"""simple docstring"""






import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__lowerCAmelCase					: Any        ={
    """sample_size""": 3_2,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [3_2, 6_4],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: str        ={
    """sample_size""": 6_4,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_0_0_0,
    """block_out_channels""": [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: Tuple        ={
    """sample_size""": 2_5_6,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    """attention_head_dim""": 6_4,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__lowerCAmelCase					: Any        ={
    """num_train_timesteps""": 4_0,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__lowerCAmelCase					: str        ={
    """num_train_timesteps""": 2_0_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}

__lowerCAmelCase					: List[str]        ={
    """num_train_timesteps""": 1_5_1,
    """sigma_min""": 0.002,
    """sigma_max""": 80.0,
}


def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__      ,	lowerCAmelCase__	):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError("""boolean value expected"""	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Optional[int]=False	)				->					List[str]:
      '''simple docstring'''


      lowercase														=			checkpoint[f'{old_prefix}.in_layers.0.weight']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.0.bias']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.2.weight']
      lowercase														=			checkpoint[f'{old_prefix}.in_layers.2.bias']
      lowercase														=			checkpoint[f'{old_prefix}.emb_layers.1.weight']
      lowercase														=			checkpoint[f'{old_prefix}.emb_layers.1.bias']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.0.weight']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.0.bias']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.3.weight']
      lowercase														=			checkpoint[f'{old_prefix}.out_layers.3.bias']

      if has_skip:
            lowercase														=			checkpoint[f'{old_prefix}.skip_connection.weight']
            lowercase														=			checkpoint[f'{old_prefix}.skip_connection.bias']

      return new_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :Optional[Any]=None	)				->					int:
      '''simple docstring'''


      lowercase							,							lowercase							,							lowercase														=			checkpoint[f'{old_prefix}.qkv.weight'].chunk(3      ,	dim=0	)
      lowercase							,							lowercase							,							lowercase														=			checkpoint[f'{old_prefix}.qkv.bias'].chunk(3      ,	dim=0	)

      lowercase														=			checkpoint[f'{old_prefix}.norm.weight']
      lowercase														=			checkpoint[f'{old_prefix}.norm.bias']

      lowercase														=			weight_q.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_q.squeeze(-1	).squeeze(-1	)
      lowercase														=			weight_k.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_k.squeeze(-1	).squeeze(-1	)
      lowercase														=			weight_v.squeeze(-1	).squeeze(-1	)
      lowercase														=			bias_v.squeeze(-1	).squeeze(-1	)

      lowercase														=			(
          checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1	).squeeze(-1	)
      )
      lowercase														=			checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1	).squeeze(-1	)

      return new_checkpoint


def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Dict	)				->					Dict:
      '''simple docstring'''


      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location="""cpu"""	)
      lowercase														=			{}

      lowercase														=			checkpoint["""time_embed.0.weight"""]
      lowercase														=			checkpoint["""time_embed.0.bias"""]
      lowercase														=			checkpoint["""time_embed.2.weight"""]
      lowercase														=			checkpoint["""time_embed.2.bias"""]

      if unet_config["num_class_embeds"] is not None:
            lowercase														=			checkpoint["""label_emb.weight"""]

      lowercase														=			checkpoint["""input_blocks.0.0.weight"""]
      lowercase														=			checkpoint["""input_blocks.0.0.bias"""]

      lowercase														=			unet_config["""down_block_types"""]
      lowercase														=			unet_config["""layers_per_block"""]
      lowercase														=			unet_config["""attention_head_dim"""]
      lowercase														=			unet_config["""block_out_channels"""]
      lowercase														=			1
      lowercase														=			channels_list[0]

      for i, layer_type in enumerate(lowerCAmelCase__	):
            lowercase														=			channels_list[i]
            lowercase														=			current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(lowerCAmelCase__	):
                        lowercase														=			f'down_blocks.{i}.resnets.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.0'
                        lowercase														=			True if j == 0 and downsample_block_has_skip else False
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(lowerCAmelCase__	):
                        lowercase														=			f'down_blocks.{i}.resnets.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.0'
                        lowercase														=			True if j == 0 and downsample_block_has_skip else False
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        lowercase														=			f'down_blocks.{i}.attentions.{j}'
                        lowercase														=			f'input_blocks.{current_layer}.1'
                        lowercase														=			convert_attention(
                            lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                        current_layer += 1

            if i != len(lowerCAmelCase__	) - 1:
                  lowercase														=			f'down_blocks.{i}.downsamplers.0'
                  lowercase														=			f'input_blocks.{current_layer}.0'
                  lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                  current_layer += 1

            lowercase														=			current_channels

      # hardcoded the mid-block for now
      lowercase														=			"""mid_block.resnets.0"""
      lowercase														=			"""middle_block.0"""
      lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
      lowercase														=			"""mid_block.attentions.0"""
      lowercase														=			"""middle_block.1"""
      lowercase														=			convert_attention(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
      lowercase														=			"""mid_block.resnets.1"""
      lowercase														=			"""middle_block.2"""
      lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      lowercase														=			0
      lowercase														=			unet_config["""up_block_types"""]

      for i, layer_type in enumerate(lowerCAmelCase__	):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1	):
                        lowercase														=			f'up_blocks.{i}.resnets.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.0'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        current_layer += 1

                  if i != len(lowerCAmelCase__	) - 1:
                        lowercase														=			f'up_blocks.{i}.upsamplers.0'
                        lowercase														=			f'output_blocks.{current_layer-1}.1'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1	):
                        lowercase														=			f'up_blocks.{i}.resnets.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.0'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	has_skip=lowerCAmelCase__	)
                        lowercase														=			f'up_blocks.{i}.attentions.{j}'
                        lowercase														=			f'output_blocks.{current_layer}.1'
                        lowercase														=			convert_attention(
                            lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)
                        current_layer += 1

                  if i != len(lowerCAmelCase__	) - 1:
                        lowercase														=			f'up_blocks.{i}.upsamplers.0'
                        lowercase														=			f'output_blocks.{current_layer-1}.2'
                        lowercase														=			convert_resnet(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)

      lowercase														=			checkpoint["""out.0.weight"""]
      lowercase														=			checkpoint["""out.0.bias"""]
      lowercase														=			checkpoint["""out.2.weight"""]
      lowercase														=			checkpoint["""out.2.bias"""]

      return new_checkpoint


if __name__ == "__main__":
       __lowerCAmelCase					: int        =argparse.ArgumentParser()

       parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
       parser.add_argument(
           """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
       )
       parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

       __lowerCAmelCase					: List[Any]        =parser.parse_args()
       __lowerCAmelCase					: List[Any]        =strabool(args.class_cond)

       __lowerCAmelCase					: Union[str, Any]        =os.path.basename(args.unet_path)
       print(F"""Checkpoint: {ckpt_name}""")

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              __lowerCAmelCase					: Optional[int]        =IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __lowerCAmelCase					: Any        =LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              __lowerCAmelCase					: List[str]        =TEST_UNET_CONFIG
       else:
              raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

       if not args.class_cond:
              __lowerCAmelCase					: Optional[int]        =None

       __lowerCAmelCase					: Dict        =con_pt_to_diffuser(args.unet_path, unet_config)

       __lowerCAmelCase					: Union[str, Any]        =UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __lowerCAmelCase					: List[Any]        =CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __lowerCAmelCase					: List[str]        =CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __lowerCAmelCase					: List[Any]        =CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

       __lowerCAmelCase					: Dict        =CMStochasticIterativeScheduler(**scheduler_config)

       __lowerCAmelCase					: Optional[int]        =ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)




"""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()		)



