


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						Any     =							{
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Optional[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      Optional[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      List[str]       						=						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":
            __lowerCamelCase     :      Union[str, Any]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Any       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      Dict       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      int       						=						value
      else:
            __lowerCamelCase     :      Optional[Any]       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[Any]       						=						[]
      __lowerCamelCase     :      List[Any]       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      str       						=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      Optional[int]       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Optional[int]       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        __lowerCamelCase     :      Tuple       						=						"""sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      Union[str, Any]       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      Dict       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Any       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      str       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      Union[str, Any]       						=						"""weight_v"""
                              elif "weight" in name:
                                    __lowerCamelCase     :      Any       						=						"""weight"""
                              elif "bias" in name:
                                    __lowerCamelCase     :      Any       						=						"""bias"""
                              else:
                                    __lowerCamelCase     :      Union[str, Any]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      Optional[Any]       						=						name.split("""."""      )
      __lowerCamelCase     :      Optional[int]       						=						int(items[0]      )
      __lowerCamelCase     :      Optional[int]       						=						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."""
                  )
                  __lowerCamelCase     :      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."""
                  )
                  __lowerCamelCase     :      Optional[int]       						=						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."
                  )
                  __lowerCamelCase     :      Dict       						=						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."""
                  )
                  __lowerCamelCase     :      Any       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						SEWConfig()
      if is_finetuned:
            __lowerCamelCase     :      Dict       						=						model.wav_encoder.wav_model.cfg
      else:
            __lowerCamelCase     :      Any       						=						model.cfg

      __lowerCamelCase     :      List[str]       						=						fs_config.conv_bias
      __lowerCamelCase     :      List[str]       						=						eval(fs_config.conv_feature_layers      )
      __lowerCamelCase     :      Any       						=						[x[0] for x in conv_layers]
      __lowerCamelCase     :      Tuple       						=						[x[1] for x in conv_layers]
      __lowerCamelCase     :      List[str]       						=						[x[2] for x in conv_layers]
      __lowerCamelCase     :      Dict       						=						"""gelu"""
      __lowerCamelCase     :      str       						=						"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      __lowerCamelCase     :      Any       						=						0.0
      __lowerCamelCase     :      Optional[int]       						=						fs_config.activation_fn.name
      __lowerCamelCase     :      Optional[Any]       						=						fs_config.encoder_embed_dim
      __lowerCamelCase     :      List[str]       						=						0.0_2
      __lowerCamelCase     :      Dict       						=						fs_config.encoder_ffn_embed_dim
      __lowerCamelCase     :      Dict       						=						1e-5
      __lowerCamelCase     :      str       						=						fs_config.encoder_layerdrop
      __lowerCamelCase     :      int       						=						fs_config.encoder_attention_heads
      __lowerCamelCase     :      List[str]       						=						fs_config.conv_pos_groups
      __lowerCamelCase     :      Tuple       						=						fs_config.conv_pos
      __lowerCamelCase     :      Optional[Any]       						=						len(UpperCAmelCase      )
      __lowerCamelCase     :      Optional[Any]       						=						fs_config.encoder_layers
      __lowerCamelCase     :      str       						=						fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            __lowerCamelCase     :      Any       						=						model.cfg
            __lowerCamelCase     :      Dict       						=						fs_config.final_dropout
            __lowerCamelCase     :      Any       						=						fs_config.layerdrop
      __lowerCamelCase     :      Union[str, Any]       						=						fs_config.activation_dropout
      __lowerCamelCase     :      Tuple       						=						fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      __lowerCamelCase     :      List[str]       						=						fs_config.attention_dropout
      __lowerCamelCase     :      Union[str, Any]       						=						fs_config.dropout_input
      __lowerCamelCase     :      List[Any]       						=						fs_config.dropout
      __lowerCamelCase     :      List[str]       						=						fs_config.mask_channel_length
      __lowerCamelCase     :      Optional[Any]       						=						fs_config.mask_channel_prob
      __lowerCamelCase     :      Optional[Any]       						=						fs_config.mask_length
      __lowerCamelCase     :      Optional[Any]       						=						fs_config.mask_prob

      __lowerCamelCase     :      str       						=						"""Wav2Vec2FeatureExtractor"""
      __lowerCamelCase     :      List[str]       						=						"""Wav2Vec2CTCTokenizer"""

      return config





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Union[str, Any]=None						,	UpperCAmelCase      :			Any=None						,	UpperCAmelCase      :			List[str]=True      ):
      """simple docstring"""




      if is_finetuned:
            __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]						,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""      )[:-1]      )}      )
      else:
            __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

      if config_path is not None:
            __lowerCamelCase     :      List[str]       						=						SEWConfig.from_pretrained(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      List[Any]       						=						convert_config(model[0]						,	UpperCAmelCase      )
      __lowerCamelCase     :      Dict       						=						model[0].eval()

      __lowerCamelCase     :      Any       						=						True if config.feat_extract_norm == """layer""" else False
      __lowerCamelCase     :      Any       						=						WavaVecaFeatureExtractor(
          feature_size=1						,	sampling_rate=16_000						,	padding_value=0						,	do_normalize=UpperCAmelCase						,	return_attention_mask=UpperCAmelCase						,	)

      if is_finetuned:
            if dict_path:
                  __lowerCamelCase     :      Union[str, Any]       						=						Dictionary.load(UpperCAmelCase      )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __lowerCamelCase     :      int       						=						target_dict.pad_index
                  __lowerCamelCase     :      Optional[Any]       						=						target_dict.bos_index
                  __lowerCamelCase     :      Union[str, Any]       						=						target_dict.pad_index
                  __lowerCamelCase     :      Union[str, Any]       						=						target_dict.bos_index
                  __lowerCamelCase     :      List[str]       						=						target_dict.eos_index
                  __lowerCamelCase     :      Tuple       						=						len(target_dict.symbols      )
                  __lowerCamelCase     :      Optional[int]       						=						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      )
                  with open(UpperCAmelCase						,	"""w"""						,	encoding="""utf-8"""      ) as vocab_handle:
                        json.dump(target_dict.indices						,	UpperCAmelCase      )
                  __lowerCamelCase     :      int       						=						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     :      List[str]       						=						WavaVecaProcessor(feature_extractor=UpperCAmelCase						,	tokenizer=UpperCAmelCase      )
                  processor.save_pretrained(UpperCAmelCase      )

            __lowerCamelCase     :      Tuple       						=						SEWForCTC(UpperCAmelCase      )
      else:
            __lowerCamelCase     :      Any       						=						SEWModel(UpperCAmelCase      )
            feature_extractor.save_pretrained(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )

      hf_model.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Union[str, 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(
        '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    __UpperCamelCase    :						int     =							parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )







import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, 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 MaskaFormerForUniversalSegmentation, MaskaFormerModel

    if is_vision_available():
        from transformers import MaskaFormerImageProcessor

if is_vision_available():
    from PIL import Image



class 							_UpperCamelCase :

   '''simple docstring'''







   def __init__(  self  : int       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Tuple=2       ,  _lowerCamelCase  : Optional[int]=True       ,  _lowerCamelCase  : Union[str, Any]=False       ,  _lowerCamelCase  : Dict=1_0       ,  _lowerCamelCase  : Union[str, Any]=3       ,  _lowerCamelCase  : Tuple=3_2 * 8       ,  _lowerCamelCase  : int=3_2 * 8       ,  _lowerCamelCase  : str=4       ,  _lowerCamelCase  : List[Any]=6_4       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						parent
         __lowerCamelCase     :      Optional[int]       						=						batch_size
         __lowerCamelCase     :      List[str]       						=						is_training
         __lowerCamelCase     :      Dict       						=						use_auxiliary_loss
         __lowerCamelCase     :      Optional[int]       						=						num_queries
         __lowerCamelCase     :      Optional[Any]       						=						num_channels
         __lowerCamelCase     :      Dict       						=						min_size
         __lowerCamelCase     :      Optional[Any]       						=						max_size
         __lowerCamelCase     :      str       						=						num_labels
         __lowerCamelCase     :      Optional[Any]       						=						hidden_dim
         __lowerCamelCase     :      Optional[Any]       						=						hidden_dim

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
             _lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						torch.ones([self.batch_size, self.min_size, self.max_size]       ,  device=_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						(
             torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,  device=_lowerCamelCase							) > 0.5
         ).float()
         __lowerCamelCase     :      List[Any]       						=						(torch.rand((self.batch_size, self.num_labels)       ,  device=_lowerCamelCase							) > 0.5).long()

         __lowerCamelCase     :      Optional[Any]       						=						self.get_config()
         return config, pixel_values, pixel_mask, mask_labels, class_labels

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						MaskaFormerConfig(
             hidden_size=self.hidden_dim       ,  )
         __lowerCamelCase     :      List[Any]       						=						self.num_queries
         __lowerCamelCase     :      List[str]       						=						self.num_labels

         __lowerCamelCase     :      List[str]       						=						[1, 1, 1, 1]
         __lowerCamelCase     :      Optional[int]       						=						self.num_channels

         __lowerCamelCase     :      Optional[int]       						=						6_4
         __lowerCamelCase     :      int       						=						1_2_8
         __lowerCamelCase     :      Any       						=						self.hidden_dim
         __lowerCamelCase     :      List[str]       						=						self.hidden_dim
         __lowerCamelCase     :      List[Any]       						=						self.hidden_dim
         return config

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      int       						=						self.prepare_config_and_inputs()
         __lowerCamelCase     :      Optional[int]       						=						{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
         return config, inputs_dict

   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						output.encoder_hidden_states
         __lowerCamelCase     :      Any       						=						output.pixel_decoder_hidden_states
         __lowerCamelCase     :      Optional[Any]       						=						output.transformer_decoder_hidden_states

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

   def   _snake_case    (  self  : Tuple       ,  _lowerCamelCase  : List[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : List[str]=False							):






         '''simple docstring'''



         with torch.no_grad():
               __lowerCamelCase     :      Any       						=						MaskaFormerModel(config=_lowerCamelCase							)
               model.to(_lowerCamelCase							)
               model.eval()

               __lowerCamelCase     :      Dict       						=						model(pixel_values=_lowerCamelCase       ,  pixel_mask=_lowerCamelCase							)
               __lowerCamelCase     :      Optional[Any]       						=						model(_lowerCamelCase       ,  output_hidden_states=_lowerCamelCase							)

         self.parent.assertEqual(
             output.transformer_decoder_last_hidden_state.shape       ,  (self.batch_size, self.num_queries, self.hidden_dim)       ,  )
         # 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(_lowerCamelCase       ,  _lowerCamelCase							)


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						MaskaFormerForUniversalSegmentation(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()

         def comm_check_on_output(_lowerCamelCase  : Optional[int]							):
               # 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     :      Optional[int]       						=						model(pixel_values=_lowerCamelCase       ,  pixel_mask=_lowerCamelCase							)
               __lowerCamelCase     :      Tuple       						=						model(_lowerCamelCase							)

               comm_check_on_output(_lowerCamelCase							)

               __lowerCamelCase     :      str       						=						model(
                   pixel_values=_lowerCamelCase       ,  pixel_mask=_lowerCamelCase       ,  mask_labels=_lowerCamelCase       ,  class_labels=_lowerCamelCase							)

         comm_check_on_output(_lowerCamelCase							)

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



@require_torch
class 							_UpperCamelCase (						A,A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							Union[str, Any]    = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
   a_				:							Union[str, Any]    = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

   a_				:							Optional[Any]    = False
   a_				:							int    = False
   a_				:							List[str]    = False
   a_				:							List[Any]    = False

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						MaskaFormerModelTester(self							)
         __lowerCamelCase     :      int       						=						ConfigTester(self       ,  config_class=_lowerCamelCase       ,  has_text_modality=_lowerCamelCase							)

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         self.config_tester.run_common_tests()

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase       ,  **_lowerCamelCase       ,  output_hidden_states=_lowerCamelCase							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCamelCase							)

   @unittest.skip(reason="""Mask2Former does not use inputs_embeds"""							)
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         pass

   @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""							)
   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         pass

   @unittest.skip(reason="""Mask2Former is not a generative model"""							)
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         pass

   @unittest.skip(reason="""Mask2Former does not use token embeddings"""							)
   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         pass

   @require_torch_multi_gpu
   @unittest.skip(
       reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""							)
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         pass

   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						self.model_tester.prepare_config_and_inputs_for_common()

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

               __lowerCamelCase     :      List[Any]       						=						["""pixel_values"""]
               self.assertListEqual(arg_names[:1]       ,  _lowerCamelCase							)

   @slow
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               __lowerCamelCase     :      Dict       						=						MaskaFormerModel.from_pretrained(_lowerCamelCase							)
               self.assertIsNotNone(_lowerCamelCase							)

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						(self.model_tester.min_size,) * 2
         __lowerCamelCase     :      Optional[Any]       						=						{
             """pixel_values""": torch.randn((2, 3, *size)       ,  device=_lowerCamelCase							),
             """mask_labels""": torch.randn((2, 1_0, *size)       ,  device=_lowerCamelCase							),
             """class_labels""": torch.zeros(2       ,  1_0       ,  device=_lowerCamelCase							).long(),
         }
         __lowerCamelCase     :      Any       						=						self.model_tester.get_config()

         __lowerCamelCase     :      Tuple       						=						MaskaFormerForUniversalSegmentation(_lowerCamelCase							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						model(**_lowerCamelCase							)
         self.assertTrue(outputs.loss is not None							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      Any       						=						self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.create_and_check_maskaformer_model(_lowerCamelCase       ,  **_lowerCamelCase       ,  output_hidden_states=_lowerCamelCase							)

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						self.model_tester.prepare_config_and_inputs_for_common()

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

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         if not self.model_tester.is_training:
               return

         __lowerCamelCase     :      str       						=						self.all_model_classes[1]
         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						self.model_tester.prepare_config_and_inputs()

         __lowerCamelCase     :      List[Any]       						=						model_class(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.train()

         __lowerCamelCase     :      Tuple       						=						model(_lowerCamelCase       ,  mask_labels=_lowerCamelCase       ,  class_labels=_lowerCamelCase							).loss
         loss.backward()


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.all_model_classes[1]
         __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Optional[int]       						=						self.model_tester.prepare_config_and_inputs()
         __lowerCamelCase     :      Optional[int]       						=						True
         __lowerCamelCase     :      Any       						=						True

         __lowerCamelCase     :      Any       						=						model_class(_lowerCamelCase							).to(_lowerCamelCase							)
         model.train()

         __lowerCamelCase     :      Optional[Any]       						=						model(_lowerCamelCase       ,  mask_labels=_lowerCamelCase       ,  class_labels=_lowerCamelCase							)

         __lowerCamelCase     :      Union[str, Any]       						=						outputs.encoder_hidden_states[0]
         encoder_hidden_states.retain_grad()

         __lowerCamelCase     :      Any       						=						outputs.pixel_decoder_hidden_states[0]
         pixel_decoder_hidden_states.retain_grad()

         __lowerCamelCase     :      Optional[Any]       						=						outputs.transformer_decoder_hidden_states[0]
         transformer_decoder_hidden_states.retain_grad()

         __lowerCamelCase     :      List[str]       						=						outputs.attentions[0]
         attentions.retain_grad()

         outputs.loss.backward(retain_graph=_lowerCamelCase							)

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

__UpperCamelCase    :						str     =							1E-4





def   _UpperCAmelCase       (     ):
      """simple docstring"""




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




@require_vision
@slow
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @cached_property
   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         return "facebook/mask2former-swin-small-coco-instance"

   @cached_property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints							) if is_vision_available() else None

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						MaskaFormerModel.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						self.default_image_processor
         __lowerCamelCase     :      Dict       						=						prepare_img()
         __lowerCamelCase     :      List[Any]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							).to(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						inputs["""pixel_values"""].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
         # check size
         self.assertEqual(_lowerCamelCase       ,  (1, 3, 3_8_4, 3_8_4)							)

         with torch.no_grad():
               __lowerCamelCase     :      str       						=						model(**_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						torch.tensor(
             [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

         __lowerCamelCase     :      Union[str, Any]       						=						torch.tensor(
             [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

         __lowerCamelCase     :      Tuple       						=						torch.tensor(
             [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]							).to(_lowerCamelCase							)
         self.assertTrue(
             torch.allclose(
                 outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							).eval()
         __lowerCamelCase     :      Any       						=						self.default_image_processor
         __lowerCamelCase     :      Union[str, Any]       						=						prepare_img()
         __lowerCamelCase     :      int       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							).to(_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						inputs["""pixel_values"""].shape
         # check size is divisible by 32
         self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
         # check size
         self.assertEqual(_lowerCamelCase       ,  (1, 3, 3_8_4, 3_8_4)							)

         with torch.no_grad():
               __lowerCamelCase     :      Dict       						=						model(**_lowerCamelCase							)
         # masks_queries_logits
         __lowerCamelCase     :      Dict       						=						outputs.masks_queries_logits
         self.assertEqual(
             masks_queries_logits.shape       ,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)							)
         __lowerCamelCase     :      Any       						=						[
             [-8.7_839, -9.0_056, -8.8_121],
             [-7.4_104, -7.0_313, -6.5_401],
             [-6.6_105, -6.3_427, -6.4_675],
         ]
         __lowerCamelCase     :      List[str]       						=						torch.tensor(_lowerCamelCase							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)
         # class_queries_logits
         __lowerCamelCase     :      Union[str, Any]       						=						outputs.class_queries_logits
         self.assertEqual(class_queries_logits.shape       ,  (1, model.config.num_queries, model.config.num_labels + 1)							)
         __lowerCamelCase     :      List[Any]       						=						torch.tensor(
             [
                 [1.8_324, -8.0_835, -4.1_922],
                 [0.8_450, -9.0_050, -3.6_053],
                 [0.3_045, -7.7_293, -3.0_275],
             ]							).to(_lowerCamelCase							)
         self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,  _lowerCamelCase       ,  atol=_lowerCamelCase							)							)


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints							).to(_lowerCamelCase							).eval()
         __lowerCamelCase     :      Dict       						=						self.default_image_processor

         __lowerCamelCase     :      List[Any]       						=						image_processor(
             [np.zeros((3, 8_0_0, 1_3_3_3)							), np.zeros((3, 8_0_0, 1_3_3_3)							)]       ,  segmentation_maps=[np.zeros((3_8_4, 3_8_4)							).astype(np.floataa							), np.zeros((3_8_4, 3_8_4)							).astype(np.floataa							)]       ,  return_tensors="""pt"""       ,  )

         __lowerCamelCase     :      int       						=						inputs["""pixel_values"""].to(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						[el.to(_lowerCamelCase							) for el in inputs["""mask_labels"""]]
         __lowerCamelCase     :      Union[str, Any]       						=						[el.to(_lowerCamelCase							) for el in inputs["""class_labels"""]]

         with torch.no_grad():
               __lowerCamelCase     :      Dict       						=						model(**_lowerCamelCase							)

         self.assertTrue(outputs.loss is not None							)



