

import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def        __lowerCAmelCase    (       )		->							List[Any]:
 '''simple docstring'''


 raise RuntimeError('CUDA out of memory.'	)




class      UpperCamelCase (     nn.Module   ):

    def __init__(			self :      Any    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			nn.Linear(3							,    4    )
     SCREAMING_SNAKE_CASE								=			nn.BatchNormad(4    )
     SCREAMING_SNAKE_CASE								=			nn.Linear(4							,    5    )




    def UpperCamelCase			(			self :      Any							,    snake_case__ :      str    ):

     """simple docstring"""






     return self.lineara(self.batchnorm(self.lineara(snake_case__    )    )    )




class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]

     @find_executable_batch_size(starting_batch_size=1_2_8    )
     def mock_training_loop_function(snake_case__ :      int    ):
      nonlocal batch_sizes
      batch_sizes.append(snake_case__    )
      if batch_size != 8:
       raise_fake_out_of_memory()

     mock_training_loop_function()
     self.assertListEqual(snake_case__							,    [1_2_8, 6_4, 3_2, 1_6, 8]    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]

     @find_executable_batch_size(starting_batch_size=1_2_8    )
     def mock_training_loop_function(snake_case__ :      Union[str, Any]							,    snake_case__ :      Any    ):
      nonlocal batch_sizes
      batch_sizes.append(snake_case__    )
      if batch_size != 8:
       raise_fake_out_of_memory()
      return batch_size, arga

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			mock_training_loop_function('hello'    )
     self.assertListEqual(snake_case__							,    [1_2_8, 6_4, 3_2, 1_6, 8]    )
     self.assertListEqual([bs, arga]							,    [8, 'hello']    )

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

     """simple docstring"""






     @find_executable_batch_size(starting_batch_size=0    )
     def mock_training_loop_function(snake_case__ :      Union[str, Any]    ):
      pass

     with self.assertRaises(snake_case__    ) as cm:
      mock_training_loop_function()
      self.assertIn('No executable batch size found, reached zero.'							,    cm.exception.args[0]    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     @find_executable_batch_size(starting_batch_size=1_6    )
     def mock_training_loop_function(snake_case__ :      Union[str, Any]    ):
      if batch_size > 0:
       raise_fake_out_of_memory()
      pass

     with self.assertRaises(snake_case__    ) as cm:
      mock_training_loop_function()
      self.assertIn('No executable batch size found, reached zero.'							,    cm.exception.args[0]    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     @find_executable_batch_size(starting_batch_size=1_2_8    )
     def mock_training_loop_function(snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      Tuple    ):
      if batch_size != 8:
       raise raise_fake_out_of_memory()

     with self.assertRaises(snake_case__    ) as cm:
      mock_training_loop_function(1_2_8							,    'hello'							,    'world'    )
      self.assertIn('Batch size was passed into `f`'							,    cm.exception.args[0]    )
      self.assertIn('`f(arg1=\'hello\', arg2=\'world\')'							,    cm.exception.args[0]    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     @find_executable_batch_size(starting_batch_size=1_6    )
     def mock_training_loop_function(snake_case__ :      Dict    ):
      raise ValueError('Oops, we had an error!'    )

     with self.assertRaises(snake_case__    ) as cm:
      mock_training_loop_function()
      self.assertIn('Oops, we had an error!'							,    cm.exception.args[0]    )




    @require_cuda
    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.cuda.memory_allocated()
     SCREAMING_SNAKE_CASE								=			ModelForTest()
     model.cuda()
     self.assertGreater(torch.cuda.memory_allocated()							,    snake_case__    )
     SCREAMING_SNAKE_CASE								=			release_memory(snake_case__    )
     self.assertEqual(torch.cuda.memory_allocated()							,    snake_case__    )




# 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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="facebook/bart-large-mnli"
    __UpperCamelCase         =(
        "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."
    )
    __UpperCamelCase         ="text_classifier"
    __UpperCamelCase         =AutoTokenizer
    __UpperCamelCase         =AutoModelForSequenceClassification

    __UpperCamelCase         =["text", ["text"]]
    __UpperCamelCase         =["text"]

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     super().setup()
     SCREAMING_SNAKE_CASE								=			self.model.config
     SCREAMING_SNAKE_CASE								=			-1
     for idx, label in config.idalabel.items():
      if label.lower().startswith('entail'    ):
       SCREAMING_SNAKE_CASE								=			int(snake_case__    )
     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 :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			labels
     return self.pre_processor(
         [text] * len(snake_case__    )							,    [F"""This example is {label}""" for label in labels]							,    return_tensors='pt'							,    padding='max_length'							,    )




    def UpperCamelCase			(			self :      Dict							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			outputs.logits
     SCREAMING_SNAKE_CASE								=			torch.argmax(logits[:, 2]    ).item()
     return self._labels[label_id]

