





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin






class 							_a(     __A						, unittest.TestCase     ):
		lowerCamelCase__  :Union[str, Any] 					=		SpeechaTextaTokenizer
		lowerCamelCase__  :Any 					=		False







		def 							lowercase							(     self					)   ->   List[str]:


							'''simple docstring'''

							super().setUp()

							_snake_case							:       Tuple												= "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "					)
							_snake_case							:       List[str]												= ["he re</w> 123", "here a 1456"]
							_snake_case							:       List[Any]												= dict(zip(__snake_case			,    range(len(__snake_case					)					)					)					)

							_snake_case							:       Optional[int]												= {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

							_snake_case							:       Any												= tempfile.mkdtemp()
							_snake_case							:       int												= os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["vocab_file"]					)
							_snake_case							:       Optional[Any]												= os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["merges_file"]					)

							with open(self.vocab_file			,    "w"			,    encoding="utf-8"					) as fp:
												fp.write(json.dumps(__snake_case					) + "\n"					)

							with open(self.merges_file			,    "w"					) as fp:
												fp.write("\n".join(__snake_case					)					)







		def 							lowercase							(     self					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= list(self.get_tokenizer().get_vocab().keys()					)

							self.assertEqual(vocab_keys[0]			,    "<s>"					)
							self.assertEqual(vocab_keys[1]			,    "<pad>"					)
							self.assertEqual(vocab_keys[-1]			,    "vocab"					)
							self.assertEqual(len(__snake_case					)			,    1_4					)







		def 							lowercase							(     self					)   ->   Optional[Any]:


							'''simple docstring'''

							self.assertEqual(self.get_tokenizer().vocab_size			,    1_4					)







		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							_snake_case							:       List[Any]												= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

							# make sure @@ is correctly concatenated
							_snake_case							:       Any												= [4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
							_snake_case							:       str												= tokenizer.decode(__snake_case					)

							self.assertTrue(output_string == "herecouple words ofthe"					)







		def 							lowercase							(     self					)   ->   str:


							'''simple docstring'''

							_snake_case							:       int												= SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(__snake_case					)
												os.remove(os.path.join(__snake_case			,    "merges.txt"					)					)

												# load tokenizer without merges file should not throw an error
												_snake_case							:       List[Any]												= SpeechaTextaTokenizer.from_pretrained(__snake_case					)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(__snake_case					)
												_snake_case							:       int												= SpeechaTextaTokenizer.from_pretrained(__snake_case					)

							self.assertIsNotNone(__snake_case					)







		def 							lowercase							(     self					)   ->   Dict:


							'''simple docstring'''

							if not self.test_slow_tokenizer:
												return

							_snake_case							:       Optional[Any]												= inspect.signature(self.tokenizer_class.__init__					)
							_snake_case							:       List[str]												= self.get_tokenizer()

							for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																	self.assertIn(__snake_case			,    tokenizer.init_kwargs					)






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,
							)


__lowerCAmelCase			:Optional[Any]	 =     logging.get_logger(__name__)


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case			,    **__snake_case					)

							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 							lowercase							(     self			,    **__snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

							return super().__call__(__snake_case			,    **__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

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







		def 							lowercase							(     self			,    __snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

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

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

							elif subtask in {"semantic", None} and hasattr(self.image_processor			,    "post_process_semantic_segmentation"					):
												_snake_case							:       Tuple												= self.image_processor.post_process_semantic_segmentation(
												    __snake_case			,    target_sizes=model_outputs["target_size"]					)[0]

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= 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