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.
SCREAMING_SNAKE_CASE			:						Any				  =		{"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class  A_			(							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  _SCREAMING_SNAKE_CASE     					=      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    _SCREAMING_SNAKE_CASE     					=      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    _SCREAMING_SNAKE_CASE     					=      {
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }

  @require_torch
  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	pipeline(
       task="text-classification"  ,				model="hf-internal-testing/tiny-random-distilbert"  ,				framework="pt"							)

   __a							=	text_classifier("This is great !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}]							)

   __a							=	text_classifier("This is great !"  ,				top_k=2							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

   __a							=	text_classifier(["This is great !", "This is bad"]  ,				top_k=2							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[
           [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
           [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
       ]  ,				)

   __a							=	text_classifier("This is great !"  ,				top_k=1							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}]							)

   # Legacy behavior
   __a							=	text_classifier("This is great !"  ,				return_all_scores=__SCREAMING_SNAKE_CASE							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}]							)

   __a							=	text_classifier("This is great !"  ,				return_all_scores=__SCREAMING_SNAKE_CASE							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

   __a							=	text_classifier(["This is great !", "Something else"]  ,				return_all_scores=__SCREAMING_SNAKE_CASE							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[
           [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
           [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
       ]  ,				)

   __a							=	text_classifier(["This is great !", "Something else"]  ,				return_all_scores=__SCREAMING_SNAKE_CASE							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[
           {"label": "LABEL_0", "score": 0.5_04},
           {"label": "LABEL_0", "score": 0.5_04},
       ]  ,				)

  @require_torch
  def 		_UpperCAmelCase (		self			:   List[str]							):
   import torch

   __a							=	pipeline(
       task="text-classification"  ,				model="hf-internal-testing/tiny-random-distilbert"  ,				framework="pt"  ,				device=torch.device("cpu"							)  ,				)

   __a							=	text_classifier("This is great !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}]							)

  @require_tf
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	pipeline(
       task="text-classification"  ,				model="hf-internal-testing/tiny-random-distilbert"  ,				framework="tf"							)

   __a							=	text_classifier("This is great !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "LABEL_0", "score": 0.5_04}]							)

  @slow
  @require_torch
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	pipeline("text-classification"							)

   __a							=	text_classifier("This is great !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "POSITIVE", "score": 1.0}]							)
   __a							=	text_classifier("This is bad !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "NEGATIVE", "score": 1.0}]							)
   __a							=	text_classifier("Birds are a type of animal"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "POSITIVE", "score": 0.9_88}]							)

  @slow
  @require_tf
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	pipeline("text-classification"  ,				framework="tf"							)

   __a							=	text_classifier("This is great !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "POSITIVE", "score": 1.0}]							)
   __a							=	text_classifier("This is bad !"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "NEGATIVE", "score": 1.0}]							)
   __a							=	text_classifier("Birds are a type of animal"							)
   self.assertEqual(nested_simplify(__SCREAMING_SNAKE_CASE							)  ,				[{"label": "POSITIVE", "score": 0.9_88}]							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	TextClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,				tokenizer=__SCREAMING_SNAKE_CASE							)
   return text_classifier, ["HuggingFace is in", "This is another test"]

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	text_classifier.model
   # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
   __a							=	"HuggingFace is in"
   __a							=	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()							)

   __a							=	["HuggingFace is in ", "Paris is in France"]
   __a							=	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
   __a							=	text_classifier(__SCREAMING_SNAKE_CASE  ,				top_k=__SCREAMING_SNAKE_CASE							)
   __a							=	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]  ,				)

   __a							=	{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
   __a							=	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.
   __a							=	[["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
   __a							=	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()							)



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		[
      """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MCTCTForCTC""",
      """MCTCTModel""",
      """MCTCTPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
  from .feature_extraction_mctct import MCTCTFeatureExtractor
  from .processing_mctct import MCTCTProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


