





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCAmelCase_      ):
 __SCREAMING_SNAKE_CASE							       =	(UniPCMultistepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(("""num_inference_steps""", 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''solver_type''': '''bh2''',
        }

        config.update(**__lowercase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowercase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowercase )
               A__											=						scheduler_class(**__lowercase )
               scheduler.set_timesteps(__lowercase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowercase )
                      A__											=						scheduler_class.from_pretrained(__lowercase )
                      new_scheduler.set_timesteps(__lowercase )
                      # copy over dummy past residuals
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						sample, sample
               for t in range(__lowercase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample
                      A__											=						new_scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample

                      assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowercase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowercase )
               scheduler.set_timesteps(__lowercase )

               # copy over dummy past residuals (must be after setting timesteps)
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowercase )
                      A__											=						scheduler_class.from_pretrained(__lowercase )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__lowercase )

                      # copy over dummy past residual (must be after setting timesteps)
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample
               A__											=						new_scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample

               assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowercase )
               A__											=						scheduler_class(**__lowercase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowercase )
        A__											=						scheduler_class(**__lowercase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowercase,__lowercase )
               A__											=						scheduler.step(__lowercase,__lowercase,__lowercase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						dict(self.forward_default_kwargs )

        A__											=						kwargs.pop('''num_inference_steps''',__lowercase )

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowercase )

               A__											=						self.dummy_sample
               A__											=						0.1 * sample

               if num_inference_steps is not None and hasattr(__lowercase,'''set_timesteps''' ):
                      scheduler.set_timesteps(__lowercase )
               elif num_inference_steps is not None and not hasattr(__lowercase,'''set_timesteps''' ):
                      A__											=						num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               A__											=						scheduler.timesteps[5]
               A__											=						scheduler.timesteps[6]

               A__											=						scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample
               A__											=						scheduler.step(__lowercase,__lowercase,__lowercase,**__lowercase ).prev_sample

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



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						UniPCMultistepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowercase )
        A__											=						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2464 ) < 1E-3

        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )
        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowercase )
        A__											=						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2464 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowercase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowercase )
        for order in [1, 2, 3]:
               for solver_type in ["bh1", "bh2"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__lowercase,prediction_type=__lowercase,sample_max_value=__lowercase,solver_order=__lowercase,solver_type=__lowercase,)



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowercase )



 def 				UpperCamelCase			(							self ):
        for solver_type in ["bh1", "bh2"]:
               for order in [1, 2, 3]:
                      for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 solver_order=__lowercase,solver_type=__lowercase,prediction_type=__lowercase,)
                             A__											=						self.full_loop(
                                 solver_order=__lowercase,solver_type=__lowercase,prediction_type=__lowercase,)
                             assert not torch.isnan(__lowercase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowercase )
        self.check_over_configs(lower_order_final=__lowercase )



 def 				UpperCamelCase			(							self ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowercase,time_step=0 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.2464 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowercase ) )

        assert abs(result_mean.item() - 0.1014 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowercase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowercase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowercase,__lowercase )
               A__											=						scheduler.step(__lowercase,__lowercase,__lowercase ).prev_sample

        assert sample.dtype == torch.floataa




 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowercase )
               A__											=						scheduler_class(**__lowercase )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps )
               assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps






# 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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """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__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





