
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 __lowerCAmelCase    :	List[Any]						=						"platform"

 import jax
 import jax.numpy as jnp

 from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
     FlaxBlenderbotSmallForConditionalGeneration,
     FlaxBlenderbotSmallModel,
     shift_tokens_right,
 )


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    )       ->  List[str]:
    if attention_mask is None:
        __lowercase						:	Union[str, Any]					=				np.where(input_ids != config.pad_token_id		,    1		,    0		)
    if decoder_attention_mask is None:
        __lowercase						:	Optional[int]					=				np.where(decoder_input_ids != config.pad_token_id		,    1		,    0		)
    if head_mask is None:
        __lowercase						:	int					=				np.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        __lowercase						:	Union[str, Any]					=				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    if cross_attn_head_mask is None:
        __lowercase						:	List[Any]					=				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }


class   __lowerCAmelCase		:



      """simple docstring"""







      def __init__(					self						:				Union[str, Any] ,				_snake_case						:				int ,				_snake_case						:				Union[str, Any]=13 ,				_snake_case						:				List[str]=7 ,				_snake_case						:				List[str]=True ,				_snake_case						:				int=False ,				_snake_case						:				Optional[Any]=99 ,				_snake_case						:				List[Any]=16 ,				_snake_case						:				int=2 ,				_snake_case						:				Dict=4 ,				_snake_case						:				Union[str, Any]=4 ,				_snake_case						:				Optional[Any]="gelu" ,				_snake_case						:				int=0.1 ,				_snake_case						:				int=0.1 ,				_snake_case						:				List[str]=32 ,				_snake_case						:				Tuple=2 ,				_snake_case						:				Dict=1 ,				_snake_case						:				List[str]=0 ,				_snake_case						:				List[str]=0.02 ,				):
          __lowercase						:	List[str]					=				parent
          __lowercase						:	Tuple					=				batch_size
          __lowercase						:	str					=				seq_length
          __lowercase						:	Dict					=				is_training
          __lowercase						:	List[Any]					=				use_labels
          __lowercase						:	Optional[int]					=				vocab_size
          __lowercase						:	int					=				hidden_size
          __lowercase						:	Optional[Any]					=				num_hidden_layers
          __lowercase						:	Dict					=				num_attention_heads
          __lowercase						:	List[str]					=				intermediate_size
          __lowercase						:	Optional[int]					=				hidden_act
          __lowercase						:	str					=				hidden_dropout_prob
          __lowercase						:	int					=				attention_probs_dropout_prob
          __lowercase						:	Optional[Any]					=				max_position_embeddings
          __lowercase						:	str					=				eos_token_id
          __lowercase						:	str					=				pad_token_id
          __lowercase						:	str					=				bos_token_id
          __lowercase						:	List[Any]					=				initializer_range




      def 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	Optional[Any]					=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size ) ,				3 ,				self.vocab_size )
          __lowercase						:	Any					=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa )) ,				-1 )

          __lowercase						:	str					=				shift_tokens_right(lowercase_ ,				1 ,				2 )

          __lowercase						:	str					=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size ,				d_model=self.hidden_size ,				encoder_layers=self.num_hidden_layers ,				decoder_layers=self.num_hidden_layers ,				encoder_attention_heads=self.num_attention_heads ,				decoder_attention_heads=self.num_attention_heads ,				encoder_ffn_dim=self.intermediate_size ,				decoder_ffn_dim=self.intermediate_size ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				eos_token_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				initializer_range=self.initializer_range ,				use_cache=lowercase_ ,				)
          __lowercase						:	Optional[int]					=				prepare_blenderbot_inputs_dict(lowercase_ ,				lowercase_ ,				lowercase_ )
          return config, inputs_dict




      def 						snake_case_	(					self						:				int ):
          __lowercase						:	Optional[int]					=				self.prepare_config_and_inputs()
          return config, inputs_dict




      def 						snake_case_	(					self						:				Dict ,				_snake_case						:				Optional[int] ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Optional[int] ):
          __lowercase						:	List[str]					=				20
          __lowercase						:	int					=				model_class_name(lowercase_ )

          __lowercase						:	Optional[int]					=				model.encode(inputs_dict['''input_ids'''] )

          __lowercase						:	Any					=				(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          __lowercase						:	Any					=				model.init_cache(decoder_input_ids.shape[0] ,				lowercase_ ,				lowercase_ )
          __lowercase						:	Tuple					=				jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,				dtype='''i4''' )

          __lowercase						:	Union[str, Any]					=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)
          __lowercase						:	int					=				model.decode(
              decoder_input_ids[:, :-1] ,				lowercase_ ,				decoder_attention_mask=lowercase_ ,				past_key_values=lowercase_ ,				decoder_position_ids=lowercase_ ,				)

          __lowercase						:	int					=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='''i4''' )
          __lowercase						:	Dict					=				model.decode(
              decoder_input_ids[:, -1:] ,				lowercase_ ,				decoder_attention_mask=lowercase_ ,				past_key_values=outputs_cache.past_key_values ,				decoder_position_ids=lowercase_ ,				)

          __lowercase						:	Optional[Any]					=				model.decode(lowercase_ ,				lowercase_ )

          __lowercase						:	Tuple					=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3 ,				msg=F'Max diff is {diff}' )




      def 						snake_case_	(					self						:				List[Any] ,				_snake_case						:				int ,				_snake_case						:				Optional[Any] ,				_snake_case						:				int ):
          __lowercase						:	List[str]					=				20
          __lowercase						:	Any					=				model_class_name(lowercase_ )

          __lowercase						:	Tuple					=				model.encode(inputs_dict['''input_ids'''] )

          __lowercase						:	List[Any]					=				(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          __lowercase						:	Optional[Any]					=				jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
              ] ,				axis=-1 ,				)

          __lowercase						:	int					=				model.init_cache(decoder_input_ids.shape[0] ,				lowercase_ ,				lowercase_ )
          __lowercase						:	List[str]					=				jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)

          __lowercase						:	List[str]					=				model.decode(
              decoder_input_ids[:, :-1] ,				lowercase_ ,				decoder_attention_mask=lowercase_ ,				past_key_values=lowercase_ ,				decoder_position_ids=lowercase_ ,				)
          __lowercase						:	Any					=				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='''i4''' )
          __lowercase						:	Dict					=				model.decode(
              decoder_input_ids[:, -1:] ,				lowercase_ ,				past_key_values=outputs_cache.past_key_values ,				decoder_attention_mask=lowercase_ ,				decoder_position_ids=lowercase_ ,				)

          __lowercase						:	Dict					=				model.decode(lowercase_ ,				lowercase_ ,				decoder_attention_mask=lowercase_ )

          __lowercase						:	Optional[Any]					=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
          self.parent.assertTrue(diff < 1E-3 ,				msg=F'Max diff is {diff}' )






@require_flax
class   __lowerCAmelCase		(		unittest.TestCase					):



      """simple docstring"""







      A__		:	Tuple         =			9_9




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Optional[Any]					=				np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ] ,				dtype=np.intaa ,				)

          __lowercase						:	Any					=				input_ids.shape[0]
          __lowercase						:	Dict					=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size ,				d_model=24 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=32 ,				decoder_ffn_dim=32 ,				max_position_embeddings=48 ,				eos_token_id=2 ,				pad_token_id=1 ,				bos_token_id=0 ,				)
          return config, input_ids, batch_size




      def 						snake_case_	(					self						:				str ):
          __lowercase						:	Tuple					=				self._get_config_and_data()
          __lowercase						:	List[str]					=				FlaxBlenderbotSmallForConditionalGeneration(lowercase_ )
          __lowercase						:	Optional[int]					=				lm_model(input_ids=lowercase_ )
          __lowercase						:	Optional[int]					=				(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape ,				lowercase_ )




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	List[str]					=				BlenderbotSmallConfig(
              vocab_size=self.vocab_size ,				d_model=14 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=8 ,				decoder_ffn_dim=8 ,				max_position_embeddings=48 ,				)
          __lowercase						:	Optional[int]					=				FlaxBlenderbotSmallForConditionalGeneration(lowercase_ )
          __lowercase						:	str					=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,				dtype=np.intaa )
          __lowercase						:	str					=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,				dtype=np.intaa )
          __lowercase						:	Tuple					=				lm_model(input_ids=lowercase_ ,				decoder_input_ids=lowercase_ )
          __lowercase						:	Tuple					=				(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['''logits'''].shape ,				lowercase_ )




      def 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	List[Any]					=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,				dtype=np.intaa )
          __lowercase						:	Dict					=				shift_tokens_right(lowercase_ ,				1 ,				2 )
          __lowercase						:	Tuple					=				np.equal(lowercase_ ,				1 ).astype(np.floataa ).sum()
          __lowercase						:	Optional[Any]					=				np.equal(lowercase_ ,				1 ).astype(np.floataa ).sum()
          self.assertEqual(shifted.shape ,				input_ids.shape )
          self.assertEqual(lowercase_ ,				n_pad_before - 1 )
          self.assertTrue(np.equal(shifted[:, 0] ,				2 ).all() )






@require_flax
class   __lowerCAmelCase		(		lowercase__       ,       unittest.TestCase       ,       lowercase__					):



      """simple docstring"""







      A__		:	str         =			True
      A__		:	Union[str, Any]         =			(
          (
              FlaxBlenderbotSmallModel,
              FlaxBlenderbotSmallForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      A__		:	List[Any]         =			(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




      def 						snake_case_	(					self						:				str ):
          __lowercase						:	Dict					=				FlaxBlenderbotSmallModelTester(self )




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Tuple					=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowercase_ ,				lowercase_ ,				lowercase_ )




      def 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	Optional[Any]					=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_ ,				lowercase_ ,				lowercase_ )




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	Any					=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  __lowercase						:	List[Any]					=				self._prepare_for_class(lowercase_ ,				lowercase_ )
                  __lowercase						:	Dict					=				model_class(lowercase_ )

                  @jax.jit
                  def encode_jitted(_snake_case						:				Any ,				_snake_case						:				Tuple=None ,				**_snake_case						:				Dict ):
                      return model.encode(input_ids=lowercase_ ,				attention_mask=lowercase_ )

                  with self.subTest('''JIT Enabled''' ):
                      __lowercase						:	List[Any]					=				encode_jitted(**lowercase_ ).to_tuple()

                  with self.subTest('''JIT Disabled''' ):
                      with jax.disable_jit():
                          __lowercase						:	Optional[Any]					=				encode_jitted(**lowercase_ ).to_tuple()

                  self.assertEqual(len(lowercase_ ) ,				len(lowercase_ ) )
                  for jitted_output, output in zip(lowercase_ ,				lowercase_ ):
                      self.assertEqual(jitted_output.shape ,				output.shape )




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	Union[str, Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                  __lowercase						:	Optional[int]					=				model_class(lowercase_ )
                  __lowercase						:	Tuple					=				model.encode(inputs_dict['''input_ids'''] ,				inputs_dict['''attention_mask'''] )

                  __lowercase						:	int					=				{
                      "decoder_input_ids": inputs_dict["decoder_input_ids"],
                      "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                      "encoder_outputs": encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(_snake_case						:				List[Any] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				List[str] ):
                      return model.decode(
                          decoder_input_ids=lowercase_ ,				decoder_attention_mask=lowercase_ ,				encoder_outputs=lowercase_ ,				)

                  with self.subTest('''JIT Enabled''' ):
                      __lowercase						:	str					=				decode_jitted(**lowercase_ ).to_tuple()

                  with self.subTest('''JIT Disabled''' ):
                      with jax.disable_jit():
                          __lowercase						:	List[Any]					=				decode_jitted(**lowercase_ ).to_tuple()

                  self.assertEqual(len(lowercase_ ) ,				len(lowercase_ ) )
                  for jitted_output, output in zip(lowercase_ ,				lowercase_ ):
                      self.assertEqual(jitted_output.shape ,				output.shape )




      @slow
      def 						snake_case_	(					self						:				List[Any] ):
          for model_class_name in self.all_model_classes:
              __lowercase						:	Optional[Any]					=				model_class_name.from_pretrained('''facebook/blenderbot_small-90M''' )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              __lowercase						:	List[str]					=				np.ones((1, 1) ) * model.config.eos_token_id
              __lowercase						:	Optional[int]					=				model(lowercase_ )
              self.assertIsNotNone(lowercase_ )




"""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()
_a		       =							logging.get_logger(__name__)







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224",		out_features=["stage1", "stage2", "stage3", "stage4"]  )
   UpperCAmelCase_					:					Dict			     =    MaskFormerConfig(backbone_config=__lowerCamelCase  )

   UpperCAmelCase_					:					int			     =    "huggingface/label-files"
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCAmelCase_					:					Dict			     =    847
      UpperCAmelCase_					:					str			     =    "maskformer-ade20k-full-id2label.json"
   elif "ade" in model_name:
      # this should be ok
      UpperCAmelCase_					:					Tuple			     =    150
      UpperCAmelCase_					:					int			     =    "ade20k-id2label.json"
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCAmelCase_					:					str			     =    171
      UpperCAmelCase_					:					Optional[int]			     =    "maskformer-coco-stuff-id2label.json"
   elif "coco" in model_name:
      # TODO
      UpperCAmelCase_					:					int			     =    133
      UpperCAmelCase_					:					Tuple			     =    "coco-panoptic-id2label.json"
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCAmelCase_					:					List[Any]			     =    19
      UpperCAmelCase_					:					Optional[int]			     =    "cityscapes-id2label.json"
   elif "vistas" in model_name:
      # this should be ok
      UpperCAmelCase_					:					Any			     =    65
      UpperCAmelCase_					:					Union[str, Any]			     =    "mapillary-vistas-id2label.json"

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

   return config







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Dict			     =    []
   # 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_					:					Union[str, Any]			     =    dct.pop(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    val







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
   for i in range(len(backbone_config.depths  )  ):
      UpperCAmelCase_					:					List[Any]			     =    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_					:					Tuple			     =    state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""  )
         UpperCAmelCase_					:					Optional[int]			     =    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_					:					Tuple			     =    in_proj_weight[:dim, :]
         UpperCAmelCase_					:					List[Any]			     =    in_proj_bias[: dim]
         UpperCAmelCase_					:					Any			     =    in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase_					:					Optional[int]			     =    in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase_					:					Tuple			     =    in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase_					:					Tuple			     =    in_proj_bias[-dim :]
         # fmt: on







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   # fmt: off
   UpperCAmelCase_					:					Dict			     =    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_					:					int			     =    state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""  )
      UpperCAmelCase_					:					int			     =    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_					:					Any			     =    in_proj_weight[: hidden_size, :]
      UpperCAmelCase_					:					int			     =    in_proj_bias[:config.hidden_size]
      UpperCAmelCase_					:					Any			     =    in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase_					:					List[Any]			     =    in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase_					:					Dict			     =    in_proj_weight[-hidden_size :, :]
      UpperCAmelCase_					:					List[Any]			     =    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_					:					str			     =    state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""  )
      UpperCAmelCase_					:					Dict			     =    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_					:					str			     =    in_proj_weight[: hidden_size, :]
      UpperCAmelCase_					:					Tuple			     =    in_proj_bias[:config.hidden_size]
      UpperCAmelCase_					:					int			     =    in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase_					:					List[str]			     =    in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase_					:					List[Any]			     =    in_proj_weight[-hidden_size :, :]
      UpperCAmelCase_					:					Optional[Any]			     =    in_proj_bias[-hidden_size :]
   # fmt: on







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_					:					Tuple			     =    Image.open(requests.get(__lowerCamelCase,		stream=__lowerCamelCase  ).raw  )
   return im







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = False  ):
   UpperCAmelCase_					:					List[str]			     =    get_maskformer_config(__lowerCamelCase  )

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

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

   # rename keys
   UpperCAmelCase_					:					int			     =    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_					:					Optional[int]			     =    torch.from_numpy(__lowerCamelCase  )

   # load 🤗 model
   UpperCAmelCase_					:					Dict			     =    MaskFormerForInstanceSegmentation(__lowerCamelCase  )
   model.eval()

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

   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    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_					:					Optional[int]			     =    prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase_					:					List[str]			     =    65
   elif "cityscapes" in model_name:
      UpperCAmelCase_					:					Tuple			     =    6_5535
   else:
      UpperCAmelCase_					:					Dict			     =    255
   UpperCAmelCase_					:					Optional[Any]			     =    True if "ade" in model_name else False
   UpperCAmelCase_					:					Dict			     =    MaskFormerImageProcessor(ignore_index=__lowerCamelCase,		reduce_labels=__lowerCamelCase  )

   UpperCAmelCase_					:					Union[str, Any]			     =    image_processor(__lowerCamelCase,		return_tensors="pt"  )

   UpperCAmelCase_					:					Dict			     =    model(**__lowerCamelCase  )

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

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase_					:					Any			     =    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__":
    _a		       =							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.'
    )

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


