    lowerCAmelCase__     )     )
																						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																		shutil.rmtree(lowerCAmelCase__     )

																		# Save tokenizer rust, legacy_format=True
																		lowercase         =      tempfile.mkdtemp()

																		lowercase         =      tokenizer_r.save_pretrained(lowerCAmelCase__		,    legacy_format=lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.save_pretrained(lowerCAmelCase__     )

																		# Checks it save with the same files
																		self.assertSequenceEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

																		# Checks everything loads correctly in the same way
																		lowercase         =      tokenizer_r.from_pretrained(lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.from_pretrained(lowerCAmelCase__     )

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(lowerCAmelCase__		,    lowerCAmelCase__     )     )

																		shutil.rmtree(lowerCAmelCase__     )

																		# Save tokenizer rust, legacy_format=False
																		lowercase         =      tempfile.mkdtemp()

																		lowercase         =      tokenizer_r.save_pretrained(lowerCAmelCase__		,    legacy_format=lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.save_pretrained(lowerCAmelCase__     )

																		# Checks it saved the tokenizer.json file
																		self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

																		# Checks everything loads correctly in the same way
																		lowercase         =      tokenizer_r.from_pretrained(lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.from_pretrained(lowerCAmelCase__     )

																		# Check special tokens are set accordingly on Rust and Python
																		for key in tokenizer_pp.special_tokens_map:
																						self.assertTrue(hasattr(lowerCAmelCase__		,    lowerCAmelCase__     )     )

																		shutil.rmtree(lowerCAmelCase__     )





@require_torch
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					'''facebook/mbart-large-en-ro'''
						UpperCamelCase      :  Optional[int]				   =					[
						    ''' UN Chief Says There Is No Military Solution in Syria''',
						    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
						]
						UpperCamelCase      :  List[Any]				   =					[
						    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
						    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
						    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
						    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
						]
						UpperCamelCase      :  List[str]				   =					[8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]





						@classmethod
						def        SCREAMING_SNAKE_CASE(			cls							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      MBartTokenizer.from_pretrained(
										    cls.checkpoint_name		,    src_lang="en_XX"		,    tgt_lang="ro_RO"     )
										lowercase         =      1
										return cls





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Any:
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]		,    250001     )
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]		,    250004     )
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]		,    250020     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->List[str]:
										lowercase         =      self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
										self.assertListEqual(self.expected_src_tokens		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										self.assertIn(lowerCAmelCase__		,    self.tokenizer.all_special_ids     )
										lowercase         =      [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
										lowercase         =      self.tokenizer.decode(lowerCAmelCase__		,    skip_special_tokens=lowerCAmelCase__     )
										lowercase         =      self.tokenizer.decode(generated_ids[1:]		,    skip_special_tokens=lowerCAmelCase__     )
										self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )
										self.assertNotIn(self.tokenizer.eos_token		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      ["this is gunna be a long sentence " * 20]
										assert isinstance(src_text[0]		,    lowerCAmelCase__     )
										lowercase         =      10
										lowercase         =      self.tokenizer(lowerCAmelCase__		,    max_length=lowerCAmelCase__		,    truncation=lowerCAmelCase__     ).input_ids[0]
										self.assertEqual(ids[-2]		,    2     )
										self.assertEqual(ids[-1]		,    lowerCAmelCase__     )
										self.assertEqual(len(lowerCAmelCase__     )		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->int:
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )		,    [250026, 250001]     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Any:
										lowercase         =      tempfile.mkdtemp()
										lowercase         =      self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(lowerCAmelCase__     )
										lowercase         =      MBartTokenizer.from_pretrained(lowerCAmelCase__     )
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,    lowerCAmelCase__     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      self.tokenizer(self.src_text		,    text_target=self.tgt_text		,    padding=lowerCAmelCase__		,    return_tensors="pt"     )
										lowercase         =      shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id     )

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Optional[int]:
										lowercase         =      self.tokenizer(
										    self.src_text		,    text_target=self.tgt_text		,    padding=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    max_length=len(self.expected_src_tokens     )		,    return_tensors="pt"		,    )

										lowercase         =      shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id     )

										self.assertIsInstance(lowerCAmelCase__		,    lowerCAmelCase__     )

										self.assertEqual((2, 14)		,    batch.input_ids.shape     )
										self.assertEqual((2, 14)		,    batch.attention_mask.shape     )
										lowercase         =      batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens		,    lowerCAmelCase__     )
										self.assertEqual(2		,    batch.decoder_input_ids[0, -1]     )  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens		,    []     )
										self.assertEqual(self.tokenizer.suffix_tokens		,    [self.tokenizer.eos_token_id, EN_CODE]     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										lowercase         =      self.tokenizer(self.src_text		,    padding=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    max_length=3		,    return_tensors="pt"     )
										lowercase         =      self.tokenizer(
										    text_target=self.tgt_text		,    padding=lowerCAmelCase__		,    truncation=lowerCAmelCase__		,    max_length=10		,    return_tensors="pt"     )
										lowercase         =      targets["input_ids"]
										lowercase         =      shift_tokens_right(lowerCAmelCase__		,    self.tokenizer.pad_token_id     )

										self.assertEqual(batch.input_ids.shape[1]		,    3     )
										self.assertEqual(batch.decoder_input_ids.shape[1]		,    10     )





						@require_torch
						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->Any:
										lowercase         =      self.tokenizer._build_translation_inputs(
										    "A test"		,    return_tensors="pt"		,    src_lang="en_XX"		,    tgt_lang="ar_AR"     )

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__     )		,    {
										        # A, test, EOS, en_XX
										        "input_ids": [[62, 3034, 2, 250004]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # ar_AR
										        "forced_bos_token_id": 250001,
										    }		,    )


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

_snake_case      :							List[Any]     	=    logging.get_logger(__name__)





@add_end_docstrings(__a		)
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:Tuple		,    **lowerCAmelCase__							:Dict     )  ->int:
										super().__init__(**lowerCAmelCase__     )
										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(lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										lowercase         =      {}
										lowercase         =      {}
										lowercase         =      {}
										# preprocess args
										if "points_per_batch" in kwargs:
														lowercase         =      kwargs["points_per_batch"]
										if "points_per_crop" in kwargs:
														lowercase         =      kwargs["points_per_crop"]
										if "crops_n_layers" in kwargs:
														lowercase         =      kwargs["crops_n_layers"]
										if "crop_overlap_ratio" in kwargs:
														lowercase         =      kwargs["crop_overlap_ratio"]
										if "crop_n_points_downscale_factor" in kwargs:
														lowercase         =      kwargs["crop_n_points_downscale_factor"]
										# postprocess args
										if "pred_iou_thresh" in kwargs:
														lowercase         =      kwargs["pred_iou_thresh"]
										if "stability_score_offset" in kwargs:
														lowercase         =      kwargs["stability_score_offset"]
										if "mask_threshold" in kwargs:
														lowercase         =      kwargs["mask_threshold"]
										if "stability_score_thresh" in kwargs:
														lowercase         =      kwargs["stability_score_thresh"]
										if "crops_nms_thresh" in kwargs:
														lowercase         =      kwargs["crops_nms_thresh"]
										if "output_rle_mask" in kwargs:
														lowercase         =      kwargs["output_rle_mask"]
										if "output_bboxes_mask" in kwargs:
														lowercase         =      kwargs["output_bboxes_mask"]
										return preprocess_kwargs, forward_params, postprocess_kwargs





						def __call__(			self							:List[str]		,    lowerCAmelCase__							:int		,    *lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Any=None		,    **lowerCAmelCase__							:List[str]     )  ->Optional[int]:
										return super().__call__(lowerCAmelCase__		,    *lowerCAmelCase__		,    num_workers=lowerCAmelCase__		,    batch_size=lowerCAmelCase__		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int = 0		,    lowerCAmelCase__							:float = 512 / 1500		,    lowerCAmelCase__							:Optional[int] = 32		,    lowerCAmelCase__							:Optional[int] = 1		,    )  ->Any:
										lowercase         =      load_image(lowerCAmelCase__     )
										lowercase         =      self.image_processor.size["longest_edge"]
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.generate_crop_boxes(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )
										lowercase         =      self.image_processor(images=lowerCAmelCase__		,    return_tensors="pt"     )

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

										lowercase         =      grid_points.shape[1]
										lowercase         =      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		,    lowerCAmelCase__		,    lowerCAmelCase__     ):
														lowercase         =      grid_points[:, i : i + points_per_batch, :, :]
														lowercase         =      input_labels[:, i : i + points_per_batch]
														lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Dict=0.88		,    lowerCAmelCase__							:Dict=0.95		,    lowerCAmelCase__							:str=0		,    lowerCAmelCase__							:int=1		,    )  ->str:
										lowercase         =      model_inputs.pop("input_boxes"     )
										lowercase         =      model_inputs.pop("is_last"     )
										lowercase         =      model_inputs.pop("original_sizes"     ).tolist()
										lowercase         =      model_inputs.pop("reshaped_input_sizes"     ).tolist()

										lowercase         =      self.model(**lowerCAmelCase__     )

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





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Dict		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:str=False		,    lowerCAmelCase__							:int=0.7		,    )  ->List[Any]:
										lowercase         =      []
										lowercase         =      []
										lowercase         =      []
										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"     )     )

										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase         =      torch.cat(lowerCAmelCase__     )
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      self.image_processor.post_process_for_mask_generation(
										    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      {}
										if output_rle_mask:
														lowercase         =      rle_mask

										if output_bboxes_mask:
														lowercase         =      bounding_boxes

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