


'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roformer.modeling_flax_roformer import (
     FlaxRoFormerForMaskedLM,
     FlaxRoFormerForMultipleChoice,
     FlaxRoFormerForQuestionAnswering,
     FlaxRoFormerForSequenceClassification,
     FlaxRoFormerForTokenClassification,
     FlaxRoFormerModel,
 )


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1_3				,							SCREAMING_SNAKE_CASE__     :							Dict=7				,							SCREAMING_SNAKE_CASE__     :							List[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							str=True				,							SCREAMING_SNAKE_CASE__     :							Any=True				,							SCREAMING_SNAKE_CASE__     :							Any=9_9				,							SCREAMING_SNAKE_CASE__     :							List[Any]=3_2				,							SCREAMING_SNAKE_CASE__     :							Any=5				,							SCREAMING_SNAKE_CASE__     :							str=4				,							SCREAMING_SNAKE_CASE__     :							List[str]=3_7				,							SCREAMING_SNAKE_CASE__     :							Dict="gelu"				,							SCREAMING_SNAKE_CASE__     :							str=0.1				,							SCREAMING_SNAKE_CASE__     :							Dict=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=5_1_2				,							SCREAMING_SNAKE_CASE__     :							Tuple=1_6				,							SCREAMING_SNAKE_CASE__     :							Any=2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0.0_2				,							SCREAMING_SNAKE_CASE__     :							int=4				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   batch_size
       __a       =   seq_length
       __a       =   is_training
       __a       =   use_attention_mask
       __a       =   use_token_type_ids
       __a       =   use_labels
       __a       =   vocab_size
       __a       =   hidden_size
       __a       =   num_hidden_layers
       __a       =   num_attention_heads
       __a       =   intermediate_size
       __a       =   hidden_act
       __a       =   hidden_dropout_prob
       __a       =   attention_probs_dropout_prob
       __a       =   max_position_embeddings
       __a       =   type_vocab_size
       __a       =   type_sequence_label_size
       __a       =   initializer_range
       __a       =   num_choices

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

       __a       =   None
       if self.use_attention_mask:
            __a       =   random_attention_mask([self.batch_size, self.seq_length]						)

       __a       =   None
       if self.use_token_type_ids:
            __a       =   ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size						)

       __a       =   RoFormerConfig(
           vocab_size=self.vocab_size				,							hidden_size=self.hidden_size				,							num_hidden_layers=self.num_hidden_layers				,							num_attention_heads=self.num_attention_heads				,							intermediate_size=self.intermediate_size				,							hidden_act=self.hidden_act				,							hidden_dropout_prob=self.hidden_dropout_prob				,							attention_probs_dropout_prob=self.attention_probs_dropout_prob				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							is_decoder=SCREAMING_SNAKE_CASE__				,							initializer_range=self.initializer_range				,							)

       return config, input_ids, token_type_ids, attention_mask


  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.prepare_config_and_inputs()
       __a ,						__a ,						__a ,						__a       =   config_and_inputs
       __a       =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict


@require_flax
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[int]												=True

  a_       :int												=(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   FlaxRoFormerModelTester(self						)


  @slow
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for model_class_name in self.all_model_classes:
            __a       =   model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,							from_pt=SCREAMING_SNAKE_CASE__						)
            __a       =   model(np.ones((1, 1)						)						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)


@require_flax
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""						)
       __a       =   jnp.array([[0, 1, 2, 3, 4, 5]]						)
       __a       =   model(SCREAMING_SNAKE_CASE__						)[0]

       __a       =   5_0_0_0_0

       __a       =   (1, 6, vocab_size)
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)

       __a       =   jnp.array(
           [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]						)

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-4						)						)










'''simple docstring'''







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_      =			{'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :List[Any]												=MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a_       :List[Any]												=TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __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_0_4}]						)

       __a       =   text_classifier("""This is great !"""				,							top_k=2						)
       self.assertEqual(
           nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]						)

       __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_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __a       =   text_classifier("""This is great !"""				,							top_k=1						)
       self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__						)				,							[{"""label""": """LABEL_0""", """score""": 0.5_0_4}]						)

       # 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_0_4}]						)

       __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_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]]						)

       __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_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
               [{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}],
           ]				,							)

       __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_0_4},
               {"""label""": """LABEL_0""", """score""": 0.5_0_4},
           ]				,							)

  @require_torch
  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       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_0_4}]						)

  @require_tf
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __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_0_4}]						)

  @slow
  @require_torch
  def __a						(					self     :							int						):





       '''simple docstring'''

       __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_8_8}]						)

  @slow
  @require_tf
  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __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_8_8}]						)

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __a       =   TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__				,							tokenizer=SCREAMING_SNAKE_CASE__						)
       return text_classifier, ["HuggingFace is in", "This is another test"]


  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       __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()						)






