import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


      return EnvironmentCommand()




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return EnvironmentCommand(args.accelerate_config_file							)







class 					A							(    UpperCAmelCase_					):


   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						ArgumentParser		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  parser.add_parser("env"		)
         download_parser.set_defaults(func=__UpperCAmelCase		)
         download_parser.add_argument(
             "--accelerate-config_file"	,  default=__UpperCAmelCase	,  help="The accelerate config file to use for the default values in the launching script."	,  )
         download_parser.set_defaults(func=__UpperCAmelCase		)


   def __init__(self					:						Tuple	,  __UpperCAmelCase					:						Union[str, Any]	,  *__UpperCAmelCase					:						Optional[int]		)						->    None:
         """simple docstring"""



         UpperCAmelCase__								=  accelerate_config_file


   def       lowercase_				(self					:						Tuple		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  "not installed"
         if is_safetensors_available():
               import safetensors

               UpperCAmelCase__								=  safetensors.__version__
         elif importlib.util.find_spec("safetensors"		) is not None:
               import safetensors

               UpperCAmelCase__								=  f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  UpperCAmelCase__								=  "not found"
         if is_accelerate_available():
               import accelerate
               from accelerate.commands.config import default_config_file, load_config_from_file

               UpperCAmelCase__								=  accelerate.__version__
               # Get the default from the config file.
               if self._accelerate_config_file is not None or os.path.isfile(__UpperCAmelCase		):
                     UpperCAmelCase__								=  load_config_from_file(self._accelerate_config_file		).to_dict()

               UpperCAmelCase__								=  (
                   "\n".join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]		)
                   if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		)
                   else f"""\t{accelerate_config}"""
               )

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_torch_available():
               import torch

               UpperCAmelCase__								=  torch.__version__
               UpperCAmelCase__								=  torch.cuda.is_available()

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_tf_available():
               import tensorflow as tf

               UpperCAmelCase__								=  tf.__version__
               try:
                     # deprecated in v2.1
                     UpperCAmelCase__								=  tf.test.is_gpu_available()
               except AttributeError:
                     # returns list of devices, convert to bool
                     UpperCAmelCase__								=  bool(tf.config.list_physical_devices("GPU"		)		)

         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "not installed"
         UpperCAmelCase__								=  "NA"
         if is_flax_available():
               import flax
               import jax
               import jaxlib

               UpperCAmelCase__								=  flax.__version__
               UpperCAmelCase__								=  jax.__version__
               UpperCAmelCase__								=  jaxlib.__version__
               UpperCAmelCase__								=  jax.lib.xla_bridge.get_backend().platform

         UpperCAmelCase__								=  {
             "`transformers` version": version,
             "Platform": platform.platform(),
             "Python version": platform.python_version(),
             "Huggingface_hub version": huggingface_hub.__version__,
             "Safetensors version": f"""{safetensors_version}""",
             "Accelerate version": f"""{accelerate_version}""",
             "Accelerate config": f"""{accelerate_config_str}""",
             "PyTorch version (GPU?)": f"""{pt_version} ({pt_cuda_available})""",
             "Tensorflow version (GPU?)": f"""{tf_version} ({tf_cuda_available})""",
             "Flax version (CPU?/GPU?/TPU?)": f"""{flax_version} ({jax_backend})""",
             "Jax version": f"""{jax_version}""",
             "JaxLib version": f"""{jaxlib_version}""",
             "Using GPU in script?": "<fill in>",
             "Using distributed or parallel set-up in script?": "<fill in>",
         }

         print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"		)
         print(self.format_dict(__UpperCAmelCase		)		)

         return info





   @staticmethod
   def       lowercase_				(__UpperCAmelCase					:						Tuple		)						->    Dict:
         """simple docstring"""



         return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]		) + "\n"




# 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 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       '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			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



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





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



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



