


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_  :				Optional[int]						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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_  :				Dict						= [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						List[Any]		):





 for attribute in key.split("."		):
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		)

 if weight_type is not None:
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		).shape
 else:
  lowerCamelCase_      =  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_      =  value
 elif weight_type == "weight_g":
  lowerCamelCase_      =  value
 elif weight_type == "weight_v":
  lowerCamelCase_      =  value
 elif weight_type == "bias":
  lowerCamelCase_      =  value
 elif weight_type == "running_mean":
  lowerCamelCase_      =  value
 elif weight_type == "running_var":
  lowerCamelCase_      =  value
 elif weight_type == "num_batches_tracked":
  lowerCamelCase_      =  value
 elif weight_type == "inv_freq":
  lowerCamelCase_      =  value
 else:
  lowerCamelCase_      =  value

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





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  fairseq_model.state_dict()

 lowerCamelCase_      =  hf_model.wavaveca_conformer.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase_      =  False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				hf_model.config.feat_extract_norm == "group"					,				)
   lowerCamelCase_      =  True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase_      =  """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
     lowerCamelCase_      =  True
     if "*" in mapped_key:
      lowerCamelCase_      =  name.split(UpperCAmelCase_		)[0].split("."		)[-2]
      lowerCamelCase_      =  mapped_key.replace("*"					,				UpperCAmelCase_		)
     if "pos_bias_u" in name:
      lowerCamelCase_      =  None
     elif "pos_bias_v" in name:
      lowerCamelCase_      =  None
     elif "weight_g" in name:
      lowerCamelCase_      =  """weight_g"""
     elif "weight_v" in name:
      lowerCamelCase_      =  """weight_v"""
     elif "bias" in name:
      lowerCamelCase_      =  """bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      lowerCamelCase_      =  """weight"""
     elif "running_mean" in name:
      lowerCamelCase_      =  """running_mean"""
     elif "inv_freq" in name:
      lowerCamelCase_      =  """inv_freq"""
     elif "running_var" in name:
      lowerCamelCase_      =  """running_var"""
     elif "num_batches_tracked" in name:
      lowerCamelCase_      =  """num_batches_tracked"""
     else:
      lowerCamelCase_      =  None
     set_recursively(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
    continue
  if not is_used:
   unused_weights.append(UpperCAmelCase_		)

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





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  full_name.split("conv_layers."		)[-1]
 lowerCamelCase_      =  name.split("."		)
 lowerCamelCase_      =  int(items[0]		)
 lowerCamelCase_      =  int(items[1]		)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''		)
   lowerCamelCase_      =  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:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
 else:
  unused_weights.append(UpperCAmelCase_		)





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						int=None					,				UpperCAmelCase_						:						str=None					,				UpperCAmelCase_						:						Tuple=True		):





 if config_path is not None:
  lowerCamelCase_      =  WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_					,				hidden_act="swish"		)
 else:
  lowerCamelCase_      =  WavaVecaConformerConfig()

 if "rope" in checkpoint_path:
  lowerCamelCase_      =  """rotary"""

 if is_finetuned:
  if dict_path:
   lowerCamelCase_      =  Dictionary.load(UpperCAmelCase_		)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase_      =  target_dict.pad_index
   lowerCamelCase_      =  target_dict.bos_index
   lowerCamelCase_      =  target_dict.eos_index
   lowerCamelCase_      =  len(target_dict.symbols		)
   lowerCamelCase_      =  os.path.join(UpperCAmelCase_					,				"vocab.json"		)
   if not os.path.isdir(UpperCAmelCase_		):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_		)		)
    return
   os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_		)
   lowerCamelCase_      =  target_dict.indices

   # fairseq has the <pad> and <s> switched
   lowerCamelCase_      =  0
   lowerCamelCase_      =  1
   with open(UpperCAmelCase_					,				"w"					,				encoding="utf-8"		) as vocab_handle:
    json.dump(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  WavaVecaCTCTokenizer(
       UpperCAmelCase_					,				unk_token=target_dict.unk_word					,				pad_token=target_dict.pad_word					,				bos_token=target_dict.bos_word					,				eos_token=target_dict.eos_word					,				word_delimiter_token="|"					,				do_lower_case=UpperCAmelCase_					,				)
   lowerCamelCase_      =  True if config.feat_extract_norm == """layer""" else False
   lowerCamelCase_      =  WavaVecaFeatureExtractor(
       feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=UpperCAmelCase_					,				return_attention_mask=UpperCAmelCase_					,				)
   lowerCamelCase_      =  WavaVecaProcessor(feature_extractor=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_		)
   processor.save_pretrained(UpperCAmelCase_		)

  lowerCamelCase_      =  WavaVecaConformerForCTC(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  WavaVecaConformerForPreTraining(UpperCAmelCase_		)

 if is_finetuned:
  lowerCamelCase_      =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]					,				arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
 else:
  lowerCamelCase_      =  argparse.Namespace(task="audio_pretraining"		)
  lowerCamelCase_      =  fairseq.tasks.setup_task(UpperCAmelCase_		)

  lowerCamelCase_      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,				task=UpperCAmelCase_		)

 lowerCamelCase_      =  model[0].eval()

 recursively_load_weights(UpperCAmelCase_					,				UpperCAmelCase_					,				not is_finetuned		)

 hf_wavavec.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Any						= 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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     a_  :				Dict						= parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )








'''simple docstring'''





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

 if is_vision_available():
  from transformers import MaskFormerImageProcessor

if is_vision_available():
 from PIL import Image






class        lowerCAmelCase_:
  '''simple docstring'''




  def __init__(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=2  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=10  ,__UpperCAmelCase=3  ,__UpperCAmelCase=32 * 4  ,__UpperCAmelCase=32 * 6  ,__UpperCAmelCase=4  ,__UpperCAmelCase=32  ,)    ->		str:
      lowerCAmelCase__ :  Optional[int]							      =					parent
      lowerCAmelCase__ :  Optional[int]							      =					batch_size
      lowerCAmelCase__ :  Optional[int]							      =					is_training
      lowerCAmelCase__ :  Dict							      =					use_auxiliary_loss
      lowerCAmelCase__ :  Union[str, Any]							      =					num_queries
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  List[str]							      =					min_size
      lowerCAmelCase__ :  int							      =					max_size
      lowerCAmelCase__ :  Optional[Any]							      =					num_labels
      lowerCAmelCase__ :  List[Any]							      =					mask_feature_size




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  str							      =					floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
          __UpperCAmelCase      )

      lowerCAmelCase__ :  str							      =					torch.ones([self.batch_size, self.min_size, self.max_size]  ,device=__UpperCAmelCase      )

      lowerCAmelCase__ :  Any							      =					(
          torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]  ,device=__UpperCAmelCase      ) > 0.5
      ).float()
      lowerCAmelCase__ :  Optional[int]							      =					(torch.rand((self.batch_size, self.num_labels)  ,device=__UpperCAmelCase      ) > 0.5).long()

      lowerCAmelCase__ :  Any							      =					self.get_config()
      return config, pixel_values, pixel_mask, mask_labels, class_labels




  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      return MaskFormerConfig.from_backbone_and_decoder_configs(
          backbone_config=SwinConfig(
              depths=[1, 1, 1, 1]  ,)  ,decoder_config=DetrConfig(
              decoder_ffn_dim=128  ,num_queries=self.num_queries  ,decoder_attention_heads=2  ,d_model=self.mask_feature_size  ,)  ,mask_feature_size=self.mask_feature_size  ,fpn_feature_size=self.mask_feature_size  ,num_channels=self.num_channels  ,num_labels=self.num_labels  ,)




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.prepare_config_and_inputs()
      lowerCAmelCase__ :  List[str]							      =					{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
      return config, inputs_dict




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Any:
      lowerCAmelCase__ :  Optional[int]							      =					output.encoder_hidden_states
      lowerCAmelCase__ :  Optional[int]							      =					output.pixel_decoder_hidden_states
      lowerCAmelCase__ :  Dict							      =					output.transformer_decoder_hidden_states

      self.parent.assertTrue(len(__UpperCAmelCase      )  ,len(config.backbone_config.depths      )      )
      self.parent.assertTrue(len(__UpperCAmelCase      )  ,len(config.backbone_config.depths      )      )
      self.parent.assertTrue(len(__UpperCAmelCase      )  ,config.decoder_config.decoder_layers      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=False      )    ->		Optional[Any]:
      with torch.no_grad():
          lowerCAmelCase__ :  int							      =					MaskFormerModel(config=__UpperCAmelCase      )
          model.to(__UpperCAmelCase      )
          model.eval()

          lowerCAmelCase__ :  str							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  int							      =					model(__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )
      # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
      # encoder and pixel decoder
      self.parent.assertEqual(
          output.transformer_decoder_last_hidden_state.shape  ,(self.batch_size, self.num_queries, self.mask_feature_size)  ,)
      # let's ensure the other two hidden state exists
      self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
      self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

      if output_hidden_states:
          self.check_output_hidden_state(__UpperCAmelCase  ,__UpperCAmelCase      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[int]:
      lowerCAmelCase__ :  Dict							      =					MaskFormerForInstanceSegmentation(config=__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.eval()

      def comm_check_on_output(__UpperCAmelCase      ):
          # let's still check that all the required stuff is there
          self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None      )
          self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None      )
          self.parent.assertTrue(result.encoder_last_hidden_state is not None      )
          # okay, now we need to check the logits shape
          # due to the encoder compression, masks have a //4 spatial size
          self.parent.assertEqual(
              result.masks_queries_logits.shape  ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)  ,)
          # + 1 for null class
          self.parent.assertEqual(
              result.class_queries_logits.shape  ,(self.batch_size, self.num_queries, self.num_labels + 1)      )

      with torch.no_grad():
          lowerCAmelCase__ :  List[Any]							      =					model(pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase      )

          comm_check_on_output(__UpperCAmelCase      )

          lowerCAmelCase__ :  Optional[int]							      =					model(
              pixel_values=__UpperCAmelCase  ,pixel_mask=__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      )

      comm_check_on_output(__UpperCAmelCase      )

      self.parent.assertTrue(result.loss is not None      )
      self.parent.assertEqual(result.loss.shape  ,torch.Size([1]      )      )







@require_torch
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
  __lowercase							:      int				=      (
      {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
      if is_torch_available()
      else {}
  )

  __lowercase							:      Union[str, Any]				=      False
  __lowercase							:      Dict				=      False
  __lowercase							:      Tuple				=      False
  __lowercase							:      List[Any]				=      False




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  str							      =					MaskFormerModelTester(self      )
      lowerCAmelCase__ :  List[Any]							      =					ConfigTester(self  ,config_class=__UpperCAmelCase  ,has_text_modality=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      self.config_tester.run_common_tests()




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__UpperCAmelCase      )




  @unittest.skip(reason="""MaskFormer does not use inputs_embeds"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      pass




  @unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""      )
  def 							UpperCAmelCase_	(    self      )    ->		str:
      pass




  @unittest.skip(reason="""MaskFormer is not a generative model"""      )
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      pass




  @unittest.skip(reason="""MaskFormer does not use token embeddings"""      )
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      pass




  @require_torch_multi_gpu
  @unittest.skip(
      reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""      )
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      pass




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




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__ :  Tuple							      =					["""pixel_values"""]
          self.assertListEqual(arg_names[:1]  ,__UpperCAmelCase      )




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model_name in ["facebook/maskformer-swin-small-coco"]:
          lowerCAmelCase__ :  List[str]							      =					MaskFormerModel.from_pretrained(__UpperCAmelCase      )
          self.assertIsNotNone(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(self.model_tester.min_size,) * 2
      lowerCAmelCase__ :  Any							      =					{
          """pixel_values""": torch.randn((2, 3, *size)  ,device=__UpperCAmelCase      ),
          """mask_labels""": torch.randn((2, 10, *size)  ,device=__UpperCAmelCase      ),
          """class_labels""": torch.zeros(2  ,10  ,device=__UpperCAmelCase      ).long(),
      }

      lowerCAmelCase__ :  Tuple							      =					MaskFormerForInstanceSegmentation(MaskFormerConfig()      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )
      self.assertTrue(outputs.loss is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.create_and_check_maskformer_model(__UpperCAmelCase  ,**__UpperCAmelCase  ,output_hidden_states=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          lowerCAmelCase__ :  int							      =					model_class(__UpperCAmelCase      ).to(__UpperCAmelCase      )
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase  ,output_attentions=__UpperCAmelCase      )
          self.assertTrue(outputs.attentions is not None      )




  def 							UpperCAmelCase_	(    self      )    ->		int:
      if not self.model_tester.is_training:
          return
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Dict							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()

      lowerCAmelCase__ :  List[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  List[str]							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      ).loss
      loss.backward()






  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      # only MaskFormerForInstanceSegmentation has the loss
      lowerCAmelCase__ :  Tuple							      =					self.all_model_classes[1]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					self.model_tester.prepare_config_and_inputs()
      lowerCAmelCase__ :  Union[str, Any]							      =					True
      lowerCAmelCase__ :  Tuple							      =					True

      lowerCAmelCase__ :  Optional[Any]							      =					model_class(__UpperCAmelCase      )
      model.to(__UpperCAmelCase      )
      model.train()

      lowerCAmelCase__ :  Dict							      =					model(__UpperCAmelCase  ,mask_labels=__UpperCAmelCase  ,class_labels=__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					outputs.encoder_hidden_states[0]
      encoder_hidden_states.retain_grad()

      lowerCAmelCase__ :  str							      =					outputs.pixel_decoder_hidden_states[0]
      pixel_decoder_hidden_states.retain_grad()
      # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
      lowerCAmelCase__ :  Union[str, Any]							      =					outputs.transformer_decoder_hidden_states[0]
      transformer_decoder_hidden_states.retain_grad()

      lowerCAmelCase__ :  List[Any]							      =					outputs.attentions[0]
      attentions.retain_grad()

      outputs.loss.backward(retain_graph=__UpperCAmelCase      )

      self.assertIsNotNone(encoder_hidden_states.grad      )
      self.assertIsNotNone(pixel_decoder_hidden_states.grad      )
      self.assertIsNotNone(transformer_decoder_hidden_states.grad      )
      self.assertIsNotNone(attentions.grad      )







_lowerCAmelCase      						=	1e-4
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
    return image






@require_vision
@slow
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      return (
          MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          if is_vision_available()
          else None
      )




  def 							UpperCAmelCase_	(    self      )    ->		Any:
      lowerCAmelCase__ :  Any							      =					MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					self.default_image_processor
      lowerCAmelCase__ :  str							      =					prepare_img()
      lowerCAmelCase__ :  Optional[int]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Dict							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor(
          [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]      ).to(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.encoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Dict							      =					torch.tensor(
          [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]      ).to(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )

      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor(
          [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]      ).to(__UpperCAmelCase      )
      self.assertTrue(
          torch.allclose(
              outputs.transformer_decoder_last_hidden_state[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  List[str]							      =					prepare_img()
      lowerCAmelCase__ :  str							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[int]							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  List[Any]							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  Optional[int]							      =					outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,)
      lowerCAmelCase__ :  Optional[int]							      =					[
          [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
          [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
          [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
      ]
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Tuple							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [
              [1.65_12E00, -5.25_72E00, -3.35_19E00],
              [3.61_69E-02, -5.90_25E00, -2.93_13E00],
              [1.07_66E-04, -7.76_30E00, -5.12_63E00],
          ]      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )




  def 							UpperCAmelCase_	(    self      )    ->		str:
      lowerCAmelCase__ :  List[Any]							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Optional[Any]							      =					self.default_image_processor
      lowerCAmelCase__ :  int							      =					prepare_img()
      lowerCAmelCase__ :  Optional[Any]							      =					image_processor(__UpperCAmelCase  ,return_tensors="""pt"""      ).to(__UpperCAmelCase      )
      lowerCAmelCase__ :  str							      =					inputs["""pixel_values"""].shape
      # check size is divisible by 32
      self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0      )
      # check size
      self.assertEqual(__UpperCAmelCase  ,(1, 3, 800, 1088)      )

      with torch.no_grad():
          lowerCAmelCase__ :  str							      =					model(**__UpperCAmelCase      )
      # masks_queries_logits
      lowerCAmelCase__ :  Optional[Any]							      =					outputs.masks_queries_logits
      self.assertEqual(
          masks_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)  ,)
      lowerCAmelCase__ :  int							      =					[[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
      lowerCAmelCase__ :  List[str]							      =					torch.tensor(__UpperCAmelCase      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )
      # class_queries_logits
      lowerCAmelCase__ :  Optional[Any]							      =					outputs.class_queries_logits
      self.assertEqual(
          class_queries_logits.shape  ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)      )
      lowerCAmelCase__ :  Tuple							      =					torch.tensor(
          [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]      ).to(__UpperCAmelCase      )
      self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,__UpperCAmelCase  ,atol=__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  str							      =					(
          MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""      )
          .to(__UpperCAmelCase      )
          .eval()
      )
      lowerCAmelCase__ :  Dict							      =					self.default_image_processor

      lowerCAmelCase__ :  Union[str, Any]							      =					image_processor(
          [np.zeros((3, 800, 1333)      ), np.zeros((3, 800, 1333)      )]  ,segmentation_maps=[np.zeros((384, 384)      ).astype(np.floataa      ), np.zeros((384, 384)      ).astype(np.floataa      )]  ,return_tensors="""pt"""  ,)

      lowerCAmelCase__ :  Tuple							      =					inputs["""pixel_values"""].to(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""mask_labels"""]]
      lowerCAmelCase__ :  Union[str, Any]							      =					[el.to(__UpperCAmelCase      ) for el in inputs["""class_labels"""]]

      with torch.no_grad():
          lowerCAmelCase__ :  Any							      =					model(**__UpperCAmelCase      )

      self.assertTrue(outputs.loss is not None      )


