


import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
UpperCAmelCase_      : Optional[int]  					=       logging.get_logger('transformers.models.encodec')

UpperCAmelCase_      : Any  					=       {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
UpperCAmelCase_      : str  					=       {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
UpperCAmelCase_      : Any  					=       {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
UpperCAmelCase_      : Optional[int]  					=       {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
UpperCAmelCase_      : List[str]  					=       {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
UpperCAmelCase_      : List[str]  					=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCAmelCase_      : Optional[Any]  					=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCAmelCase_      : Tuple  					=       []
UpperCAmelCase_      : List[Any]  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Tuple  ,    __A :			List[Any]  ,    __A :			Union[str, Any]  ,    __A :			List[Any]				)	->		Optional[Any]:

       """simple docstring"""

       for attribute in key.split('.'				):
              a_					:		Optional[int]							=  getattr(__A  ,    __A				)

       if weight_type is not None:
              a_					:		Any							=  getattr(__A  ,    __A				).shape
       else:
              a_					:		Dict							=  hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
                  F""" {value.shape} for {full_name}"""				)

       if weight_type == "weight":
              a_					:		Optional[int]							=  value
       elif weight_type == "weight_g":
              a_					:		Dict							=  value
       elif weight_type == "weight_v":
              a_					:		Optional[int]							=  value
       elif weight_type == "bias":
              a_					:		Tuple							=  value
       elif weight_type == "running_mean":
              a_					:		Optional[int]							=  value
       elif weight_type == "running_var":
              a_					:		int							=  value
       elif weight_type == "num_batches_tracked":
              a_					:		List[Any]							=  value
       elif weight_type == "weight_ih_l0":
              a_					:		Optional[Any]							=  value
       elif weight_type == "weight_hh_l0":
              a_					:		Optional[int]							=  value
       elif weight_type == "bias_ih_l0":
              a_					:		Any							=  value
       elif weight_type == "bias_hh_l0":
              a_					:		Any							=  value
       elif weight_type == "weight_ih_l1":
              a_					:		Optional[int]							=  value
       elif weight_type == "weight_hh_l1":
              a_					:		Any							=  value
       elif weight_type == "bias_ih_l1":
              a_					:		List[str]							=  value
       elif weight_type == "bias_hh_l1":
              a_					:		Optional[Any]							=  value
       else:
              a_					:		Optional[Any]							=  value

       logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""				)







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			Dict				)	->		Tuple:

       """simple docstring"""

       for key in ignore_keys:
              if key.endswith('.*'				):
                     if name.startswith(key[:-1]				):
                            return True
              elif ".*." in key:
                     a_							,		a_					:		Optional[int]							=  key.split('.*.'				)
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			List[Any]  ,    __A :			int  ,    __A :			Union[str, Any]				)	->		int:

       """simple docstring"""

       a_					:		str							=  []

       if model_name == "encodec_24khz" or "encodec_32khz":
              a_					:		List[str]							=  MAPPING_24K
       elif model_name == "encodec_48khz":
              a_					:		int							=  MAPPING_48K
       else:
              raise ValueError(F"""Unsupported model: {model_name}"""				)

       for name, value in orig_dict.items():
              if should_ignore(__A  ,    __A				):
                     logger.info(F"""{name} was ignored"""				)
                     continue

              a_					:		Optional[int]							=  False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            a_							,		a_					:		Any							=  key.split('.*.'				)
                            if prefix in name and suffix in name:
                                   a_					:		int							=  suffix

                     if key in name:
                            # HACK otherwise .embed gets initialized with .embed_avg too
                            if key.endswith('embed'				) and name.endswith('embed_avg'				):
                                   continue

                            a_					:		Optional[Any]							=  True
                            if "*" in mapped_key:
                                   a_					:		Union[str, Any]							=  name.split(__A				)[0].split('.'				)[-2]
                                   a_					:		Optional[int]							=  mapped_key.replace('*'  ,    __A				)
                            if "weight_g" in name:
                                   a_					:		int							=  'weight_g'
                            elif "weight_v" in name:
                                   a_					:		Tuple							=  'weight_v'
                            elif "weight_ih_l0" in name:
                                   a_					:		Optional[int]							=  'weight_ih_l0'
                            elif "weight_hh_l0" in name:
                                   a_					:		Any							=  'weight_hh_l0'
                            elif "bias_ih_l0" in name:
                                   a_					:		Tuple							=  'bias_ih_l0'
                            elif "bias_hh_l0" in name:
                                   a_					:		List[str]							=  'bias_hh_l0'
                            elif "weight_ih_l1" in name:
                                   a_					:		Optional[Any]							=  'weight_ih_l1'
                            elif "weight_hh_l1" in name:
                                   a_					:		Any							=  'weight_hh_l1'
                            elif "bias_ih_l1" in name:
                                   a_					:		Optional[Any]							=  'bias_ih_l1'
                            elif "bias_hh_l1" in name:
                                   a_					:		Dict							=  'bias_hh_l1'
                            elif "bias" in name:
                                   a_					:		int							=  'bias'
                            elif "weight" in name:
                                   a_					:		Union[str, Any]							=  'weight'
                            elif "running_mean" in name:
                                   a_					:		Union[str, Any]							=  'running_mean'
                            elif "running_var" in name:
                                   a_					:		int							=  'running_var'
                            elif "num_batches_tracked" in name:
                                   a_					:		str							=  'num_batches_tracked'
                            else:
                                   a_					:		List[Any]							=  None
                            set_recursively(__A  ,    __A  ,    __A  ,    __A  ,    __A				)
                     continue
              if not is_used:
                     unused_weights.append(__A				)

       logger.warning(F"""Unused weights: {unused_weights}"""				)







@torch.no_grad()
def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			Dict  ,    __A :			Tuple  ,    __A :			List[str]=None  ,    __A :			int=None  ,    )	->		Tuple:

       """simple docstring"""

       if config_path is not None:
              a_					:		List[str]							=  EncodecConfig.from_pretrained(__A				)
       else:
              a_					:		List[Any]							=  EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              a_					:		Optional[int]							=  [8, 5, 4, 4]
              a_					:		Any							=  [2.2]
              a_					:		List[str]							=  64
              a_					:		Optional[Any]							=  3_20_00
              a_					:		Union[str, Any]							=  20_48
              a_					:		str							=  False
              a_					:		Any							=  False
              a_					:		List[str]							=  False
       elif model_name == "encodec_48khz":
              a_					:		int							=  [8, 5, 4, 2]
              a_					:		str							=  [3.0, 6.0, 12.0, 24.0]
              a_					:		Any							=  4_80_00
              a_					:		str							=  2
              a_					:		Dict							=  False
              a_					:		List[Any]							=  'time_group_norm'
              a_					:		List[str]							=  True
              a_					:		str							=  1.0
              a_					:		List[Any]							=  0.01
       else:
              raise ValueError(F"""Unknown model name: {model_name}"""				)

       a_					:		Union[str, Any]							=  EncodecModel(__A				)

       a_					:		Optional[int]							=  EncodecFeatureExtractor(
           feature_size=config.audio_channels  ,    sampling_rate=config.sampling_rate  ,    chunk_length_s=config.chunk_length_s  ,    overlap=config.overlap  ,    )
       feature_extractor.save_pretrained(__A				)

       a_					:		Union[str, Any]							=  torch.load(__A				)
       if "best_state" in original_checkpoint:
              # we might have a training state saved, in which case discard the yaml results and just retain the weights
              a_					:		Dict							=  original_checkpoint['best_state']
       recursively_load_weights(__A  ,    __A  ,    __A				)
       model.save_pretrained(__A				)

       if repo_id:
              print('Pushing to the hub...'				)
              feature_extractor.push_to_hub(__A				)
              model.push_to_hub(__A				)


if __name__ == "__main__":
   UpperCAmelCase_      : List[Any]  					=       argparse.ArgumentParser()
   parser.add_argument(
       '--model',
       default='encodec_24khz',
       type=str,
       help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
   )
   parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
   parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
   parser.add_argument(
       '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
   )

   UpperCAmelCase_      : int  					=       parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )






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_      : Dict  					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      snake_case__				:     List[str]        =						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      snake_case__				:     Optional[Any]        =						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            snake_case__				:     str        =						{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        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     )

             a_					:		int							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]     )

             a_					:		List[str]							=  text_classifier(['This is great !', 'This is bad']     ,							top_k=2     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		Tuple							=  text_classifier('This is great !'     ,							top_k=1     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             # Legacy behavior
             a_					:		Union[str, Any]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )

             a_					:		List[str]							=  text_classifier('This is great !'     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]     )

             a_					:		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': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]     ,							)

             a_					:		str							=  text_classifier(['This is great !', 'Something else']     ,							return_all_scores=SCREAMING_SNAKE_CASE__     )
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]     ,							)
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             import torch

             a_					:		List[Any]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='pt'     ,							device=torch.device('cpu'     )     ,							)

             a_					:		Any							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Optional[Any]:
             a_					:		List[str]							=  pipeline(
                 task='text-classification'     ,							model='hf-internal-testing/tiny-random-distilbert'     ,							framework='tf'     )

             a_					:		Optional[int]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'LABEL_0', 'score': 0.504}]     )
      @slow
      @require_torch
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_					:		List[str]							=  pipeline('text-classification'     )

             a_					:		Dict							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		Union[str, Any]							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Tuple							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      @slow
      @require_tf
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			List[Any]:
             a_					:		Dict							=  pipeline('text-classification'     ,							framework='tf'     )

             a_					:		Optional[Any]							=  text_classifier('This is great !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 1.0}]     )
             a_					:		int							=  text_classifier('This is bad !'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'NEGATIVE', 'score': 1.0}]     )
             a_					:		Optional[int]							=  text_classifier('Birds are a type of animal'     )
             self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__     )     ,							[{'label': 'POSITIVE', 'score': 0.988}]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Any:
             a_					:		Optional[Any]							=  TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__     ,							tokenizer=SCREAMING_SNAKE_CASE__     )
             return text_classifier, ["HuggingFace is in", "This is another test"]

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Union[str, Any]:
             a_					:		List[str]							=  text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             a_					:		Union[str, Any]							=  'HuggingFace is in'
             a_					:		int							=  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_					:		Union[str, Any]							=  ['HuggingFace is in ', 'Paris is in France']
             a_					:		int							=  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_					:		List[Any]							=  text_classifier(SCREAMING_SNAKE_CASE__     ,							top_k=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  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_					:		int							=  {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
             a_					:		Optional[int]							=  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_					:		Any							=  [['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_					:		Tuple							=  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()     )


