"""simple docstring"""






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers.models.layoutlm.modeling_tf_layoutlm import (
           TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFLayoutLMForMaskedLM,
           TFLayoutLMForQuestionAnswering,
           TFLayoutLMForSequenceClassification,
           TFLayoutLMForTokenClassification,
           TFLayoutLMModel,
       )





class 				_A		:

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=2	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=3	, __lowerCAmelCase=4	, __lowerCAmelCase=None	, __lowerCAmelCase=1000	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_input_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_labels
            lowercase														=			num_choices
            lowercase														=			scope
            lowercase														=			range_bbox

      def        A__ (		self		):







            """simple docstring"""



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

            # convert bbox to numpy since TF does not support item assignment
            lowercase														=			ids_tensor([self.batch_size, self.seq_length, 4]	, self.range_bbox		).numpy()
            # Ensure that bbox is legal
            for i in range(bbox.shape[0]		):
                  for j in range(bbox.shape[1]		):
                        if bbox[i, j, 3] < bbox[i, j, 1]:
                              lowercase														=			bbox[i, j, 3]
                              lowercase														=			bbox[i, j, 1]
                              lowercase														=			t
                        if bbox[i, j, 2] < bbox[i, j, 0]:
                              lowercase														=			bbox[i, j, 2]
                              lowercase														=			bbox[i, j, 0]
                              lowercase														=			t
            lowercase														=			tf.convert_to_tensor(__lowerCAmelCase		)

            lowercase														=			None
            if self.use_input_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

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

            lowercase														=			None
            lowercase														=			None
            lowercase														=			None
            if self.use_labels:
                  lowercase														=			ids_tensor([self.batch_size]	, self.type_sequence_label_size		)
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.num_labels		)
                  lowercase														=			ids_tensor([self.batch_size]	, self.num_choices		)

            lowercase														=			LayoutLMConfig(
                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	, initializer_range=self.initializer_range	, )

            return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TFLayoutLMModel(config=__lowerCAmelCase		)

            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)
            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)
            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)		)
            self.parent.assertEqual(result.pooler_output.shape	, (self.batch_size, self.hidden_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TFLayoutLMForMaskedLM(config=__lowerCAmelCase		)

            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			TFLayoutLMForSequenceClassification(config=__lowerCAmelCase		)

            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.num_labels
            lowercase														=			TFLayoutLMForTokenClassification(config=__lowerCAmelCase		)

            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			TFLayoutLMForQuestionAnswering(config=__lowerCAmelCase		)

            lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)
            self.parent.assertEqual(result.start_logits.shape	, (self.batch_size, self.seq_length)		)
            self.parent.assertEqual(result.end_logits.shape	, (self.batch_size, self.seq_length)		)







      def        A__ (		self		):







            """simple docstring"""



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






@require_tf
class 				_A		( lowerCAmelCase						,					lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     List[str]													=			(
          (
              TFLayoutLMModel,
              TFLayoutLMForMaskedLM,
              TFLayoutLMForTokenClassification,
              TFLayoutLMForSequenceClassification,
              TFLayoutLMForQuestionAnswering,
          )
          if is_tf_available()
          else ()
      )
      snake_case__    :     Tuple													=			(
          {
              'feature-extraction': TFLayoutLMModel,
              'fill-mask': TFLayoutLMForMaskedLM,
              'text-classification': TFLayoutLMForSequenceClassification,
              'token-classification': TFLayoutLMForTokenClassification,
              'zero-shot': TFLayoutLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      snake_case__    :     Union[str, Any]													=			False
      snake_case__    :     Optional[int]													=			True
      snake_case__    :     int													=			10

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMModelTester(self		)
            lowercase														=			ConfigTester(self	, config_class=__lowerCAmelCase	, hidden_size=37		)

      def        A__ (		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase		)

      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase														=			TFLayoutLMModel.from_pretrained(__lowerCAmelCase		)
                  self.assertIsNotNone(__lowerCAmelCase		)







      @unittest.skip("""Onnx compliancy broke with TF 2.10"""		)
      def        A__ (		self		):







            """simple docstring"""



            pass






def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	)  # noqa: E231
      lowercase														=			tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
      lowercase														=			tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	)  # noqa: E231
      lowercase														=			tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
      # these are sequence labels (i.e. at the token level)
      lowercase														=			tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	)  # noqa: E231
      # fmt: on

      return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 				_A		( unittest.TestCase			):

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""		)

            lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=			model(input_ids=__lowerCAmelCase	, bbox=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)

            # test the sequence output on [0, :3, :3]
            lowercase														=			tf.convert_to_tensor(
                [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]	, )

            self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	, __lowerCAmelCase	, atol=1E-3		)		)

            # test the pooled output on [1, :3]
            lowercase														=			tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]		)

            self.assertTrue(np.allclose(outputs.pooler_output[1, :3]	, __lowerCAmelCase	, atol=1E-3		)		)

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	, num_labels=2		)

            lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=			model(
                input_ids=__lowerCAmelCase	, bbox=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=tf.convert_to_tensor([1, 1]		)	, )

            # test whether we get a loss as a scalar
            lowercase														=			outputs.loss
            lowercase														=			(2,)
            self.assertEqual(loss.shape	, __lowerCAmelCase		)

            # test the shape of the logits
            lowercase														=			outputs.logits
            lowercase														=			(2, 2)
            self.assertEqual(logits.shape	, __lowerCAmelCase		)

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""	, num_labels=13		)

            lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=			model(
                input_ids=__lowerCAmelCase	, bbox=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase	, labels=__lowerCAmelCase		)

            # test the shape of the logits
            lowercase														=			outputs.logits
            lowercase														=			tf.convert_to_tensor((2, 25, 13)		)
            self.assertEqual(logits.shape	, __lowerCAmelCase		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""		)

            lowercase							,							lowercase							,							lowercase							,							lowercase							,							lowercase														=			prepare_layoutlm_batch_inputs()

            # forward pass
            lowercase														=			model(input_ids=__lowerCAmelCase	, bbox=__lowerCAmelCase	, attention_mask=__lowerCAmelCase	, token_type_ids=__lowerCAmelCase		)

            # test the shape of the logits
            lowercase														=			tf.convert_to_tensor((2, 25)		)
            self.assertEqual(outputs.start_logits.shape	, __lowerCAmelCase		)
            self.assertEqual(outputs.end_logits.shape	, __lowerCAmelCase		)




"""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.
__lowerCAmelCase					: int        ={"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class 				_A		( unittest.TestCase			):
      snake_case__    :     Optional[int]													=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__    :     str													=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline(
                task="""text-classification"""	, model="""hf-internal-testing/tiny-random-distilbert"""	, framework="""pt"""		)

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

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

            lowercase														=			text_classifier(["""This is great !""", """This is bad"""]	, top_k=2		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [
                    [{"""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}],
                ]	, )

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

            # Legacy behavior
            lowercase														=			text_classifier("""This is great !"""	, return_all_scores=__lowerCAmelCase		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """LABEL_0""", """score""": 0.5_0_4}]		)

            lowercase														=			text_classifier("""This is great !"""	, return_all_scores=__lowerCAmelCase		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [[{"""label""": """LABEL_0""", """score""": 0.5_0_4}, {"""label""": """LABEL_1""", """score""": 0.4_9_6}]]		)

            lowercase														=			text_classifier(["""This is great !""", """Something else"""]	, return_all_scores=__lowerCAmelCase		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [
                    [{"""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}],
                ]	, )

            lowercase														=			text_classifier(["""This is great !""", """Something else"""]	, return_all_scores=__lowerCAmelCase		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [
                    {"""label""": """LABEL_0""", """score""": 0.5_0_4},
                    {"""label""": """LABEL_0""", """score""": 0.5_0_4},
                ]	, )

      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            import torch

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

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

      @require_tf
      def        A__ (		self		):







            """simple docstring"""



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

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

      @slow
      @require_torch
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""		)

            lowercase														=			text_classifier("""This is great !"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """POSITIVE""", """score""": 1.0}]		)
            lowercase														=			text_classifier("""This is bad !"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """NEGATIVE""", """score""": 1.0}]		)
            lowercase														=			text_classifier("""Birds are a type of animal"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """POSITIVE""", """score""": 0.9_8_8}]		)

      @slow
      @require_tf
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			pipeline("""text-classification"""	, framework="""tf"""		)

            lowercase														=			text_classifier("""This is great !"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """POSITIVE""", """score""": 1.0}]		)
            lowercase														=			text_classifier("""This is bad !"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """NEGATIVE""", """score""": 1.0}]		)
            lowercase														=			text_classifier("""Birds are a type of animal"""		)
            self.assertEqual(nested_simplify(__lowerCAmelCase		)	, [{"""label""": """POSITIVE""", """score""": 0.9_8_8}]		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



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







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			text_classifier.model
            # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
            lowercase														=			"""HuggingFace is in"""
            lowercase														=			text_classifier(__lowerCAmelCase		)

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

            lowercase														=			["""HuggingFace is in """, """Paris is in France"""]
            lowercase														=			text_classifier(__lowerCAmelCase		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [{"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}, {"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}]	, )
            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
            lowercase														=			text_classifier(__lowerCAmelCase	, top_k=__lowerCAmelCase		)
            lowercase														=			len(model.config.idalabel.values()		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, [[{"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}] * N, [{"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}] * N]	, )

            lowercase														=			{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
            lowercase														=			text_classifier(__lowerCAmelCase		)
            self.assertEqual(
                nested_simplify(__lowerCAmelCase		)	, {"""label""": ANY(__lowerCAmelCase		), """score""": ANY(__lowerCAmelCase		)}	, )
            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.
            lowercase														=			[["""HuggingFace is in """, """Paris is in France"""]]
            with self.assertRaises(__lowerCAmelCase		):
                  text_classifier(__lowerCAmelCase		)

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



