
import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_A   =     {
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

_A   =     {
    "ctrl": 2_56,
}

_A   =     {
    "Pregnancy": 16_86_29,
    "Christianity": 76_75,
    "Explain": 10_64_23,
    "Fitness": 6_34_40,
    "Saving": 6_31_63,
    "Ask": 2_71_71,
    "Ass": 9_59_85,
    "Joke": 16_35_09,
    "Questions": 4_56_22,
    "Thoughts": 4_96_05,
    "Retail": 5_23_42,
    "Feminism": 16_43_38,
    "Writing": 1_19_92,
    "Atheism": 19_22_63,
    "Netflix": 4_86_16,
    "Computing": 3_96_39,
    "Opinion": 4_32_13,
    "Alone": 4_49_67,
    "Funny": 5_89_17,
    "Gaming": 4_03_58,
    "Human": 40_88,
    "India": 13_31,
    "Joker": 7_71_38,
    "Diet": 3_62_06,
    "Legal": 1_18_59,
    "Norman": 49_39,
    "Tip": 7_26_89,
    "Weight": 5_23_43,
    "Movies": 4_62_73,
    "Running": 2_34_25,
    "Science": 20_90,
    "Horror": 3_77_93,
    "Confession": 6_05_72,
    "Finance": 1_22_50,
    "Politics": 1_63_60,
    "Scary": 19_19_85,
    "Support": 1_26_54,
    "Technologies": 3_25_16,
    "Teenage": 6_61_60,
    "Event": 3_27_69,
    "Learned": 6_74_60,
    "Notion": 18_27_70,
    "Wikipedia": 3_75_83,
    "Books": 66_65,
    "Extract": 7_60_50,
    "Confessions": 10_27_01,
    "Conspiracy": 7_59_32,
    "Links": 6_36_74,
    "Narcissus": 15_04_25,
    "Relationship": 5_47_66,
    "Relationships": 13_47_96,
    "Reviews": 4_16_71,
    "News": 42_56,
    "Translation": 2_68_20,
    "multilingual": 12_84_06,
}






def    lowerCamelCase__				(				__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	set()
			lowerCAmelCase_						=	word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)				)
						lowerCAmelCase_						=	char

			lowerCAmelCase_						=	set(__lowerCAmelCase				)
			return pairs





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=VOCAB_FILES_NAMES
				_lowercase					=PRETRAINED_VOCAB_FILES_MAP
				_lowercase					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase					=CONTROL_CODES




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase="<unk>"				,				**_UpperCamelCase     )				->      List[str]:
							super().__init__(unk_token=_UpperCamelCase				,				**_UpperCamelCase     )

							with open(_UpperCamelCase				,				encoding="utf-8"     ) as vocab_handle:
										lowerCAmelCase_						=	json.load(_UpperCamelCase     )
							lowerCAmelCase_						=	{v: k for k, v in self.encoder.items()}
							with open(_UpperCamelCase				,				encoding="utf-8"     ) as merges_handle:
										lowerCAmelCase_						=	merges_handle.read().split("\n"     )[1:-1]
							lowerCAmelCase_						=	[tuple(merge.split()     ) for merge in merges]
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	{}




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return len(self.encoder     )




				def      __a						(					self     )				->      Any:
							return dict(self.encoder				,				**self.added_tokens_encoder     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							if token in self.cache:
										return self.cache[token]
							lowerCAmelCase_						=	tuple(_UpperCamelCase     )
							lowerCAmelCase_						=	tuple(list(word[:-1]     ) + [word[-1] + "</w>"]     )
							lowerCAmelCase_						=	get_pairs(_UpperCamelCase     )

							if not pairs:
										return token

							while True:
										lowerCAmelCase_						=	min(_UpperCamelCase				,				key=lambda _UpperCamelCase     : self.bpe_ranks.get(_UpperCamelCase				,				float("inf"     )     )     )
										if bigram not in self.bpe_ranks:
													break
										lowerCAmelCase_	,  lowerCAmelCase_						=	bigram
										lowerCAmelCase_						=	[]
										lowerCAmelCase_						=	0
										while i < len(_UpperCamelCase     ):
													try:
																lowerCAmelCase_						=	word.index(_UpperCamelCase				,				_UpperCamelCase     )
													except ValueError:
																new_word.extend(word[i:]     )
																break
													else:
																new_word.extend(word[i:j]     )
																lowerCAmelCase_						=	j

													if word[i] == first and i < len(_UpperCamelCase     ) - 1 and word[i + 1] == second:
																new_word.append(first + second     )
																i += 2
													else:
																new_word.append(word[i]     )
																i += 1
										lowerCAmelCase_						=	tuple(_UpperCamelCase     )
										lowerCAmelCase_						=	new_word
										if len(_UpperCamelCase     ) == 1:
													break
										else:
													lowerCAmelCase_						=	get_pairs(_UpperCamelCase     )
							lowerCAmelCase_						=	"@@ ".join(_UpperCamelCase     )
							lowerCAmelCase_						=	word[:-4]
							lowerCAmelCase_						=	word
							return word




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	[]

							lowerCAmelCase_						=	re.findall(r"\S+\n?"				,				_UpperCamelCase     )

							for token in words:
										split_tokens.extend(list(self.bpe(_UpperCamelCase     ).split(" "     )     )     )
							return split_tokens




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							return self.encoder.get(_UpperCamelCase				,				self.encoder.get(self.unk_token     )     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Any:
							return self.decoder.get(_UpperCamelCase				,				self.unk_token     )




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	" ".join(_UpperCamelCase     ).replace("@@ "				,				""     ).strip()
							return out_string




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None     )				->      Tuple[str]:
							if not os.path.isdir(_UpperCamelCase     ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
										return
							lowerCAmelCase_						=	os.path.join(
							    _UpperCamelCase				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCAmelCase_						=	os.path.join(
							    _UpperCamelCase				,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

							with open(_UpperCamelCase				,				"w"				,				encoding="utf-8"     ) as f:
										f.write(json.dumps(self.encoder				,				indent=2				,				sort_keys=_UpperCamelCase				,				ensure_ascii=_UpperCamelCase     ) + "\n"     )

							lowerCAmelCase_						=	0
							with open(_UpperCamelCase				,				"w"				,				encoding="utf-8"     ) as writer:
										writer.write("#version: 0.2\n"     )
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,				key=lambda _UpperCamelCase     : kv[1]     ):
													if index != token_index:
																logger.warning(
																    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																    " Please check that the tokenizer is not corrupted!"     )
																lowerCAmelCase_						=	token_index
													writer.write(" ".join(_UpperCamelCase     ) + "\n"     )
													index += 1

							return vocab_file, merge_file

				# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
				#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
				#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
				#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
				#     return ''.join(tokens_generated_so_far)





import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:





			class 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

							self.assertGreater(len(_UpperCamelCase     )				,				0     )
							for detected_object in outputs:
										self.assertEqual(
										    _UpperCamelCase				,				{
										        "score": ANY(_UpperCamelCase     ),
										        "label": ANY(_UpperCamelCase     ),
										        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
										    }				,				)

							import datasets

							lowerCAmelCase_						=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"				,				"image"				,				split="test"     )

							lowerCAmelCase_						=	[
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							    # RGBA
							    dataset[0]["file"],
							    # LA
							    dataset[1]["file"],
							    # L
							    dataset[2]["file"],
							]
							lowerCAmelCase_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

							self.assertEqual(len(_UpperCamelCase     )				,				len(_UpperCamelCase     )     )
							for outputs in batch_outputs:
										self.assertGreater(len(_UpperCamelCase     )				,				0     )
										for detected_object in outputs:
													self.assertEqual(
													    _UpperCamelCase				,				{
													        "score": ANY(_UpperCamelCase     ),
													        "label": ANY(_UpperCamelCase     ),
													        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
													    }				,				)




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=0.0     )

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]				,				threshold=0.0				,				)

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)




				@require_torch
				@require_pytesseract
				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							    ]				,				)



