"""simple docstring"""





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest




class  _snake_case		(    _UpperCAmelCase ):







				"""simple docstring"""






				a			=						(CMStochasticIterativeScheduler,)
				a			=						10





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



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    {
											    """num_train_timesteps""": 2_0_1,
											    """sigma_min""": 0.002,
											    """sigma_max""": 80.0,
											}

											config.update(**lowercase_)
											return config





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    1_0

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.scheduler_classes[0](**lowercase_)

											scheduler.set_timesteps(lowercase_)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    scheduler.timesteps[0]
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    scheduler.timesteps[1]

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.dummy_sample
											_SCREAMING_SNAKE_CASE		:						Any			  =    0.1 * sample

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    scheduler.step(lowercase_      ,    lowercase_      ,    lowercase_).prev_sample
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    scheduler.step(lowercase_      ,    lowercase_      ,    lowercase_).prev_sample

											self.assertEqual(output_a.shape      ,    sample.shape)
											self.assertEqual(output_a.shape      ,    output_a.shape)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																		self.check_over_configs(num_train_timesteps=lowercase_)





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



											"""simple docstring"""







											for clip_denoised in [True, False]:
																		self.check_over_configs(clip_denoised=lowercase_)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    scheduler_class(**lowercase_)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    1
											scheduler.set_timesteps(lowercase_)
											_SCREAMING_SNAKE_CASE		:						Any			  =    scheduler.timesteps

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.manual_seed(0)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.dummy_model()
											_SCREAMING_SNAKE_CASE		:						int			  =    self.dummy_sample_deter * scheduler.init_noise_sigma

											for i, t in enumerate(lowercase_):
																		# 1. scale model input
																		_SCREAMING_SNAKE_CASE		:						int			  =    scheduler.scale_model_input(lowercase_      ,    lowercase_)

																		# 2. predict noise residual
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(lowercase_      ,    lowercase_)

																		# 3. predict previous sample x_t-1
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    scheduler.step(lowercase_      ,    lowercase_      ,    lowercase_      ,    generator=lowercase_).prev_sample

																		_SCREAMING_SNAKE_CASE		:						str			  =    pred_prev_sample

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.sum(torch.abs(lowercase_))
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 1_9_2.7_6_1_4) < 1e-2
											assert abs(result_mean.item() - 0.2_510) < 1e-3





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    scheduler_class(**lowercase_)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [1_0_6, 0]
											scheduler.set_timesteps(timesteps=lowercase_)
											_SCREAMING_SNAKE_CASE		:						str			  =    scheduler.timesteps

											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.manual_seed(0)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.dummy_model()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.dummy_sample_deter * scheduler.init_noise_sigma

											for t in timesteps:
																		# 1. scale model input
																		_SCREAMING_SNAKE_CASE		:						Any			  =    scheduler.scale_model_input(lowercase_      ,    lowercase_)

																		# 2. predict noise residual
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(lowercase_      ,    lowercase_)

																		# 3. predict previous sample x_t-1
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    scheduler.step(lowercase_      ,    lowercase_      ,    lowercase_      ,    generator=lowercase_).prev_sample

																		_SCREAMING_SNAKE_CASE		:						Dict			  =    pred_prev_sample

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.sum(torch.abs(lowercase_))
											_SCREAMING_SNAKE_CASE		:						str			  =    torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 3_4_7.6_3_5_7) < 1e-2
											assert abs(result_mean.item() - 0.4_527) < 1e-3





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    scheduler_class(**lowercase_)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [3_9, 3_0, 1_2, 1_5, 0]

											with self.assertRaises(lowercase_      ,    msg="""`timesteps` must be in descending order."""):
																		scheduler.set_timesteps(timesteps=lowercase_)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    scheduler_class(**lowercase_)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [3_9, 3_0, 1_2, 1, 0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    len(lowercase_)

											with self.assertRaises(lowercase_      ,    msg="""Can only pass one of `num_inference_steps` or `timesteps`."""):
																		scheduler.set_timesteps(num_inference_steps=lowercase_      ,    timesteps=lowercase_)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE		:						str			  =    self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    scheduler_class(**lowercase_)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    [scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    lowercase_      ,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""      ,    ):
																		scheduler.set_timesteps(timesteps=lowercase_)



"""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]


