


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      return 1.0 / (1.0 + np.exp(-_outputs      ))






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[Any]:




      """simple docstring"""

      snake_case_ =   np.max(_outputs      ,      axis=-1      ,      keepdims=SCREAMING_SNAKE_CASE      )
      snake_case_ =   np.exp(_outputs - maxes      )
      return shifted_exp / shifted_exp.sum(axis=-1      ,      keepdims=SCREAMING_SNAKE_CASE      )







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"sigmoid"
 __snake_case				=						"softmax"
 __snake_case				=						"none"







@add_end_docstrings(
    lowerCamelCase__      ,		R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "      ,		)
class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						False
 __snake_case				=						ClassificationFunction.NONE



 def __init__(			self	,   **_UpperCAmelCase      ):
       super().__init__(**_UpperCAmelCase      )

       self.check_model_type(
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=""	,   **_UpperCAmelCase      ):
       # Using "" as default argument because we're going to use `top_k=None` in user code to declare
       # "No top_k"
       snake_case_ =   tokenizer_kwargs

       snake_case_ =   {}
       if hasattr(self.model.config	,   '''return_all_scores'''      ) and return_all_scores is None:
             snake_case_ =   self.model.config.return_all_scores

       if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) or top_k is None:
             snake_case_ =   top_k
             snake_case_ =   False
       elif return_all_scores is not None:
             warnings.warn(
                 '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                 ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''	,   _UpperCAmelCase	,   )
             if return_all_scores:
                   snake_case_ =   None
             else:
                   snake_case_ =   1

       if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             snake_case_ =   ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
             snake_case_ =   function_to_apply
       return preprocess_params, {}, postprocess_params



 def __call__(			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
       snake_case_ =   super().__call__(*_UpperCAmelCase	,   **_UpperCAmelCase      )
       # TODO try and retrieve it in a nicer way from _sanitize_parameters.
       snake_case_ =   '''top_k''' not in kwargs
       if isinstance(args[0]	,   _UpperCAmelCase      ) and _legacy:
             # This pipeline is odd, and return a list when single item is run
             return [result]
       else:
             return result



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   **_UpperCAmelCase      ):
       snake_case_ =   self.framework
       if isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             return self.tokenizer(**_UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      )
       elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ) and len(_UpperCAmelCase      ) == 1 and isinstance(inputs[0]	,   _UpperCAmelCase      ) and len(inputs[0]      ) == 2:
             # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
             return self.tokenizer(
                 text=inputs[0][0]	,   text_pair=inputs[0][1]	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      )
       elif isinstance(_UpperCAmelCase	,   _UpperCAmelCase      ):
             # This is likely an invalid usage of the pipeline attempting to pass text pairs.
             raise ValueError(
                 '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                 ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''      )
       return self.tokenizer(_UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   **_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       return self.model(**_UpperCAmelCase      )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=1	,   _UpperCAmelCase=True      ):
       # `_legacy` is used to determine if we're running the naked pipeline and in backward
       # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
       # the more natural result containing the list.
       # Default value before `set_parameters`
       if function_to_apply is None:
             if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   snake_case_ =   ClassificationFunction.SIGMOID
             elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   snake_case_ =   ClassificationFunction.SOFTMAX
             elif hasattr(self.model.config	,   '''function_to_apply'''      ) and function_to_apply is None:
                   snake_case_ =   self.model.config.function_to_apply
             else:
                   snake_case_ =   ClassificationFunction.NONE

       snake_case_ =   model_outputs['''logits'''][0]
       snake_case_ =   outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
             snake_case_ =   sigmoid(_UpperCAmelCase      )
       elif function_to_apply == ClassificationFunction.SOFTMAX:
             snake_case_ =   softmax(_UpperCAmelCase      )
       elif function_to_apply == ClassificationFunction.NONE:
             snake_case_ =   outputs
       else:
             raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''      )

       if top_k == 1 and _legacy:
             return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

       snake_case_ =   [
           {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(_UpperCAmelCase      )
       ]
       if not _legacy:
             dict_scores.sort(key=lambda _UpperCAmelCase      : x["score"]	,   reverse=_UpperCAmelCase      )
             if top_k is not None:
                   snake_case_ =   dict_scores[:top_k]
       return dict_scores



# Copyright 2021 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.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCAmelCase    =					"""pytorch_model.bin"""
UpperCAmelCase    =					"""pytorch_model.bin.index.json"""
UpperCAmelCase    =					"""adapter_config.json"""
UpperCAmelCase    =					"""adapter_model.bin"""
UpperCAmelCase    =					"""adapter_model.safetensors"""
UpperCAmelCase    =					"""tf_model.h5"""
UpperCAmelCase    =					"""tf_model.h5.index.json"""
UpperCAmelCase    =					"""model.ckpt"""
UpperCAmelCase    =					"""flax_model.msgpack"""
UpperCAmelCase    =					"""flax_model.msgpack.index.json"""
UpperCAmelCase    =					"""model.safetensors"""
UpperCAmelCase    =					"""model.safetensors.index.json"""
UpperCAmelCase    =					"""config.json"""
UpperCAmelCase    =					"""preprocessor_config.json"""
UpperCAmelCase    =					FEATURE_EXTRACTOR_NAME
UpperCAmelCase    =					"""generation_config.json"""
UpperCAmelCase    =					"""modelcard.json"""

UpperCAmelCase    =					"""▁"""
UpperCAmelCase    =					SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCAmelCase    =					[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCAmelCase    =					[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCAmelCase    =					[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						List[str]:




      """simple docstring"""

      if version.parse(SCREAMING_SNAKE_CASE      ) < version.parse(SCREAMING_SNAKE_CASE      ):
            if "dev" in min_version:
                  snake_case_ =   (
                      '''This example requires a source install from HuggingFace Transformers (see '''
                      '''`https://huggingface.co/docs/transformers/installation#install-from-source`),'''
                  )
            else:
                  snake_case_ =   f'''This example requires a minimum version of {min_version},'''
            error_message += f''' but the version found is {__version__}.\n'''
            raise ImportError(
                error_message
                + '''Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '''
                '''versions of HuggingFace Transformers.'''      )