

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase								=			{'LayoutLMv2Config', 'LayoutLMv3Config'}



@is_pipeline_test
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      int										=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  lowerCamelCase   :      Optional[Any]										=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    lowerCamelCase   :      Tuple										={config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    lowerCamelCase   :      Optional[Any]										={
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }







  @require_torch
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]         =						pipeline(
         task="""text-classification"""	,							model="""hf-internal-testing/tiny-random-distilbert"""	,							framework="""pt"""   )

     __lowerCAmelCase  :      Optional[int]         =						text_classifier("""This is great !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}]   )

     __lowerCAmelCase  :      Optional[Any]         =						text_classifier("""This is great !"""	,							top_k=2   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]   )

     __lowerCAmelCase  :      List[Any]         =						text_classifier(["""This is great !""", """This is bad"""]	,							top_k=2   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[
             [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
             [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
         ]	,							)

     __lowerCAmelCase  :      Any         =						text_classifier("""This is great !"""	,							top_k=1   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}]   )

     # Legacy behavior
     __lowerCAmelCase  :      Any         =						text_classifier("""This is great !"""	,							return_all_scores=_SCREAMING_SNAKE_CASE   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}]   )

     __lowerCAmelCase  :      List[str]         =						text_classifier("""This is great !"""	,							return_all_scores=_SCREAMING_SNAKE_CASE   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]]   )

     __lowerCAmelCase  :      List[str]         =						text_classifier(["""This is great !""", """Something else"""]	,							return_all_scores=_SCREAMING_SNAKE_CASE   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[
             [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
             [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
         ]	,							)

     __lowerCAmelCase  :      int         =						text_classifier(["""This is great !""", """Something else"""]	,							return_all_scores=_SCREAMING_SNAKE_CASE   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[
             {"""label""": """LABEL_0""", """score""": 0.504},
             {"""label""": """LABEL_0""", """score""": 0.504},
         ]	,							)







  @require_torch
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[int]:






     """simple docstring"""







     import torch

     __lowerCAmelCase  :      Union[str, Any]         =						pipeline(
         task="""text-classification"""	,							model="""hf-internal-testing/tiny-random-distilbert"""	,							framework="""pt"""	,							device=torch.device("""cpu"""   )	,							)

     __lowerCAmelCase  :      Union[str, Any]         =						text_classifier("""This is great !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}]   )







  @require_tf
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple         =						pipeline(
         task="""text-classification"""	,							model="""hf-internal-testing/tiny-random-distilbert"""	,							framework="""tf"""   )

     __lowerCAmelCase  :      List[str]         =						text_classifier("""This is great !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """LABEL_0""", """score""": 0.504}]   )







  @slow
  @require_torch
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]         =						pipeline("""text-classification"""   )

     __lowerCAmelCase  :      List[str]         =						text_classifier("""This is great !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """POSITIVE""", """score""": 1.0}]   )
     __lowerCAmelCase  :      str         =						text_classifier("""This is bad !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """NEGATIVE""", """score""": 1.0}]   )
     __lowerCAmelCase  :      Optional[int]         =						text_classifier("""Birds are a type of animal"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """POSITIVE""", """score""": 0.988}]   )







  @slow
  @require_tf
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple         =						pipeline("""text-classification"""	,							framework="""tf"""   )

     __lowerCAmelCase  :      str         =						text_classifier("""This is great !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """POSITIVE""", """score""": 1.0}]   )
     __lowerCAmelCase  :      Dict         =						text_classifier("""This is bad !"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """NEGATIVE""", """score""": 1.0}]   )
     __lowerCAmelCase  :      str         =						text_classifier("""Birds are a type of animal"""   )
     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": """POSITIVE""", """score""": 0.988}]   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Any   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]         =						TextClassificationPipeline(model=_SCREAMING_SNAKE_CASE	,							tokenizer=_SCREAMING_SNAKE_CASE   )
     return text_classifier, ["HuggingFace is in", "This is another test"]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Any   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]         =						text_classifier.model
     # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
     __lowerCAmelCase  :      Union[str, Any]         =						"HuggingFace is in"
     __lowerCAmelCase  :      Optional[int]         =						text_classifier(_SCREAMING_SNAKE_CASE   )

     self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}]   )
     self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()   )

     __lowerCAmelCase  :      Tuple         =						["HuggingFace is in ", "Paris is in France"]
     __lowerCAmelCase  :      Optional[Any]         =						text_classifier(_SCREAMING_SNAKE_CASE   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}, {"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}]	,							)
     self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()   )
     self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values()   )

     # Forcing to get all results with `top_k=None`
     # This is NOT the legacy format
     __lowerCAmelCase  :      int         =						text_classifier(_SCREAMING_SNAKE_CASE	,							top_k=_SCREAMING_SNAKE_CASE   )
     __lowerCAmelCase  :      Union[str, Any]         =						len(model.config.idalabel.values()   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[[{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}] * N, [{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}] * N]	,							)

     __lowerCAmelCase  :      Optional[int]         =						{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
     __lowerCAmelCase  :      str         =						text_classifier(_SCREAMING_SNAKE_CASE   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}	,							)
     self.assertTrue(outputs["""label"""] in model.config.idalabel.values()   )

     # This might be used a text pair, but tokenizer + pipe interaction
     # makes it hard to understand that it's not using the pair properly
     # https://github.com/huggingface/transformers/issues/17305
     # We disabled this usage instead as it was outputting wrong outputs.
     __lowerCAmelCase  :      Optional[int]         =						[["HuggingFace is in ", "Paris is in France"]]
     with self.assertRaises(_SCREAMING_SNAKE_CASE   ):
        text_classifier(_SCREAMING_SNAKE_CASE   )

     # This used to be valid for doing text pairs
     # We're keeping it working because of backward compatibility
     __lowerCAmelCase  :      List[Any]         =						text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]]   )
     self.assertEqual(
         nested_simplify(_SCREAMING_SNAKE_CASE   )	,							[{"""label""": ANY(_SCREAMING_SNAKE_CASE   ), """score""": ANY(_SCREAMING_SNAKE_CASE   )}]	,							)
     self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values()   )





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				()      ->     str:
   # Get the sagemaker specific mp parameters from smp_options variable.
   __lowerCAmelCase  :      List[Any]    =						os.getenv("""SM_HP_MP_PARAMETERS""" ,       """{}"""					)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      __lowerCAmelCase  :      Any    =						json.loads(__A					)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   __lowerCAmelCase  :      Any    =						os.getenv("""SM_FRAMEWORK_PARAMS""" ,       """{}"""					)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      __lowerCAmelCase  :      Union[str, Any]    =						json.loads(__A					)
      if not mpi_options.get("""sagemaker_mpi_enabled""" ,       __A					):
         return False
   except json.JSONDecodeError:
      return False
   # Lastly, check if the `smdistributed` module is present.
   return importlib.util.find_spec("""smdistributed"""					) is not None


if is_sagemaker_model_parallel_available():
   import smdistributed.modelparallel.torch as smp

   smp.init()



@dataclass
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      default=""       ,							metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}       ,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     super().__post_init__()
     warnings.warn(
         """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
         """`TrainingArguments` instead."""	,							lowerCAmelCase	,							)







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     "torch.device":






     """simple docstring"""







     logger.info("""PyTorch: setting up devices"""   )
     if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
        logger.warning(
            """torch.distributed process group is initialized, but local_rank == -1. """
            """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""   )
     if self.no_cuda:
        __lowerCAmelCase  :      Optional[Any]    =						torch.device("""cpu"""   )
        __lowerCAmelCase  :      Union[str, Any]    =						0
     elif is_sagemaker_model_parallel_available():
        __lowerCAmelCase  :      int    =						smp.local_rank()
        __lowerCAmelCase  :      str    =						torch.device("""cuda"""	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						1
     elif is_sagemaker_dp_enabled():
        import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

        torch.distributed.init_process_group(backend="""smddp"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""   )   )
        __lowerCAmelCase  :      Tuple    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      Optional[int]    =						1
     elif self.local_rank == -1:
        # if n_gpu is > 1 we'll use nn.DataParallel.
        # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
        # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
        # trigger an error that a device index is missing. Index 0 takes into account the
        # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
        # will use the first GPU in that env, i.e. GPU#1
        __lowerCAmelCase  :      List[Any]    =						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
        # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
        # the default value.
        __lowerCAmelCase  :      Optional[int]    =						torch.cuda.device_count()
     else:
        # Here, we'll use torch.distributed.
        # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
        if not torch.distributed.is_initialized():
           torch.distributed.init_process_group(backend="""nccl"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      int    =						1

     if device.type == "cuda":
        torch.cuda.set_device(lowerCAmelCase   )

     return device







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return not is_sagemaker_model_parallel_available()







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     return False


