



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase	       =	logging.get_logger(__name__)
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					Any:
   UpperCamelCase   :  List[str]						     =     DPTConfig(embedding_type='hybrid'     )

   if "large" in checkpoint_url:
      UpperCamelCase   :  Tuple						     =     1024
      UpperCamelCase   :  List[str]						     =     4096
      UpperCamelCase   :  List[Any]						     =     24
      UpperCamelCase   :  Optional[Any]						     =     16
      UpperCamelCase   :  List[str]						     =     [5, 11, 17, 23]
      UpperCamelCase   :  Tuple						     =     [256, 512, 1024, 1024]
      UpperCamelCase   :  Dict						     =     (1, 384, 384)

   if "nyu" or "midas" in checkpoint_url:
      UpperCamelCase   :  List[Any]						     =     768
      UpperCamelCase   :  Optional[int]						     =     [1, 1, 1, 0.5]
      UpperCamelCase   :  Union[str, Any]						     =     [256, 512, 768, 768]
      UpperCamelCase   :  Any						     =     150
      UpperCamelCase   :  Optional[int]						     =     16
      UpperCamelCase   :  int						     =     (1, 384, 384)
      UpperCamelCase   :  Optional[Any]						     =     False
      UpperCamelCase   :  Optional[int]						     =     'project'

   if "ade" in checkpoint_url:
      UpperCamelCase   :  str						     =     True
      UpperCamelCase   :  Union[str, Any]						     =     768
      UpperCamelCase   :  List[str]						     =     [1, 1, 1, 0.5]
      UpperCamelCase   :  Tuple						     =     150
      UpperCamelCase   :  Union[str, Any]						     =     16
      UpperCamelCase   :  List[Any]						     =     'huggingface/label-files'
      UpperCamelCase   :  Tuple						     =     'ade20k-id2label.json'
      UpperCamelCase   :  int						     =     json.load(open(cached_download(hf_hub_url(snake_case__	,					snake_case__	,					repo_type='dataset'     )     )	,					'r'     )     )
      UpperCamelCase   :  str						     =     {int(snake_case__     ): v for k, v in idalabel.items()}
      UpperCamelCase   :  Optional[int]						     =     idalabel
      UpperCamelCase   :  List[Any]						     =     {v: k for k, v in idalabel.items()}
      UpperCamelCase   :  Tuple						     =     [1, 150, 480, 480]

   return config, expected_shape
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   UpperCamelCase   :  List[str]						     =     ['pretrained.model.head.weight', 'pretrained.model.head.bias']
   for k in ignore_keys:
      state_dict.pop(snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					Tuple:
   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      UpperCamelCase   :  int						     =     name.replace('pretrained.model'	,					'dpt.encoder'     )
   if "pretrained.model" in name:
      UpperCamelCase   :  int						     =     name.replace('pretrained.model'	,					'dpt.embeddings'     )
   if "patch_embed" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('patch_embed'	,					''     )
   if "pos_embed" in name:
      UpperCamelCase   :  List[Any]						     =     name.replace('pos_embed'	,					'position_embeddings'     )
   if "attn.proj" in name:
      UpperCamelCase   :  Tuple						     =     name.replace('attn.proj'	,					'attention.output.dense'     )
   if "proj" in name and "project" not in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('proj'	,					'projection'     )
   if "blocks" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('blocks'	,					'layer'     )
   if "mlp.fc1" in name:
      UpperCamelCase   :  Union[str, Any]						     =     name.replace('mlp.fc1'	,					'intermediate.dense'     )
   if "mlp.fc2" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('mlp.fc2'	,					'output.dense'     )
   if "norm1" in name and "backbone" not in name:
      UpperCamelCase   :  List[str]						     =     name.replace('norm1'	,					'layernorm_before'     )
   if "norm2" in name and "backbone" not in name:
      UpperCamelCase   :  List[str]						     =     name.replace('norm2'	,					'layernorm_after'     )
   if "scratch.output_conv" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('scratch.output_conv'	,					'head'     )
   if "scratch" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('scratch'	,					'neck'     )
   if "layer1_rn" in name:
      UpperCamelCase   :  str						     =     name.replace('layer1_rn'	,					'convs.0'     )
   if "layer2_rn" in name:
      UpperCamelCase   :  int						     =     name.replace('layer2_rn'	,					'convs.1'     )
   if "layer3_rn" in name:
      UpperCamelCase   :  str						     =     name.replace('layer3_rn'	,					'convs.2'     )
   if "layer4_rn" in name:
      UpperCamelCase   :  Dict						     =     name.replace('layer4_rn'	,					'convs.3'     )
   if "refinenet" in name:
      UpperCamelCase   :  Tuple						     =     int(name[len('neck.refinenet'     ) : len('neck.refinenet'     ) + 1]     )
      # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
      UpperCamelCase   :  int						     =     name.replace(F"""refinenet{layer_idx}"""	,					F"""fusion_stage.layers.{abs(layer_idx-4     )}"""     )
   if "out_conv" in name:
      UpperCamelCase   :  Any						     =     name.replace('out_conv'	,					'projection'     )
   if "resConfUnit1" in name:
      UpperCamelCase   :  Any						     =     name.replace('resConfUnit1'	,					'residual_layer1'     )
   if "resConfUnit2" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('resConfUnit2'	,					'residual_layer2'     )
   if "conv1" in name:
      UpperCamelCase   :  Dict						     =     name.replace('conv1'	,					'convolution1'     )
   if "conv2" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('conv2'	,					'convolution2'     )
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      UpperCamelCase   :  Dict						     =     name.replace('pretrained.act_postprocess1.0.project.0'	,					'neck.reassemble_stage.readout_projects.0.0'     )
   if "pretrained.act_postprocess2.0.project.0" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('pretrained.act_postprocess2.0.project.0'	,					'neck.reassemble_stage.readout_projects.1.0'     )
   if "pretrained.act_postprocess3.0.project.0" in name:
      UpperCamelCase   :  Dict						     =     name.replace('pretrained.act_postprocess3.0.project.0'	,					'neck.reassemble_stage.readout_projects.2.0'     )
   if "pretrained.act_postprocess4.0.project.0" in name:
      UpperCamelCase   :  Union[str, Any]						     =     name.replace('pretrained.act_postprocess4.0.project.0'	,					'neck.reassemble_stage.readout_projects.3.0'     )

   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      UpperCamelCase   :  Dict						     =     name.replace('pretrained.act_postprocess1.3'	,					'neck.reassemble_stage.layers.0.projection'     )
   if "pretrained.act_postprocess1.4" in name:
      UpperCamelCase   :  str						     =     name.replace('pretrained.act_postprocess1.4'	,					'neck.reassemble_stage.layers.0.resize'     )
   if "pretrained.act_postprocess2.3" in name:
      UpperCamelCase   :  List[Any]						     =     name.replace('pretrained.act_postprocess2.3'	,					'neck.reassemble_stage.layers.1.projection'     )
   if "pretrained.act_postprocess2.4" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('pretrained.act_postprocess2.4'	,					'neck.reassemble_stage.layers.1.resize'     )
   if "pretrained.act_postprocess3.3" in name:
      UpperCamelCase   :  str						     =     name.replace('pretrained.act_postprocess3.3'	,					'neck.reassemble_stage.layers.2.projection'     )
   if "pretrained.act_postprocess4.3" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('pretrained.act_postprocess4.3'	,					'neck.reassemble_stage.layers.3.projection'     )
   if "pretrained.act_postprocess4.4" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('pretrained.act_postprocess4.4'	,					'neck.reassemble_stage.layers.3.resize'     )
   if "pretrained" in name:
      UpperCamelCase   :  Dict						     =     name.replace('pretrained'	,					'dpt'     )
   if "bn" in name:
      UpperCamelCase   :  Dict						     =     name.replace('bn'	,					'batch_norm'     )
   if "head" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('head'	,					'head.head'     )
   if "encoder.norm" in name:
      UpperCamelCase   :  str						     =     name.replace('encoder.norm'	,					'layernorm'     )
   if "auxlayer" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('auxlayer'	,					'auxiliary_head.head'     )
   if "backbone" in name:
      UpperCamelCase   :  Optional[Any]						     =     name.replace('backbone'	,					'backbone.bit.encoder'     )

   if ".." in name:
      UpperCamelCase   :  List[str]						     =     name.replace('..'	,					'.'     )

   if "stem.conv" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('stem.conv'	,					'bit.embedder.convolution'     )
   if "blocks" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('blocks'	,					'layers'     )
   if "convolution" in name and "backbone" in name:
      UpperCamelCase   :  List[Any]						     =     name.replace('convolution'	,					'conv'     )
   if "layer" in name and "backbone" in name:
      UpperCamelCase   :  str						     =     name.replace('layer'	,					'layers'     )
   if "backbone.bit.encoder.bit" in name:
      UpperCamelCase   :  Dict						     =     name.replace('backbone.bit.encoder.bit'	,					'backbone.bit'     )
   if "embedder.conv" in name:
      UpperCamelCase   :  List[str]						     =     name.replace('embedder.conv'	,					'embedder.convolution'     )
   if "backbone.bit.encoder.stem.norm" in name:
      UpperCamelCase   :  Optional[int]						     =     name.replace('backbone.bit.encoder.stem.norm'	,					'backbone.bit.embedder.norm'     )
   return name
def 		UpperCamelCase     (       snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Union[str, Any]:
   for i in range(config.num_hidden_layers     ):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      UpperCamelCase   :  List[Any]						     =     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.weight"""     )
      UpperCamelCase   :  Optional[int]						     =     state_dict.pop(F"""dpt.encoder.layer.{i}.attn.qkv.bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase   :  List[Any]						     =     in_proj_weight[: config.hidden_size, :]
      UpperCamelCase   :  Optional[Any]						     =     in_proj_bias[: config.hidden_size]
      UpperCamelCase   :  List[str]						     =     in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      UpperCamelCase   :  List[str]						     =     in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      UpperCamelCase   :  List[Any]						     =     in_proj_weight[
          -config.hidden_size :, :
      ]
      UpperCamelCase   :  Optional[int]						     =     in_proj_bias[-config.hidden_size :]
def 		UpperCamelCase     (       )     ->					Tuple:
   UpperCamelCase   :  Tuple						     =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase   :  int						     =     Image.open(requests.get(snake_case__	,					stream=snake_case__     ).raw     )
   return im
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any	,					snake_case__	:   Optional[Any]	,					snake_case__	:   Dict	,					snake_case__	:   Any     )     ->					Dict:
   UpperCamelCase   :  Optional[Any]						     =     get_dpt_config(snake_case__     )
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   UpperCamelCase   :  str						     =     torch.load(snake_case__	,					map_location='cpu'     )
   # remove certain keys
   remove_ignore_keys_(snake_case__     )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCamelCase   :  Union[str, Any]						     =     state_dict.pop(snake_case__     )
      UpperCamelCase   :  Any						     =     val
   # read in qkv matrices
   read_in_q_k_v(snake_case__	,					snake_case__     )

   # load HuggingFace model
   UpperCamelCase   :  List[Any]						     =     DPTForSemanticSegmentation(snake_case__     ) if 'ade' in checkpoint_url else DPTForDepthEstimation(snake_case__     )
   model.load_state_dict(snake_case__     )
   model.eval()

   # Check outputs on an image
   UpperCamelCase   :  Tuple						     =     480 if 'ade' in checkpoint_url else 384
   UpperCamelCase   :  str						     =     DPTImageProcessor(size=snake_case__     )

   UpperCamelCase   :  str						     =     prepare_img()
   UpperCamelCase   :  Optional[Any]						     =     image_processor(snake_case__	,					return_tensors='pt'     )

   # forward pass
   UpperCamelCase   :  str						     =     model(**snake_case__     ).logits if 'ade' in checkpoint_url else model(**snake_case__     ).predicted_depth

   if show_prediction:
      UpperCamelCase   :  int						     =     (
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1     )	,					size=(image.size[1], image.size[0])	,					mode='bicubic'	,					align_corners=snake_case__	,					)
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 255     ).show()

   if pytorch_dump_folder_path is not None:
      Path(snake_case__     ).mkdir(exist_ok=snake_case__     )
      print(F"""Saving model to {pytorch_dump_folder_path}"""     )
      model.save_pretrained(snake_case__     )
      print(F"""Saving image processor to {pytorch_dump_folder_path}"""     )
      image_processor.save_pretrained(snake_case__     )

   if push_to_hub:
      model.push_to_hub('ybelkada/dpt-hybrid-midas'     )
      image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'     )


if __name__ == "__main__":
       __UpperCAmelCase	       =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
           type=str,
           help='''URL of the original DPT checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=False,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument(
           '''--push_to_hub''',
           action='''store_true''',
       )
       parser.add_argument(
           '''--model_name''',
           default='''dpt-large''',
           type=str,
           help='''Name of the model, in case you\'re pushing to the hub.''',
       )
       parser.add_argument(
           '''--show_prediction''',
           action='''store_true''',
       )

       __UpperCAmelCase	       =	parser.parse_args()
       convert_dpt_checkpoint(
           args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
       )








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     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       UpperCAmelCase__    : List[str]       					=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              UpperCAmelCase__    : 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:
              UpperCAmelCase__    : Dict       					=  {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          UpperCamelCase   :  Dict						     =     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}]	)

          UpperCamelCase   :  List[Any]						     =     text_classifier(['This is great !', 'This is bad'],      top_k=2	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
              ],      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !',      top_k=1	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          # Legacy behavior
          UpperCamelCase   :  str						     =     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}]	)

          UpperCamelCase   :  Tuple						     =     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}]]	)

          UpperCamelCase   :  List[Any]						     =     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}],
              ],      )

          UpperCamelCase   :  Optional[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': 0.5_04},
                  {'label': 'LABEL_0', 'score': 0.5_04},
              ],      )
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt',      device=torch.device('cpu'	),      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @require_tf
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[Any]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='tf'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     pipeline('text-classification'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  List[str]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     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  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  int						     =     pipeline('text-classification',      framework='tf'	)

          UpperCamelCase   :  Tuple						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  Union[str, Any]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     text_classifier('Birds are a type of animal'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 0.9_88}]	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  str						     =     TextClassificationPipeline(model=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_	)
          return text_classifier, ["HuggingFace is in", "This is another test"]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  List[str]						     =     text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          UpperCamelCase   :  List[str]						     =     'HuggingFace is in'
          UpperCamelCase   :  Tuple						     =     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()	)

          UpperCamelCase   :  List[Any]						     =     ['HuggingFace is in ', 'Paris is in France']
          UpperCamelCase   :  List[str]						     =     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
          UpperCamelCase   :  Optional[int]						     =     text_classifier(SCREAMING_SNAKE_CASE_,      top_k=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     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],      )

          UpperCamelCase   :  str						     =     {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
          UpperCamelCase   :  Union[str, Any]						     =     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.
          UpperCamelCase   :  List[str]						     =     [['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
          UpperCamelCase   :  List[str]						     =     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()	)



