
"""simple docstring"""






from typing import Any, Dict, 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():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


__UpperCAmelCase 					=    logging.get_logger(__name__)


__UpperCAmelCase 					=    Dict[str, Any]
__UpperCAmelCase 					=    List[Prediction]




@add_end_docstrings(A__				)
class      _SCREAMING_SNAKE_CASE   (     A__				):






			def __init__(   self			,  *__A			,  **__A			)     ->	List[str]:
						super().__init__(*__A			,  **__A			)

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

						requires_backends(self			,  """vision"""			)
						self.check_model_type(
						    dict(
						        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
						        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
						        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
						        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						lowerCAmelCase_       :str     	=    {}
						lowerCAmelCase_       :List[Any]     	=    {}
						if "subtask" in kwargs:
									lowerCAmelCase_       :Dict     	=    kwargs["""subtask"""]
									lowerCAmelCase_       :Dict     	=    kwargs["""subtask"""]
						if "threshold" in kwargs:
									lowerCAmelCase_       :List[Any]     	=    kwargs["""threshold"""]
						if "mask_threshold" in kwargs:
									lowerCAmelCase_       :int     	=    kwargs["""mask_threshold"""]
						if "overlap_mask_area_threshold" in kwargs:
									lowerCAmelCase_       :Dict     	=    kwargs["""overlap_mask_area_threshold"""]

						return preprocess_kwargs, {}, postprocess_kwargs






			def __call__(   self			,  __A			,  **__A			)     ->	Union[Predictions, List[Prediction]]:
						return super().__call__(__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=None			)     ->	Tuple:
						lowerCAmelCase_       :Tuple     	=    load_image(__A			)
						lowerCAmelCase_       :List[str]     	=    [(image.height, image.width)]
						if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
												lowerCAmelCase_       :List[Any]     	=    {}
									else:
												lowerCAmelCase_       :Optional[Any]     	=    {"""task_inputs""": [subtask]}
									lowerCAmelCase_       :int     	=    self.image_processor(images=[image]			,  return_tensors="""pt"""			,  **__A			)
									lowerCAmelCase_       :Optional[int]     	=    self.tokenizer(
									    inputs["""task_inputs"""]			,  padding="""max_length"""			,  max_length=self.model.config.task_seq_len			,  return_tensors=self.framework			,  )["""input_ids"""]
						else:
									lowerCAmelCase_       :Any     	=    self.image_processor(images=[image]			,  return_tensors="""pt"""			)
						lowerCAmelCase_       :Optional[int]     	=    target_size
						return inputs






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Any:
						lowerCAmelCase_       :Union[str, Any]     	=    model_inputs.pop("""target_size"""			)
						lowerCAmelCase_       :Union[str, Any]     	=    self.model(**__A			)
						lowerCAmelCase_       :Any     	=    target_size
						return model_outputs






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=None			,  __A=0.9			,  __A=0.5			,  __A=0.5			)     ->	Any:
						lowerCAmelCase_       :Optional[int]     	=    None
						if subtask in {"panoptic", None} and hasattr(self.image_processor			,  """post_process_panoptic_segmentation"""			):
									lowerCAmelCase_       :Optional[int]     	=    self.image_processor.post_process_panoptic_segmentation
						elif subtask in {"instance", None} and hasattr(self.image_processor			,  """post_process_instance_segmentation"""			):
									lowerCAmelCase_       :str     	=    self.image_processor.post_process_instance_segmentation

						if fn is not None:
									lowerCAmelCase_       :Any     	=    fn(
									    __A			,  threshold=__A			,  mask_threshold=__A			,  overlap_mask_area_threshold=__A			,  target_sizes=model_outputs["""target_size"""]			,  )[0]

									lowerCAmelCase_       :Union[str, Any]     	=    []
									lowerCAmelCase_       :str     	=    outputs["""segmentation"""]

									for segment in outputs["segments_info"]:
												lowerCAmelCase_       :str     	=    (segmentation == segment["""id"""]) * 255
												lowerCAmelCase_       :str     	=    Image.fromarray(mask.numpy().astype(np.uinta			)			,  mode="""L"""			)
												lowerCAmelCase_       :List[str]     	=    self.model.config.idalabel[segment["""label_id"""]]
												lowerCAmelCase_       :List[Any]     	=    segment["""score"""]
												annotation.append({"""score""": score, """label""": label, """mask""": mask}			)

						elif subtask in {"semantic", None} and hasattr(self.image_processor			,  """post_process_semantic_segmentation"""			):
									lowerCAmelCase_       :List[Any]     	=    self.image_processor.post_process_semantic_segmentation(
									    __A			,  target_sizes=model_outputs["""target_size"""]			)[0]

									lowerCAmelCase_       :List[Any]     	=    []
									lowerCAmelCase_       :Optional[int]     	=    outputs.numpy()
									lowerCAmelCase_       :Dict     	=    np.unique(__A			)

									for label in labels:
												lowerCAmelCase_       :Optional[Any]     	=    (segmentation == label) * 255
												lowerCAmelCase_       :Tuple     	=    Image.fromarray(mask.astype(np.uinta			)			,  mode="""L"""			)
												lowerCAmelCase_       :int     	=    self.model.config.idalabel[label]
												annotation.append({"""score""": None, """label""": label, """mask""": mask}			)
						else:
									raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model			)}"""			)
						return annotation








"""simple docstring"""






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class      _SCREAMING_SNAKE_CASE   (     A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[str]												=		GPTNeoXJapaneseTokenizer
			UpperCAmelCase_    :List[str]												=		False
			UpperCAmelCase_    :int												=		{"do_clean_text": False, "add_prefix_space": False}






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						super().setUp()

						lowerCAmelCase_       :int     	=    [
						    """こん""",
						    """こんに""",
						    """にちは""",
						    """ばんは""",
						    """世界,㔺界""",
						    """、""",
						    """。""",
						    """<BR>""",
						    """<SP>""",
						    """<TAB>""",
						    """<URL>""",
						    """<EMAIL>""",
						    """<TEL>""",
						    """<DATE>""",
						    """<PRICE>""",
						    """<BLOCK>""",
						    """<KIGOU>""",
						    """<U2000U2BFF>""",
						    """<|emoji1|>""",
						    """<unk>""",
						    """<|startoftext|>""",
						    """<|endoftext|>""",
						]
						lowerCAmelCase_       :str     	=    {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
						lowerCAmelCase_       :Dict     	=    {"""unk_token""": """<unk>"""}

						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""vocab_file"""]			)
						lowerCAmelCase_       :Union[str, Any]     	=    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["""emoji_file"""]			)
						with open(self.vocab_file			,  """w"""			,  encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
						with open(self.emoji_file			,  """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__A			)			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	str:
						kwargs.update(self.special_tokens_map			)
						return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    """こんにちは、世界。 \nこんばんは、㔺界。😀"""
						lowerCAmelCase_       :List[Any]     	=    """こんにちは、世界。 \nこんばんは、世界。😀"""
						return input_text, output_text






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Optional[int]     	=    self.get_input_output_texts(__A			)
						lowerCAmelCase_       :List[Any]     	=    tokenizer.encode(__A			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Optional[Any]     	=    tokenizer.decode(__A			,  clean_up_tokenization_spaces=__A			)
						return text, ids






			def 					__lowerCAmelCase					(   self			)     ->	str:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						pass  # TODO add if relevant






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :int     	=    self.get_tokenizer()

						# Testing tokenization
						lowerCAmelCase_       :List[str]     	=    """こんにちは、世界。　こんばんは、㔺界。"""
						lowerCAmelCase_       :int     	=    ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
						lowerCAmelCase_       :List[Any]     	=    tokenizer.tokenize(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids without special tokens
						lowerCAmelCase_       :List[str]     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
						lowerCAmelCase_       :Dict     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)

						# Testing conversion to ids with special tokens
						lowerCAmelCase_       :Tuple     	=    tokens + [tokenizer.unk_token]
						lowerCAmelCase_       :str     	=    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
						lowerCAmelCase_       :Any     	=    tokenizer.convert_tokens_to_ids(__A			)
						self.assertListEqual(__A			,  __A			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :str     	=    self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.encode("""ありがとう。"""			,  add_special_tokens=__A			)
						lowerCAmelCase_       :Dict     	=    tokenizer.encode("""どういたしまして。"""			,  add_special_tokens=__A			)

						lowerCAmelCase_       :Union[str, Any]     	=    tokenizer.build_inputs_with_special_tokens(__A			)
						lowerCAmelCase_       :Optional[int]     	=    tokenizer.build_inputs_with_special_tokens(__A			,  __A			)

						assert encoded_sentence == ids_a
						assert encoded_pair == ids_a + ids_a






			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						# Intentionally convert some words to accommodate character fluctuations unique to Japanese
						pass






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						# tokenizer has no padding token
						pass






