



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)

__a          =		{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
__a          =		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      for attribute in key.split("""."""		):
            snake_case__  :    Dict    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

      if weight_type is not None:
            snake_case__  :    List[Any]    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		).shape
      else:
            snake_case__  :    Union[str, Any]    						=      hf_pointer.shape

      assert hf_shape == value.shape, (
          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":
            snake_case__  :    int    						=      value
      elif weight_type == "weight_g":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "weight_v":
            snake_case__  :    List[str]    						=      value
      elif weight_type == "bias":
            snake_case__  :    Optional[Any]    						=      value
      else:
            snake_case__  :    str    						=      value

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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Union[str, Any]    						=      []
      snake_case__  :    Dict    						=      fairseq_model.state_dict()

      snake_case__  :    List[Any]    						=      hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      snake_case__  :    Optional[int]    						=      None

      for name, value in fairseq_dict.items():
            snake_case__  :    List[Any]    						=      False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
                  snake_case__  :    Union[str, Any]    						=      True
            elif name.split("""."""		)[0] == "proj":
                  snake_case__  :    Tuple    						=      fairseq_model.proj
                  snake_case__  :    int    						=      True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                              snake_case__  :    Optional[Any]    						=      True
                              if "*" in mapped_key:
                                    snake_case__  :    Optional[int]    						=      name.split(_lowerCAmelCase		)[0].split("""."""		)[-2]
                                    snake_case__  :    Tuple    						=      mapped_key.replace("""*"""     ,   _lowerCAmelCase		)
                              if "weight_g" in name:
                                    snake_case__  :    str    						=      """weight_g"""
                              elif "weight_v" in name:
                                    snake_case__  :    int    						=      """weight_v"""
                              elif "bias" in name:
                                    snake_case__  :    Dict    						=      """bias"""
                              elif "weight" in name:
                                    snake_case__  :    Union[str, Any]    						=      """weight"""
                              else:
                                    snake_case__  :    Union[str, Any]    						=      None
                              set_recursively(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
                        continue
            if not is_used:
                  unused_weights.append(_lowerCAmelCase		)

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

      return proj_weight





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      snake_case__  :    int    						=      full_name.split("""conv_layers."""		)[-1]
      snake_case__  :    Dict    						=      name.split("""."""		)
      snake_case__  :    Any    						=      int(items[0]		)
      snake_case__  :    Optional[Any]    						=      int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  snake_case__  :    str    						=      value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  snake_case__  :    Union[str, Any]    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  snake_case__  :    int    						=      value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__						,				snake_case__  :    str    						=      emb.weight.shape
      snake_case__  :    List[str]    						=      nn.Linear(_lowerCAmelCase     ,   _lowerCAmelCase     ,   bias=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      emb.weight.data
      return lin_layer





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      with open(_lowerCAmelCase     ,   """r"""     ,   encoding="""utf-8"""		) as f:
            snake_case__  :    int    						=      f.readlines()
            snake_case__  :    List[Any]    						=      [line.split(""" """		)[0] for line in lines]

      snake_case__  :    Union[str, Any]    						=      len(_lowerCAmelCase		)

      snake_case__  :    Any    						=      {
          """<s>""": 0,
          """<pad>""": 1,
          """</s>""": 2,
          """<unk>""": 3,
      }

      vocab_dict.update(dict(zip(_lowerCAmelCase     ,   range(4     ,   num_words + 4		)		)		)		)
      return vocab_dict





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   )		-> int:
      snake_case__  :    Optional[Any]    						=      WavaVecaConfig.from_pretrained(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      SpeechaTextaConfig.from_pretrained(
          _lowerCAmelCase     ,   vocab_size=_lowerCAmelCase     ,   decoder_layers=_lowerCAmelCase     ,   do_stable_layer_norm=_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=16_000     ,   padding_value=0     ,   do_normalize=_lowerCAmelCase     ,   return_attention_mask=_lowerCAmelCase     ,   )

      snake_case__						,				snake_case__						,				snake_case__  :    Union[str, Any]    						=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
      snake_case__  :    Tuple    						=      model[0].eval()

      # set weights for wav2vec2 encoder
      snake_case__  :    Optional[Any]    						=      WavaVecaModel(_lowerCAmelCase		)
      snake_case__  :    Dict    						=      recursively_load_weights_wavaveca(model.encoder     ,   _lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      SpeechaTextaForCausalLM(_lowerCAmelCase		)
      snake_case__						,				snake_case__  :    Tuple    						=      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()     ,   strict=_lowerCAmelCase		)

      # set output linear layer
      unexpected_keys.remove("""embed_out"""		)
      snake_case__  :    Tuple    						=      nn.Parameter(model.decoder.embed_out.detach()		)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}"		)
      logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"		)

      snake_case__  :    List[Any]    						=      SpeechEncoderDecoderModel(encoder=_lowerCAmelCase     ,   decoder=_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      False

      # add projection layer
      snake_case__  :    Union[str, Any]    						=      nn.Parameter(projection_layer.weight		)
      snake_case__  :    int    						=      nn.Parameter(projection_layer.bias		)

      snake_case__  :    Tuple    						=      create_vocab_dict(_lowerCAmelCase		)

      with open(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)     ,   """w"""		) as fp:
            json.dump(_lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    Tuple    						=      SpeechaTextaTokenizer(os.path.join(_lowerCAmelCase     ,   """vocab.json"""		)		)
      tokenizer.save_pretrained(_lowerCAmelCase		)

      snake_case__  :    Optional[Any]    						=      hf_wavavec.config.to_dict()
      snake_case__  :    Tuple    						=      tokenizer.pad_token_id
      snake_case__  :    Optional[Any]    						=      tokenizer.bos_token_id
      snake_case__  :    int    						=      tokenizer.eos_token_id
      snake_case__  :    str    						=      """speech_to_text_2"""
      snake_case__  :    List[Any]    						=      """wav2vec2"""

      snake_case__  :    List[str]    						=      SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase		)

      hf_wavavec.save_pretrained(_lowerCAmelCase		)
      feature_extractor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument(
           "--encoder_config_path",
           default="facebook/wav2vec2-large-lv60",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/s2t-small-mustc-en-fr-st",
           type=str,
           help="Path to hf decoder s2t checkpoint config",
       )
       parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       __a          =		parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )








import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''hidden_sizes'''  )  )
								self.parent.assertTrue(hasattr(lowerCamelCase,  '''num_attention_heads'''  )  )



class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Tuple,  lowerCamelCase						:    str,  lowerCamelCase						:    str=13,  lowerCamelCase						:    Union[str, Any]=64,  lowerCamelCase						:    str=3,  lowerCamelCase						:    int=3,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    int=1,  lowerCamelCase						:    Optional[Any]=16,  lowerCamelCase						:    Dict=[128, 256, 384],  lowerCamelCase						:    Tuple=[4, 6, 8],  lowerCamelCase						:    Optional[Any]=[2, 3, 4],  lowerCamelCase						:    str=[16, 16, 16],  lowerCamelCase						:    Dict=0,  lowerCamelCase						:    List[str]=[2, 2, 2],  lowerCamelCase						:    str=[2, 2, 2],  lowerCamelCase						:    List[Any]=0.02,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    Tuple=True,  lowerCamelCase						:    Optional[Any]=2,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			image_size
								lowercase__    =			num_channels
								lowercase__    =			kernel_size
								lowercase__    =			stride
								lowercase__    =			padding
								lowercase__    =			hidden_sizes
								lowercase__    =			num_attention_heads
								lowercase__    =			depths
								lowercase__    =			key_dim
								lowercase__    =			drop_path_rate
								lowercase__    =			patch_size
								lowercase__    =			attention_ratio
								lowercase__    =			mlp_ratio
								lowercase__    =			initializer_range
								lowercase__    =			[
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								lowercase__    =			is_training
								lowercase__    =			use_labels
								lowercase__    =			num_labels
								lowercase__    =			initializer_range




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.num_labels  )

								lowercase__    =			self.get_config()
								return config, pixel_values, labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								return LevitConfig(
								    image_size=self.image_size,  num_channels=self.num_channels,  kernel_size=self.kernel_size,  stride=self.stride,  padding=self.padding,  patch_size=self.patch_size,  hidden_sizes=self.hidden_sizes,  num_attention_heads=self.num_attention_heads,  depths=self.depths,  key_dim=self.key_dim,  drop_path_rate=self.drop_path_rate,  mlp_ratio=self.mlp_ratio,  attention_ratio=self.attention_ratio,  initializer_range=self.initializer_range,  down_ops=self.down_ops,  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			LevitModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								lowercase__    =			(self.image_size, self.image_size)
								lowercase__ ,						lowercase__    =			image_size[0], image_size[1]
								for _ in range(4  ):
												lowercase__    =			floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
												lowercase__    =			floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape,  (self.batch_size, ceil(height / 4  ) * ceil(width / 4  ), self.hidden_sizes[-1]),  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    int,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			LevitForImageClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()
								lowercase__ ,						lowercase__ ,						lowercase__    =			config_and_inputs
								lowercase__    =			{'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				lowercase__											=       (
				    {
				        """feature-extraction""": LevitModel,
				        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			LevitModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37  )




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								return




				@unittest.skip(reason='''Levit does not use inputs_embeds'''  )
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Levit does not support input and output embeddings'''  )
				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Levit does not output attentions'''  )
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			model_class(lowerCamelCase  )
												lowercase__    =			inspect.signature(model.forward  )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowercase__    =			[*signature.parameters.keys()]

												lowercase__    =			['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Tuple  ):
												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.eval()

												with torch.no_grad():
																lowercase__    =			model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )

												lowercase__    =			outputs.hidden_states

												lowercase__    =			len(self.model_tester.depths  ) + 1
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

												lowercase__    =			(self.model_tester.image_size, self.model_tester.image_size)
												lowercase__ ,						lowercase__    =			image_size[0], image_size[1]
												for _ in range(4  ):
																lowercase__    =			floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1  )
																lowercase__    =			floor(
																    (
																        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1  )
												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]  ),  [
												        height * width,
												        self.model_tester.hidden_sizes[0],
												    ],  )

								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__    =			True
												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowercase__    =			True

												check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Any=False  ):

								'''simple docstring'''
								lowercase__    =			super()._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )

								if return_labels:
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																del inputs_dict["labels"]

								return inputs_dict




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if not self.model_tester.is_training:
												return

								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__    =			True

								for model_class in self.all_model_classes:
												# LevitForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(lowerCamelCase  )
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue
												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
												return

								lowercase__    =			False
								lowercase__    =			True

								for model_class in self.all_model_classes:
												if model_class in get_values(lowerCamelCase  ) or not model_class.supports_gradient_checkpointing:
																continue
												# LevitForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																continue
												lowercase__    =			model_class(lowerCamelCase  )
												model.gradient_checkpointing_enable()
												model.to(lowerCamelCase  )
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__    =			[
								    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
								    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
								    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
								]

								for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(lowerCamelCase  ),
												    ]
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue

												for problem_type in problem_types:
																with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""  ):
																				lowercase__    =			problem_type['''title''']
																				lowercase__    =			problem_type['''num_labels''']

																				lowercase__    =			model_class(lowerCamelCase  )
																				model.to(lowerCamelCase  )
																				model.train()

																				lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )

																				if problem_type["num_labels"] > 1:
																								lowercase__    =			inputs['''labels'''].unsqueeze(1  ).repeat(1,  problem_type['''num_labels''']  )

																				lowercase__    =			inputs['''labels'''].to(problem_type['''dtype''']  )

																				# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																				# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																				# they have the same size." which is a symptom something in wrong for the regression problem.
																				# See https://github.com/huggingface/transformers/issues/11780
																				with warnings.catch_warnings(record=lowerCamelCase  ) as warning_list:
																								lowercase__    =			model(**lowerCamelCase  ).loss
																				for w in warning_list:
																								if "Using a target size that is different to the input size" in str(w.message  ):
																												raise ValueError(
																												    F"""Something is going wrong in the regression problem: intercepted {w.message}"""  )

																				loss.backward()




				@slow
				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			LevitModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )






def   a			(						):






				'''simple docstring'''
				lowercase__    =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
				return image




@require_torch
@require_vision
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				@cached_property
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )




				@slow
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(
								    lowerCamelCase  )

								lowercase__    =			self.default_image_processor
								lowercase__    =			prepare_img()
								lowercase__    =			image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__    =			model(**lowerCamelCase  )

								# verify the logits
								lowercase__    =			torch.Size((1, 1_000)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )

								lowercase__    =			torch.tensor([1.0448, -0.3745, -1.8317]  ).to(lowerCamelCase  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )




