



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )
@require_tf
class    lowercase__     :







       '''simple docstring'''


       UpperCamelCase		=  XGLMConfig
       UpperCamelCase		=  {}
       UpperCamelCase		=  '''gelu'''



       def __init__(							self    :						List[str]	,       _UpperCAmelCase    :						int	,       _UpperCAmelCase    :						List[str]=14	,       _UpperCAmelCase    :						Union[str, Any]=7	,       _UpperCAmelCase    :						Any=True	,       _UpperCAmelCase    :						List[Any]=True	,       _UpperCAmelCase    :						List[Any]=True	,       _UpperCAmelCase    :						str=99	,       _UpperCAmelCase    :						Union[str, Any]=32	,       _UpperCAmelCase    :						List[Any]=2	,       _UpperCAmelCase    :						str=4	,       _UpperCAmelCase    :						Union[str, Any]=37	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Dict=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Union[str, Any]=512	,       _UpperCAmelCase    :						Optional[Any]=0.02	,       )				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							parent
             UpperCAmelCase_    				=							batch_size
             UpperCAmelCase_    				=							seq_length
             UpperCAmelCase_    				=							is_training
             UpperCAmelCase_    				=							use_input_mask
             UpperCAmelCase_    				=							use_labels
             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							d_model
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							ffn_dim
             UpperCAmelCase_    				=							activation_function
             UpperCAmelCase_    				=							activation_dropout
             UpperCAmelCase_    				=							attention_dropout
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							None
             UpperCAmelCase_    				=							0
             UpperCAmelCase_    				=							2
             UpperCAmelCase_    				=							1



       def   lowercase__       (							self    :						str					)				->		Tuple:


             '''simple docstring'''




             return XGLMConfig.from_pretrained("facebook/xglm-564M"					)



       def   lowercase__       (							self    :						Dict					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							tf.clip_by_value(
                 ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size					)	,       clip_value_min=0	,       clip_value_max=3					)

             UpperCAmelCase_    				=							None
             if self.use_input_mask:
                   UpperCAmelCase_    				=							random_attention_mask([self.batch_size, self.seq_length]					)

             UpperCAmelCase_    				=							self.get_config()

             UpperCAmelCase_    				=							floats_tensor([self.num_hidden_layers, self.num_attention_heads]	,       2					)

             return (
                 config,
                 input_ids,
                 input_mask,
                 head_mask,
             )



       def   lowercase__       (							self    :						Any					)				->		str:


             '''simple docstring'''




             return XGLMConfig(
                 vocab_size=self.vocab_size	,       d_model=self.hidden_size	,       num_layers=self.num_hidden_layers	,       attention_heads=self.num_attention_heads	,       ffn_dim=self.ffn_dim	,       activation_function=self.activation_function	,       activation_dropout=self.activation_dropout	,       attention_dropout=self.attention_dropout	,       max_position_embeddings=self.max_position_embeddings	,       initializer_range=self.initializer_range	,       use_cache=_UpperCAmelCase	,       bos_token_id=self.bos_token_id	,       eos_token_id=self.eos_token_id	,       pad_token_id=self.pad_token_id	,       return_dict=_UpperCAmelCase	,       )







       def   lowercase__       (							self    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.prepare_config_and_inputs()

             (
                 (
                 UpperCAmelCase_
             )				,					(
                 UpperCAmelCase_
             )				,					(
                 UpperCAmelCase_
             )				,					(
                 UpperCAmelCase_
             )				,					
             )    				=							config_and_inputs

             UpperCAmelCase_    				=							{
                 "input_ids": input_ids,
                 "head_mask": head_mask,
             }

             return config, inputs_dict



@require_tf
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
       UpperCamelCase		=  (TFXGLMForCausalLM,) if is_tf_available() else ()
       UpperCamelCase		=  (
           {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
       )
       UpperCamelCase		=  False
       UpperCamelCase		=  False
       UpperCamelCase		=  False



       def   lowercase__       (							self    :						Tuple					)				->		Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFXGLMModelTester(self					)
             UpperCAmelCase_    				=							ConfigTester(self	,       config_class=_UpperCAmelCase	,       n_embd=37					)



       def   lowercase__       (							self    :						Any					)				->		List[Any]:


             '''simple docstring'''




             self.config_tester.run_common_tests()



       @slow
       def   lowercase__       (							self    :						List[str]					)				->		List[Any]:


             '''simple docstring'''




             for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   UpperCAmelCase_    				=							TFXGLMModel.from_pretrained(_UpperCAmelCase					)
                   self.assertIsNotNone(_UpperCAmelCase					)







       @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."					)
       def   lowercase__       (							self    :						List[str]					)				->		Any:


             '''simple docstring'''




             super().test_resize_token_embeddings()



@require_tf
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


       @slow
       def   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						List[Any]=True					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
             UpperCAmelCase_    				=							tf.convert_to_tensor([[2, 268, 9865]]	,       dtype=tf.intaa					)  # The dog
             # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
             # fmt: off
             UpperCAmelCase_    				=							[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
             # fmt: on
             UpperCAmelCase_    				=							model.generate(_UpperCAmelCase	,       do_sample=_UpperCAmelCase	,       num_beams=1					)
             if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist()	,       _UpperCAmelCase					)



       @slow
       def   lowercase__       (							self    :						str					)				->		Dict:


             '''simple docstring'''




             UpperCAmelCase_    				=							XGLMTokenizer.from_pretrained("facebook/xglm-564M"					)
             UpperCAmelCase_    				=							TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)

             tf.random.set_seed(0					)
             UpperCAmelCase_    				=							tokenizer("Today is a nice day and"	,       return_tensors="tf"					)
             UpperCAmelCase_    				=							tokenized.input_ids
             # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
             with tf.device(":/CPU:0"					):
                   UpperCAmelCase_    				=							model.generate(_UpperCAmelCase	,       do_sample=_UpperCAmelCase	,       seed=[7, 0]					)
             UpperCAmelCase_    				=							tokenizer.decode(output_ids[0]	,       skip_special_tokens=_UpperCAmelCase					)

             UpperCAmelCase_    				=							(
                 "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
             )
             self.assertEqual(_UpperCAmelCase	,       _UpperCAmelCase					)







       @slow
       def   lowercase__       (							self    :						str					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
             UpperCAmelCase_    				=							XGLMTokenizer.from_pretrained("facebook/xglm-564M"					)

             UpperCAmelCase_    				=							"left"

             # use different length sentences to test batching
             UpperCAmelCase_    				=							[
                 "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
                 "left-padding, such as in batched generation. The output for the sequence below should be the same "
                 "regardless of whether left padding is applied or not. When",
                 "Hello, my dog is a little",
             ]

             UpperCAmelCase_    				=							tokenizer(_UpperCAmelCase	,       return_tensors="tf"	,       padding=_UpperCAmelCase					)
             UpperCAmelCase_    				=							inputs["input_ids"]

             UpperCAmelCase_    				=							model.generate(input_ids=_UpperCAmelCase	,       attention_mask=inputs["attention_mask"]	,       max_new_tokens=12					)

             UpperCAmelCase_    				=							tokenizer(sentences[0]	,       return_tensors="tf"					).input_ids
             UpperCAmelCase_    				=							model.generate(input_ids=_UpperCAmelCase	,       max_new_tokens=12					)

             UpperCAmelCase_    				=							tokenizer(sentences[1]	,       return_tensors="tf"					).input_ids
             UpperCAmelCase_    				=							model.generate(input_ids=_UpperCAmelCase	,       max_new_tokens=12					)

             UpperCAmelCase_    				=							tokenizer.batch_decode(_UpperCAmelCase	,       skip_special_tokens=_UpperCAmelCase					)
             UpperCAmelCase_    				=							tokenizer.decode(output_non_padded[0]	,       skip_special_tokens=_UpperCAmelCase					)
             UpperCAmelCase_    				=							tokenizer.decode(output_padded[0]	,       skip_special_tokens=_UpperCAmelCase					)

             UpperCAmelCase_    				=							[
                 "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
                 "left-padding, such as in batched generation. The output for the sequence below should be the same "
                 "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
                 "a single",
                 "Hello, my dog is a little bit of a shy one, but he is very friendly",
             ]
             self.assertListEqual(_UpperCAmelCase	,       _UpperCAmelCase					)
             self.assertListEqual(_UpperCAmelCase	,       [non_padded_sentence, padded_sentence]					)









"""simple docstring"""





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()
lowerCamelCase      =		logging.get_logger(__name__)
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							SwinConfig.from_pretrained(
          "microsoft/swin-tiny-patch4-window7-224"			,    out_features=["stage1", "stage2", "stage3", "stage4"]   )
      UpperCAmelCase_    				=							MaskFormerConfig(backbone_config=lowerCAmelCase__   )

      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(lowerCAmelCase__			,    lowerCAmelCase__			,    repo_type="dataset"   )			,    "r"   )   )
      UpperCAmelCase_    				=							{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

      return config
def      a__			(   lowerCAmelCase__   ):
      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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							dct.pop(lowerCAmelCase__   )
      UpperCAmelCase_    				=							val
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # fmt: off
      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      a__			(   ):
      UpperCAmelCase_    				=							"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_    				=							Image.open(requests.get(lowerCAmelCase__			,    stream=lowerCAmelCase__   ).raw   )
      return im
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__ = False   ):
      UpperCAmelCase_    				=							get_maskformer_config(lowerCAmelCase__   )

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

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

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

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

      # load 🤗 model
      UpperCAmelCase_    				=							MaskFormerForInstanceSegmentation(lowerCAmelCase__   )
      model.eval()

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

      UpperCAmelCase_				,					UpperCAmelCase_    				=							model.load_state_dict(lowerCAmelCase__			,    strict=lowerCAmelCase__   )
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(lowerCAmelCase__   ) == 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=lowerCAmelCase__			,    reduce_labels=lowerCAmelCase__   )

      UpperCAmelCase_    				=							image_processor(lowerCAmelCase__			,    return_tensors="pt"   )

      UpperCAmelCase_    				=							model(**lowerCAmelCase__   )

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

      if model_name == "maskformer-swin-tiny-ade":
            UpperCAmelCase_    				=							torch.tensor(
                [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]   )
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,    lowerCAmelCase__			,    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(lowerCAmelCase__   ).mkdir(exist_ok=lowerCAmelCase__   )
            model.save_pretrained(lowerCAmelCase__   )
            image_processor.save_pretrained(lowerCAmelCase__   )

      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__":
  lowerCamelCase      =		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."""
  )

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




