


'''simple docstring'''






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()
__A :  Any    							=				logging.get_logger("transformers.models.encodec")

__A :  Tuple    							=				{
    "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",
}
__A :  Optional[int]    							=				{
    "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",
}
__A :  Dict    							=				{
    "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",
}
__A :  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",
}
__A :  List[Any]    							=				{
    "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",
}
__A :  List[Any]    							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__A :  str    							=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__A :  List[str]    							=				[]
__A :  Any    							=				[]







def     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       int							,   A__ :       Dict							,   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  for attribute in key.split(""".""" ):
    lowerCAmelCase_      :				Tuple      =       getattr(A__							,   A__ )

  if weight_type is not None:
    lowerCAmelCase_      :				Any      =       getattr(A__							,   A__ ).shape
  else:
    lowerCAmelCase_      :				List[Any]      =       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":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "weight_g":
    lowerCAmelCase_      :				List[str]      =       value
  elif weight_type == "weight_v":
    lowerCAmelCase_      :				List[str]      =       value
  elif weight_type == "bias":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "running_mean":
    lowerCAmelCase_      :				int      =       value
  elif weight_type == "running_var":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "num_batches_tracked":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "weight_ih_l0":
    lowerCAmelCase_      :				int      =       value
  elif weight_type == "weight_hh_l0":
    lowerCAmelCase_      :				Union[str, Any]      =       value
  elif weight_type == "bias_ih_l0":
    lowerCAmelCase_      :				int      =       value
  elif weight_type == "bias_hh_l0":
    lowerCAmelCase_      :				List[str]      =       value
  elif weight_type == "weight_ih_l1":
    lowerCAmelCase_      :				Optional[Any]      =       value
  elif weight_type == "weight_hh_l1":
    lowerCAmelCase_      :				Union[str, Any]      =       value
  elif weight_type == "bias_ih_l1":
    lowerCAmelCase_      :				Optional[int]      =       value
  elif weight_type == "bias_hh_l1":
    lowerCAmelCase_      :				int      =       value
  else:
    lowerCAmelCase_      :				str      =       value

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







def     UpperCamelCase_      (   A__ :       Optional[Any]							,   A__ :       Dict ):
  '''simple docstring'''





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







def     UpperCamelCase_      (   A__ :       Any							,   A__ :       Union[str, Any]							,   A__ :       int ):
  '''simple docstring'''





  lowerCAmelCase_      :				str      =       []

  if model_name == "encodec_24khz" or "encodec_32khz":
    lowerCAmelCase_      :				List[str]      =       MAPPING_24K
  elif model_name == "encodec_48khz":
    lowerCAmelCase_      :				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

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

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

        lowerCAmelCase_      :				Any      =       True
        if "*" in mapped_key:
          lowerCAmelCase_      :				Optional[int]      =       name.split(A__ )[0].split(""".""" )[-2]
          lowerCAmelCase_      :				int      =       mapped_key.replace("""*"""							,   A__ )
        if "weight_g" in name:
          lowerCAmelCase_      :				List[str]      =       """weight_g"""
        elif "weight_v" in name:
          lowerCAmelCase_      :				str      =       """weight_v"""
        elif "weight_ih_l0" in name:
          lowerCAmelCase_      :				Union[str, Any]      =       """weight_ih_l0"""
        elif "weight_hh_l0" in name:
          lowerCAmelCase_      :				Dict      =       """weight_hh_l0"""
        elif "bias_ih_l0" in name:
          lowerCAmelCase_      :				Optional[Any]      =       """bias_ih_l0"""
        elif "bias_hh_l0" in name:
          lowerCAmelCase_      :				Any      =       """bias_hh_l0"""
        elif "weight_ih_l1" in name:
          lowerCAmelCase_      :				Optional[int]      =       """weight_ih_l1"""
        elif "weight_hh_l1" in name:
          lowerCAmelCase_      :				Optional[Any]      =       """weight_hh_l1"""
        elif "bias_ih_l1" in name:
          lowerCAmelCase_      :				Optional[Any]      =       """bias_ih_l1"""
        elif "bias_hh_l1" in name:
          lowerCAmelCase_      :				Tuple      =       """bias_hh_l1"""
        elif "bias" in name:
          lowerCAmelCase_      :				List[str]      =       """bias"""
        elif "weight" in name:
          lowerCAmelCase_      :				List[str]      =       """weight"""
        elif "running_mean" in name:
          lowerCAmelCase_      :				Optional[Any]      =       """running_mean"""
        elif "running_var" in name:
          lowerCAmelCase_      :				Dict      =       """running_var"""
        elif "num_batches_tracked" in name:
          lowerCAmelCase_      :				Any      =       """num_batches_tracked"""
        else:
          lowerCAmelCase_      :				Optional[int]      =       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     UpperCamelCase_      (   A__ :       Tuple							,   A__ :       List[Any]							,   A__ :       Any							,   A__ :       Union[str, Any]=None							,   A__ :       str=None							,   ):
  '''simple docstring'''





  if config_path is not None:
    lowerCAmelCase_      :				int      =       EncodecConfig.from_pretrained(A__ )
  else:
    lowerCAmelCase_      :				List[str]      =       EncodecConfig()

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

  lowerCAmelCase_      :				List[Any]      =       EncodecModel(A__ )

  lowerCAmelCase_      :				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__ )

  lowerCAmelCase_      :				Optional[int]      =       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
    lowerCAmelCase_      :				Optional[Any]      =       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__":
  __A :  str    							=				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."
  )

  __A :  str    							=				parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )







'''simple docstring'''






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

  from transformers import ViTMAEForPreTraining, ViTMAEModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple=13   ,						lowerCamelCase   :  Dict=30   ,						lowerCamelCase   :  Dict=2   ,						lowerCamelCase   :  Optional[int]=3   ,						lowerCamelCase   :  List[Any]=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  str=32   ,						lowerCamelCase   :  Any=5   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  List[str]=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  str=10   ,						lowerCamelCase   :  Optional[Any]=0.02   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Union[str, Any]=0.6   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[Any]      =       parent
      lowerCAmelCase_      :				Optional[int]      =       batch_size
      lowerCAmelCase_      :				int      =       image_size
      lowerCAmelCase_      :				List[Any]      =       patch_size
      lowerCAmelCase_      :				int      =       num_channels
      lowerCAmelCase_      :				Any      =       is_training
      lowerCAmelCase_      :				Tuple      =       use_labels
      lowerCAmelCase_      :				Optional[Any]      =       hidden_size
      lowerCAmelCase_      :				List[Any]      =       num_hidden_layers
      lowerCAmelCase_      :				Optional[Any]      =       num_attention_heads
      lowerCAmelCase_      :				Dict      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Any      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				List[Any]      =       type_sequence_label_size
      lowerCAmelCase_      :				Dict      =       initializer_range
      lowerCAmelCase_      :				List[str]      =       mask_ratio
      lowerCAmelCase_      :				Tuple      =       scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      lowerCAmelCase_      :				Union[str, Any]      =       (image_size // patch_size) ** 2
      lowerCAmelCase_      :				Any      =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				str      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_      :				Optional[int]      =       None
      if self.use_labels:
        lowerCAmelCase_      :				int      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)

      lowerCAmelCase_      :				str      =       self.get_config()

      return config, pixel_values, labels

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      return ViTMAEConfig(
          image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						is_decoder=lowerCamelCase   ,						initializer_range=self.initializer_range   ,						mask_ratio=self.mask_ratio   ,						)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  Tuple:
      lowerCAmelCase_      :				Tuple      =       ViTMAEModel(config=lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       (self.image_size // self.patch_size) ** 2
      lowerCAmelCase_      :				int      =       self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)

      # test greyscale images
      lowerCAmelCase_      :				List[Any]      =       1
      lowerCAmelCase_      :				List[str]      =       ViTMAEForPreTraining(lowerCamelCase		)
      model.to(lowerCamelCase		)
      model.eval()
      lowerCAmelCase_      :				Optional[Any]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
      lowerCAmelCase_      :				Tuple      =       model(lowerCamelCase		)
      lowerCAmelCase_      :				List[Any]      =       self.patch_size**2
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_patches, expected_num_channels)		)





    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.prepare_config_and_inputs()
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       config_and_inputs
      lowerCAmelCase_      :				Any      =       {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
    lowercase       			= {'feature-extraction': ViTMAEModel} if is_torch_available() else {}

    lowercase       			= False
    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEModelTester(self		)
      lowerCAmelCase_      :				Optional[int]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						has_text_modality=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  Dict		)       ->  Tuple:
      self.config_tester.run_common_tests()

    @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      pass

    def __lowercase       (       self   :  List[str]		)       ->  Tuple:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				List[str]      =       model_class(lowerCamelCase		)
        self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module)		)
        lowerCAmelCase_      :				Tuple      =       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase   ,						nn.Linear		)		)

    def __lowercase       (       self   :  Optional[Any]		)       ->  Any:
      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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

        lowerCAmelCase_      :				Optional[Any]      =       ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  str:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  str:
      lowerCAmelCase_      :				Tuple      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[Any]		)       ->  str:
      # make masks reproducible
      np.random.seed(2		)

      lowerCAmelCase_      :				Tuple      =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)		)
      lowerCAmelCase_      :				Optional[Any]      =       torch.from_numpy(lowerCamelCase		)

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      lowerCAmelCase_      :				int      =       pt_noise

      super().check_pt_tf_models(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  Dict:
      lowerCAmelCase_,							lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCAmelCase_      :				Optional[int]      =       model_class(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        # make random mask reproducible
        torch.manual_seed(2		)
        with torch.no_grad():
          lowerCAmelCase_      :				Any      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

        lowerCAmelCase_      :				Any      =       outputs[0].cpu().numpy()
        lowerCAmelCase_      :				List[str]      =       0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				int      =       model_class.from_pretrained(lowerCamelCase		)
          model.to(lowerCamelCase		)
          # make random mask reproducible
          torch.manual_seed(2		)
          with torch.no_grad():
            lowerCAmelCase_      :				str      =       model(**self._prepare_for_class(lowerCamelCase   ,						lowerCamelCase		)		)

          # Make sure we don't have nans
          lowerCAmelCase_      :				Optional[Any]      =       after_outputs[0].cpu().numpy()
          lowerCAmelCase_      :				str      =       0
          lowerCAmelCase_      :				List[Any]      =       np.amax(np.abs(out_a - out_a		)		)
          self.assertLessEqual(lowerCamelCase   ,						1E-5		)

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[int]		)       ->  List[Any]:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Union[str, Any]		)       ->  str:
      pass

    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
    def __lowercase       (       self   :  Optional[Any]		)       ->  Union[str, Any]:
      pass

    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      pass

    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
    def __lowercase       (       self   :  List[Any]		)       ->  str:
      pass





    @slow
    def __lowercase       (       self   :  List[str]		)       ->  List[Any]:
      for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				List[Any]      =       ViTMAEModel.from_pretrained(lowerCamelCase		)
        self.assertIsNotNone(lowerCamelCase		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[str]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






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







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Union[str, Any]:
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None





    @slow
    def __lowercase       (       self   :  int		)       ->  List[Any]:
      # make random mask reproducible across the PT and TF model
      np.random.seed(2		)

      lowerCAmelCase_      :				Dict      =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(lowerCamelCase		)

      lowerCAmelCase_      :				Union[str, Any]      =       self.default_image_processor
      lowerCAmelCase_      :				Union[str, Any]      =       prepare_img()
      lowerCAmelCase_      :				Dict      =       image_processor(images=lowerCamelCase   ,						return_tensors="""pt"""		).to(lowerCamelCase		)

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      lowerCAmelCase_      :				Optional[int]      =       ViTMAEConfig()
      lowerCAmelCase_      :				Optional[Any]      =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2		)
      lowerCAmelCase_      :				Optional[int]      =       np.random.uniform(size=(1, num_patches)		)

      # forward pass
      with torch.no_grad():
        lowerCAmelCase_      :				str      =       model(**lowerCamelCase   ,						noise=torch.from_numpy(lowerCamelCase		).to(device=lowerCamelCase		)		)

      # verify the logits
      lowerCAmelCase_      :				str      =       torch.Size((1, 1_96, 7_68)		)
      self.assertEqual(outputs.logits.shape   ,						lowerCamelCase		)

      lowerCAmelCase_      :				str      =       torch.tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]		)

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



