"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
			from PIL import Image
else:




			class  _snake_case		:







							"""simple docstring"""






							@staticmethod
							def 		_lowerCAmelCase		(      *_A							:							List[str]      ,    **_A							:							Tuple):



														"""simple docstring"""







														pass



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    hashlib.mda(image.tobytes()  )
							return m.hexdigest()[:10]



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    np.array(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    npimg.shape
							return {"hash": hashimage(__SCREAMING_SNAKE_CASE  ), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				a			=						dict(
				    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
				a			=						dict(
				    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							Optional[int]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    MaskGenerationPipeline(model=_A      ,    image_processor=_A)
											return image_segmenter, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]





				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							Union[str, Any]      ,    _A							:							str):



											"""simple docstring"""







											pass





				@require_tf
				@unittest.skip("""Image segmentation not implemented in TF""")
				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											pass





				@slow
				@require_torch
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pipeline("""mask-generation"""      ,    model="""facebook/sam-vit-huge""")

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""      ,    points_per_batch=2_5_6)

											# Shortening by hashing
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    []
											for i, o in enumerate(outputs["""masks"""]):
																		new_outupt += [{"mask": mask_to_test_readable(_A), "scores": outputs["scores"][i]}]

											# fmt: off
											self.assertEqual(
											    nested_simplify(_A      ,    decimals=4)      ,    [
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_444},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.021},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_167},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_132},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_053},
											        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_967},
											        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.993},
											        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_909},
											        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_879},
											        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_834},
											        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_716},
											        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_612},
											        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_599},
											        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_552},
											        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_532},
											        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_516},
											        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_499},
											        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_483},
											        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_464},
											        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.943},
											        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.943},
											        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_408},
											        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_335},
											        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_326},
											        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.9_262},
											        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.8_999},
											        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.8_986},
											        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.8_984},
											        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.8_873},
											        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_8_0, 6_4_0)}, """scores""": 0.8_871}
											    ]      ,    )
											# fmt: on





				@require_torch
				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    """facebook/sam-vit-huge"""
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pipeline("""mask-generation"""      ,    model=_A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_segmenter(
											    """http://images.cocodataset.org/val2017/000000039769.jpg"""      ,    pred_iou_thresh=1      ,    points_per_batch=2_5_6)

											# Shortening by hashing
											_SCREAMING_SNAKE_CASE		:						Any			  =    []
											for i, o in enumerate(outputs["""masks"""]):
																		new_outupt += [{"mask": mask_to_test_readable(_A), "scores": outputs["scores"][i]}]

											self.assertEqual(
											    nested_simplify(_A      ,    decimals=4)      ,    [
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_444},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_210},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_167},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_132},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_8_0, 6_4_0)}, """scores""": 1.0_053},
											    ]      ,    )



"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"facebook/bart-large-mnli"
				a			=						(
				    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
				    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
				    "It returns the most likely label in the list of provided `labels` for the input text."
				)
				a			=						"text_classifier"
				a			=						AutoTokenizer
				a			=						AutoModelForSequenceClassification

				a			=						["text", ["text"]]
				a			=						["text"]





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setup()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model.config
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    int(_A)
											if self.entailment_id == -1:
																		raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""")





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    labels
											return self.pre_processor(
											    [text] * len(_A)      ,    [f"""This example is {label}""" for label in labels]      ,    return_tensors="""pt"""      ,    padding="""max_length"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.argmax(logits[:, 2]).item()
											return self._labels[label_id]


