



'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_lowerCAmelCase      						=	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  _lowerCAmelCase      						=	F"""down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   _lowerCAmelCase      						=	F"""down_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  _lowerCAmelCase      						=	F"""up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   _lowerCAmelCase      						=	F"""up_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0.conv."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowerCAmelCase      						=	'''mid_block.attentions.0.'''
_lowerCAmelCase      						=	'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{j}."""
 _lowerCAmelCase      						=	F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        lowerCAmelCase__ :  Optional[int]							      =					sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                lowerCAmelCase__ :  Any							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowerCAmelCase__ :  List[Any]							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    lowerCAmelCase__ :  Tuple							      =					{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  _lowerCAmelCase      						=	F"""encoder.down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0."""
  _lowerCAmelCase      						=	F"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  _lowerCAmelCase      						=	F"""decoder.up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{i}."""
 _lowerCAmelCase      						=	F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return w.reshape(*w.shape							,    1							,    1			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            lowerCAmelCase__ :  str							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCAmelCase__ :  Dict							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    lowerCAmelCase__ :  Union[str, Any]							      =					{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCAmelCase__ :  Tuple							      =					["""q""", """k""", """v""", """proj_out"""]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"""mid.attn_1.{weight_name}.weight""" in k:
                print(f"""Reshaping {k} for SD format"""			)
                lowerCAmelCase__ :  Optional[int]							      =					reshape_weight_for_sd(UpperCamelCase			)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowerCAmelCase      						=	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase      						=	re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase      						=	{'''q''': 0, '''k''': 1, '''v''': 2}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					{}
    lowerCAmelCase__ :  int							      =					{}
    lowerCAmelCase__ :  List[Any]							      =					{}
    for k, v in text_enc_dict.items():
        if (
            k.endswith(""".self_attn.q_proj.weight"""			)
            or k.endswith(""".self_attn.k_proj.weight"""			)
            or k.endswith(""".self_attn.v_proj.weight"""			)
        ):
            lowerCAmelCase__ :  Optional[int]							      =					k[: -len(""".q_proj.weight"""			)]
            lowerCAmelCase__ :  Tuple							      =					k[-len("""q_proj.weight"""			)]
            if k_pre not in capture_qkv_weight:
                lowerCAmelCase__ :  List[Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Dict							      =					v
            continue

        if (
            k.endswith(""".self_attn.q_proj.bias"""			)
            or k.endswith(""".self_attn.k_proj.bias"""			)
            or k.endswith(""".self_attn.v_proj.bias"""			)
        ):
            lowerCAmelCase__ :  str							      =					k[: -len(""".q_proj.bias"""			)]
            lowerCAmelCase__ :  List[str]							      =					k[-len("""q_proj.bias"""			)]
            if k_pre not in capture_qkv_bias:
                lowerCAmelCase__ :  Union[str, Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Any							      =					v
            continue

        lowerCAmelCase__ :  Dict							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  Any							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Tuple							      =					torch.cat(UpperCamelCase			)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  str							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					torch.cat(UpperCamelCase			)

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return text_enc_dict


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  _lowerCAmelCase      						=	load_file(unet_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  _lowerCAmelCase      						=	load_file(vae_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  _lowerCAmelCase      						=	load_file(text_enc_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 _lowerCAmelCase      						=	convert_unet_state_dict(unet_state_dict)
 _lowerCAmelCase      						=	{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 _lowerCAmelCase      						=	convert_vae_state_dict(vae_state_dict)
 _lowerCAmelCase      						=	{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 _lowerCAmelCase      						=	'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  _lowerCAmelCase      						=	{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  _lowerCAmelCase      						=	convert_text_enc_state_dict_vaa(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  _lowerCAmelCase      						=	convert_text_enc_state_dict(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 _lowerCAmelCase      						=	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  _lowerCAmelCase      						=	{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  _lowerCAmelCase      						=	{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)







'''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      )


