




import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__				:     int 		=						get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")
@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase   (				UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   GPTSwaTokenizer
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   True
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		    =     GPTSwaTokenizer(__lowercase       ,	eos_token="<unk>"       ,	bos_token="<unk>"       ,	pad_token="<unk>"						)

							tokenizer.save_pretrained(self.tmpdirname						)






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     "This is a test"
							snake_case_		    =     "This is a test"
							return input_text, output_text






						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     "<s>"
							snake_case_		    =     1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase						)       ,	__lowercase						)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase						)       ,	__lowercase						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     list(self.get_tokenizer().get_vocab().keys()						)

							self.assertEqual(vocab_keys[0]       ,	"<unk>"						)
							self.assertEqual(vocab_keys[1]       ,	"<s>"						)
							self.assertEqual(vocab_keys[-1]       ,	"j"						)
							self.assertEqual(len(__lowercase						)       ,	20_00						)






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							self.assertEqual(self.get_tokenizer().vocab_size       ,	20_00						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     GPTSwaTokenizer(__lowercase						)

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

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)       ,	[4_65, 2_87, 2_65, 6_31, 8_42]						)

							snake_case_		    =     tokenizer.tokenize("I was born in 92000, and this is falsé."						)
							# fmt: off
							self.assertListEqual(
							    __lowercase       ,	["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]       ,	)
							# fmt: on

							snake_case_		    =     tokenizer.convert_tokens_to_ids(__lowercase						)
							self.assertListEqual(
							    __lowercase       ,	[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]       ,	)

							snake_case_		    =     tokenizer.convert_ids_to_tokens(__lowercase						)
							# fmt: off
							self.assertListEqual(
							    __lowercase       ,	["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]						)
							# fmt: on






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     GPTSwaTokenizer(__lowercase						)
							snake_case_		    =     ["This is a test", "I was born in 92000, and this is falsé."]
							snake_case_		    =     [
							    [4_65, 2_87, 2_65, 6_31, 8_42],
							    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__lowercase       ,	__lowercase						):
								self.assertListEqual(tokenizer.encode_fast(__lowercase						)       ,	__lowercase						)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__lowercase       ,	__lowercase						):
								self.assertEqual(tokenizer.decode_fast(__lowercase						)       ,	__lowercase						)






						@slow
						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     [
							    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
							    "Hey there, how are you doing this fine day?",
							    "This is a text with a trailing spaces followed by a dot     .",
							    "Häj sväjs lillebrör! =)",
							    "Det är inget fel på Mr. Cool",
							]

							# fmt: off
							snake_case_		    =     {"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__lowercase       ,	model_name="AI-Sweden/gpt-sw3-126m"       ,	sequences=__lowercase       ,	)






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}
