





import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
    from transformers import (
        FlaxBertModel,
        FlaxCLIPVisionModel,
        FlaxVisionTextDualEncoderModel,
        FlaxViTModel,
        VisionTextDualEncoderConfig,
        VisionTextDualEncoderProcessor,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


if is_torch_available():
    import torch

    from transformers import VisionTextDualEncoderModel

if is_vision_available():
    from PIL import Image






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   if isinstance(A				,							collections.abc.Iterable     ):
      return x
   return (x, x)

@require_flax
class     snake_case_		:






 '''simple docstring'''





 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Dict:
    pass

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    pass

 def  __UpperCAmelCase						(  self		)	->       str:
    pass

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=np.abs((a - b)		).max()
    self.assertLessEqual(A_,		A_,		f"""Difference between torch and flax is {diff} (>= {tol})."""		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[str]:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)

    self.assertEqual(output["text_embeds"].shape,		(input_ids.shape[0], model.config.projection_dim)		)
    self.assertEqual(output["image_embeds"].shape,		(pixel_values.shape[0], model.config.projection_dim)		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       Any:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
    UpperCAmelCase__						=output[0]

    with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model(input_ids=A_,		pixel_values=A_,		attention_mask=A_		)
       UpperCAmelCase__						=after_output[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-3		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_,		A_=None,		**A_		)	->       List[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_vision_text_model(A_,		A_		)
    UpperCAmelCase__						={"vision_model": vision_model, "text_model": text_model}
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_		)

    UpperCAmelCase__						=model(
        input_ids=A_,		pixel_values=A_,		attention_mask=A_,		output_attentions=A_		)

    UpperCAmelCase__						=output.vision_model_output.attentions
    self.assertEqual(len(A_		),		vision_config.num_hidden_layers		)

    # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase__						=to_atuple(vision_model.config.image_size		)
    UpperCAmelCase__						=to_atuple(vision_model.config.patch_size		)
    UpperCAmelCase__						=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
    UpperCAmelCase__						=num_patches + 1
    self.assertEqual(vision_attentions[0].shape[-3:],		(vision_config.num_attention_heads, seq_len, seq_len)		)

    UpperCAmelCase__						=output.text_model_output.attentions
    self.assertEqual(len(A_		),		text_config.num_hidden_layers		)

    self.assertEqual(
        text_attentions[0].shape[-3:],		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Union[str, Any]:
    pt_model.to(A_		)
    pt_model.eval()

    # prepare inputs
    UpperCAmelCase__						=inputs_dict
    UpperCAmelCase__						={k: torch.tensor(v.tolist()		) for k, v in flax_inputs.items()}

    with torch.no_grad():
       UpperCAmelCase__						=pt_model(**A_		).to_tuple()

    UpperCAmelCase__						=fx_model(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output in zip(fx_outputs[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # PT -> Flax
    with tempfile.TemporaryDirectory() as tmpdirname:
       pt_model.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_,		from_pt=A_		)

    UpperCAmelCase__						=fx_model_loaded(**A_		).to_tuple()
    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],		pt_outputs[:4]		):
       self.assert_almost_equals(A_,		pt_output.numpy(),		4E-2		)

    # Flax -> PT
    with tempfile.TemporaryDirectory() as tmpdirname:
       fx_model.save_pretrained(A_		)
       UpperCAmelCase__						=VisionTextDualEncoderModel.from_pretrained(A_,		from_flax=A_		)

    pt_model_loaded.to(A_		)
    pt_model_loaded.eval()

    with torch.no_grad():
       UpperCAmelCase__						=pt_model_loaded(**A_		).to_tuple()

    self.assertEqual(len(A_		),		len(A_		),		"Output lengths differ between Flax and PyTorch"		)
    for fx_output, pt_output_loaded in zip(fx_outputs[:4],		pt_outputs_loaded[:4]		):
       self.assert_almost_equals(A_,		pt_output_loaded.numpy(),		4E-2		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=convert_pytorch_state_dict_to_flax(pt_model.state_dict(),		A_		)
    UpperCAmelCase__						=fx_state

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Dict:
    UpperCAmelCase__						=VisionTextDualEncoderConfig.from_vision_text_configs(A_,		A_		)

    UpperCAmelCase__						=VisionTextDualEncoderModel(A_		)
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel(A_		)

    UpperCAmelCase__						=load_flax_weights_in_pytorch_model(A_,		fx_model.params		)

    self.check_pt_flax_equivalence(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_model_from_pretrained_configs(**A_		)

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_dual_encoder_from_pretrained(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_save_load(**A_		)

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    self.check_vision_text_output_attention(**A_		)

 @is_pt_flax_cross_test
 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.prepare_config_and_inputs()
    UpperCAmelCase__						=config_inputs_dict.pop("vision_config"		)
    UpperCAmelCase__						=config_inputs_dict.pop("text_config"		)

    UpperCAmelCase__						=config_inputs_dict

    self.check_equivalence_pt_to_flax(A_,		A_,		A_		)
    self.check_equivalence_flax_to_pt(A_,		A_,		A_		)






 @slow
 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.get_pretrained_model_and_inputs()

    UpperCAmelCase__						=model_a(**A_		)
    UpperCAmelCase__						=outputs[0]

    with tempfile.TemporaryDirectory() as tmp_dirname:
       model_a.save_pretrained(A_		)
       UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained(A_		)

       UpperCAmelCase__						=model_a(**A_		)
       UpperCAmelCase__						=after_outputs[0]
       UpperCAmelCase__						=np.amax(np.abs(out_a - out_a		)		)
       self.assertLessEqual(A_,		1E-5		)

@require_flax
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-vit",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=FlaxViTModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__						=FlaxViTModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=vit_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_torch
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
        "hf-internal-testing/tiny-random-clip",		"hf-internal-testing/tiny-bert",		vision_from_pt=A_,		text_from_pt=A_,		)
    UpperCAmelCase__						=13
    UpperCAmelCase__						=floats_tensor(
        [
            batch_size,
            model.config.vision_config.num_channels,
            model.config.vision_config.image_size,
            model.config.vision_config.image_size,
        ]		)
    UpperCAmelCase__						=ids_tensor([batch_size, 4],		model.config.text_config.vocab_size		)
    UpperCAmelCase__						=random_attention_mask([batch_size, 4]		)
    UpperCAmelCase__						={"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

    return model, inputs

 def  __UpperCAmelCase						(  self,		A_,		A_		)	->       Tuple:
    UpperCAmelCase__						=FlaxCLIPVisionModel(A_		)
    UpperCAmelCase__						=FlaxBertModel(A_		)
    return vision_model, text_model






 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxCLIPVisionModelTester(self		)
    UpperCAmelCase__						=FlaxBertModelTester(self		)
    UpperCAmelCase__						=clip_model_tester.prepare_config_and_inputs()
    UpperCAmelCase__						=bert_model_tester.prepare_config_and_inputs()

    UpperCAmelCase__			,							UpperCAmelCase__						=vision_config_and_inputs

    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=text_config_and_inputs

    # make sure that cross attention layers are added
    return {
        "text_config": text_config,
        "vision_config": vision_config,
        "pixel_values": pixel_values,
        "attention_mask": attention_mask,
        "input_ids": input_ids,
        "token_type_ids": token_type_ids,
    }

@require_flax
@require_vision
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 @slow
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian",		logit_scale_init_value=1.0		)
    UpperCAmelCase__						=VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"		)

    UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    UpperCAmelCase__						=processor(
        text=["una foto di un gatto", "una foto di un cane"],		images=A_,		padding=A_,		return_tensors="np"		)

    UpperCAmelCase__						=model(**A_		)

    # verify the logits
    self.assertEqual(outputs.logits_per_image.shape,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])		)
    self.assertEqual(
        outputs.logits_per_text.shape,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),		)

    UpperCAmelCase__						=np.array([[1.2_28_47_27, 0.3_10_41_22]]		)

    self.assertTrue(np.allclose(outputs.logits_per_image,		A_,		atol=1E-3		)		)










import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


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






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224"				,							out_features=["stage1", "stage2", "stage3", "stage4"]     )
   UpperCAmelCase__						=MaskFormerConfig(backbone_config=A     )

   UpperCAmelCase__						="huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCAmelCase__						=847
      UpperCAmelCase__						="maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      UpperCAmelCase__						=150
      UpperCAmelCase__						="ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCAmelCase__						=171
      UpperCAmelCase__						="maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      UpperCAmelCase__						=133
      UpperCAmelCase__						="coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCAmelCase__						=19
      UpperCAmelCase__						="cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      UpperCAmelCase__						=65
      UpperCAmelCase__						="mapillary-vistas-id2label.json"

   UpperCAmelCase__						=json.load(open(hf_hub_download(A				,							A				,							repo_type="dataset"     )				,							"r"     )     )
   UpperCAmelCase__						={int(A     ): v for k, v in idalabel.items()}

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[]
   # stem
   # fmt: off
   rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")     )
   rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")     )
   rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")     )
   rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")     )
   # stages
   for i in range(len(config.backbone_config.depths     )     ):
      for j in range(config.backbone_config.depths[i]     ):
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

      if i < 3:
         rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
         rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
      rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
      rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

   # FPN
   rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")     )
   rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")     )
   rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")     )
   for source_index, target_index in zip(range(3				,							0				,							-1     )				,							range(0				,							3     )     ):
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
      rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
      rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
   rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")     )
   rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")     )

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers     ):
      # self-attention out projection
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
      # cross-attention out projection
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
      # MLP 1
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
      # MLP 2
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
      # layernorm 3 (final layernorm)
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")     )
   rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")     )

   # heads on top
   rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")     )

   rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")     )
   rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")     )

   rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")     )
   rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")     )

   for i in range(3     ):
      rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")     )
      rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")     )
   # fmt: on

   return rename_keys






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=dct.pop(A     )
   UpperCAmelCase__						=val






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
   for i in range(len(backbone_config.depths     )     ):
      UpperCAmelCase__						=num_features[i]
      for j in range(backbone_config.depths[i]     ):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         UpperCAmelCase__						=state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
         UpperCAmelCase__						=state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
         # next, add query, keys and values (in that order) to the state dict
         UpperCAmelCase__						=in_proj_weight[:dim, :]
         UpperCAmelCase__						=in_proj_bias[: dim]
         UpperCAmelCase__						=in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase__						=in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase__						=in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase__						=in_proj_bias[-dim :]
         # fmt: on






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers     ):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase__						=in_proj_weight[: hidden_size, :]
      UpperCAmelCase__						=in_proj_bias[:config.hidden_size]
      UpperCAmelCase__						=in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__						=in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__						=in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__						=in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
      UpperCAmelCase__						=state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase__						=in_proj_weight[: hidden_size, :]
      UpperCAmelCase__						=in_proj_bias[:config.hidden_size]
      UpperCAmelCase__						=in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase__						=in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase__						=in_proj_weight[-hidden_size :, :]
      UpperCAmelCase__						=in_proj_bias[-hidden_size :]
   # fmt: on






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase__						=Image.open(requests.get(A				,							stream=A     ).raw     )
   return im






@torch.no_grad()
def 				_UpperCAmelCase			(    A				,							A				,							A				,							A = False     ):




   '''simple docstring'''

   UpperCAmelCase__						=get_maskformer_config(A     )

   # load original state_dict
   with open(A				,							"rb"     ) as f:
      UpperCAmelCase__						=pickle.load(A     )
   UpperCAmelCase__						=data["model"]

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   UpperCAmelCase__						=create_rename_keys(A     )
   for src, dest in rename_keys:
      rename_key(A				,							A				,							A     )
   read_in_swin_q_k_v(A				,							config.backbone_config     )
   read_in_decoder_q_k_v(A				,							A     )

   # update to torch tensors
   for key, value in state_dict.items():
      UpperCAmelCase__						=torch.from_numpy(A     )

   # load 🤗 model
   UpperCAmelCase__						=MaskFormerForInstanceSegmentation(A     )
   model.eval()

   for name, param in model.named_parameters():
      print(A				,							param.shape     )

   UpperCAmelCase__			,							UpperCAmelCase__						=model.load_state_dict(A				,							strict=A     )
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(A     ) == 0, F"""Unexpected keys: {unexpected_keys}"""

   # verify results
   UpperCAmelCase__						=prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase__						=65
   elif "cityscapes" in model_name:
      UpperCAmelCase__						=65535
   else:
      UpperCAmelCase__						=255
   UpperCAmelCase__						=True if "ade" in model_name else False
   UpperCAmelCase__						=MaskFormerImageProcessor(ignore_index=A				,							reduce_labels=A     )

   UpperCAmelCase__						=image_processor(A				,							return_tensors="pt"     )

   UpperCAmelCase__						=model(**A     )

   print("Logits:"				,							outputs.class_queries_logits[0, :3, :3]     )

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase__						=torch.tensor(
          [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]     )
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,							A				,							atol=1e-4     )
   print("Looks ok!"     )

   if pytorch_dump_folder_path is not None:
      print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
      Path(A     ).mkdir(exist_ok=A     )
      model.save_pretrained(A     )
      image_processor.save_pretrained(A     )

   if push_to_hub:
      print("Pushing model and image processor to the hub..."     )
      model.push_to_hub(F"""nielsr/{model_name}"""     )
      image_processor.push_to_hub(F"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='maskformer-swin-tiny-ade',
        type=str,
        help=('Name of the MaskFormer model you\'d like to convert',),
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
        type=str,
        help='Path to the original state dict (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    UpperCamelCase_												=	parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )



