"""simple docstring"""





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowerCAmelCase_     =	False




class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				pass




@nightly
@require_torch_gpu
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





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



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion""")
											# remove text_unet
											pipe.remove_unused_weights()
											pipe.to(_A)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    """A painting of a squirrel eating a burger """
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						str			  =    pipe(
											    prompt=_A      ,    generator=_A      ,    guidance_scale=7.5      ,    num_inference_steps=2      ,    output_type="""numpy""").images

											with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(_A)
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    VersatileDiffusionTextToImagePipeline.from_pretrained(_A)
											pipe.to(_A)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    generator.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Any			  =    pipe(
											    prompt=_A      ,    generator=_A      ,    guidance_scale=7.5      ,    num_inference_steps=2      ,    output_type="""numpy""").images

											assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
											    """shi-labs/versatile-diffusion"""      ,    torch_dtype=torch.floataa)
											pipe.to(_A)
											pipe.set_progress_bar_config(disable=_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    """A painting of a squirrel eating a burger """
											_SCREAMING_SNAKE_CASE		:						Dict			  =    torch.manual_seed(0)
											_SCREAMING_SNAKE_CASE		:						Any			  =    pipe(
											    prompt=_A      ,    generator=_A      ,    guidance_scale=7.5      ,    num_inference_steps=5_0      ,    output_type="""numpy""").images

											_SCREAMING_SNAKE_CASE		:						Dict			  =    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2



"""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]


