



'''simple docstring'''



import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
 from transformers import LlamaTokenizerFast
except ImportError as e:
 warnings.warn(e)
 warnings.warn(
     """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
 )
 __magic_name__   :						Dict    =			None
__magic_name__   :						Optional[Any]    =			{
    """7B""": 11_008,
    """13B""": 13_824,
    """30B""": 17_920,
    """65B""": 22_016,
    """70B""": 28_672,
}
__magic_name__   :						List[Any]    =			{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=1					,	SCREAMING_SNAKE_CASE__=2_56       ):







  '''simple docstring'''



  return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3       )       ) + multiple_of - 1) // multiple_of)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  with open(a_					,	"r"       ) as f:
    return json.load(a_       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  with open(a_					,	"w"       ) as f:
    json.dump(a_					,	a_       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=True       ):







  '''simple docstring'''



  os.makedirs(a_					,	exist_ok=a_       )
  _snake_case     		=				os.path.join(a_					,	"tmp"       )
  os.makedirs(a_					,	exist_ok=a_       )

  _snake_case     		=				read_json(os.path.join(a_					,	"params.json"       )       )
  _snake_case     		=				NUM_SHARDS[model_size]
  _snake_case     		=				params['''n_layers''']
  _snake_case     		=				params['''n_heads''']
  _snake_case     		=				n_heads // num_shards
  _snake_case     		=				params['''dim''']
  _snake_case     		=				dim // n_heads
  _snake_case     		=				1_00_00.0
  _snake_case     		=				1.0 / (base ** (torch.arange(0					,	a_					,	2       ).float() / dims_per_head))

  if "n_kv_heads" in params:
    _snake_case     		=				params['''n_kv_heads''']  # for GQA / MQA
    _snake_case     		=				n_heads_per_shard // num_key_value_heads
    _snake_case     		=				dim // num_key_value_heads
  else:  # compatibility with other checkpoints
    _snake_case     		=				n_heads
    _snake_case     		=				n_heads_per_shard
    _snake_case     		=				dim

  # permute for sliced rotary
  def permute(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=n_heads					,	SCREAMING_SNAKE_CASE__=dim					,	SCREAMING_SNAKE_CASE__=dim       ):
    return w.view(a_					,	dima // n_heads // 2					,	2					,	a_       ).transpose(1					,	2       ).reshape(a_					,	a_       )

  print(f'''Fetching all parameters from the checkpoint at {input_base_path}.'''       )
  # Load weights
  if model_size == "7B":
    # Not sharded
    # (The sharded implementation would also work, but this is simpler.)
    _snake_case     		=				torch.load(os.path.join(a_					,	"consolidated.00.pth"       )					,	map_location="cpu"       )
  else:
    # Sharded
    _snake_case     		=				[
        torch.load(os.path.join(a_					,	f'''consolidated.{i:02d}.pth'''       )					,	map_location="cpu"       )
        for i in range(a_       )
    ]
  _snake_case     		=				0
  _snake_case     		=				{'''weight_map''': {}}
  for layer_i in range(a_       ):
    _snake_case     		=				f'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
    if model_size == "7B":
      # Unsharded
      _snake_case     		=				{
          f'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
              loaded[f'''layers.{layer_i}.attention.wq.weight''']       ),
          f'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
              loaded[f'''layers.{layer_i}.attention.wk.weight''']       ),
          f'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[f'''layers.{layer_i}.attention.wv.weight'''],
          f'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[f'''layers.{layer_i}.attention.wo.weight'''],
          f'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w1.weight'''],
          f'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w2.weight'''],
          f'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[f'''layers.{layer_i}.feed_forward.w3.weight'''],
          f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[f'''layers.{layer_i}.attention_norm.weight'''],
          f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[f'''layers.{layer_i}.ffn_norm.weight'''],
      }
    else:
      # Sharded
      # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
      # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
      # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

      _snake_case     		=				{
          f'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
              f'''layers.{layer_i}.attention_norm.weight'''
          ].clone(),
          f'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
              f'''layers.{layer_i}.ffn_norm.weight'''
          ].clone(),
      }
      _snake_case     		=				permute(
          torch.cat(
              [
                  loaded[i][f'''layers.{layer_i}.attention.wq.weight'''].view(a_					,	a_					,	a_       )
                  for i in range(a_       )
              ]					,	dim=0					,	).reshape(a_					,	a_       )       )
      _snake_case     		=				permute(
          torch.cat(
              [
                  loaded[i][f'''layers.{layer_i}.attention.wk.weight'''].view(
                      a_					,	a_					,	a_       )
                  for i in range(a_       )
              ]					,	dim=0					,	).reshape(a_					,	a_       )					,	a_					,	a_					,	a_					,	)
      _snake_case     		=				torch.cat(
          [
              loaded[i][f'''layers.{layer_i}.attention.wv.weight'''].view(
                  a_					,	a_					,	a_       )
              for i in range(a_       )
          ]					,	dim=0					,	).reshape(a_					,	a_       )

      _snake_case     		=				torch.cat(
          [loaded[i][f'''layers.{layer_i}.attention.wo.weight'''] for i in range(a_       )]					,	dim=1       )
      _snake_case     		=				torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(a_       )]					,	dim=0       )
      _snake_case     		=				torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(a_       )]					,	dim=1       )
      _snake_case     		=				torch.cat(
          [loaded[i][f'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(a_       )]					,	dim=0       )

    _snake_case     		=				inv_freq
    for k, v in state_dict.items():
      _snake_case     		=				filename
      param_count += v.numel()
    torch.save(a_					,	os.path.join(a_					,	a_       )       )

  _snake_case     		=				f'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
  if model_size == "7B":
    # Unsharded
    _snake_case     		=				{
        '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
        '''model.norm.weight''': loaded['''norm.weight'''],
        '''lm_head.weight''': loaded['''output.weight'''],
    }
  else:
    _snake_case     		=				{
        '''model.norm.weight''': loaded[0]['''norm.weight'''],
        '''model.embed_tokens.weight''': torch.cat(
            [loaded[i]["tok_embeddings.weight"] for i in range(a_       )]					,	dim=1       ),
        '''lm_head.weight''': torch.cat([loaded[i]["output.weight"] for i in range(a_       )]					,	dim=0       ),
    }

  for k, v in state_dict.items():
    _snake_case     		=				filename
    param_count += v.numel()
  torch.save(a_					,	os.path.join(a_					,	a_       )       )

  # Write configs
  _snake_case     		=				{'''total_size''': param_count * 2}
  write_json(a_					,	os.path.join(a_					,	"pytorch_model.bin.index.json"       )       )
  _snake_case     		=				params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
  _snake_case     		=				params['''multiple_of'''] if '''multiple_of''' in params else 2_56
  _snake_case     		=				LlamaConfig(
      hidden_size=a_					,	intermediate_size=compute_intermediate_size(a_					,	a_					,	a_       )					,	num_attention_heads=params["n_heads"]					,	num_hidden_layers=params["n_layers"]					,	rms_norm_eps=params["norm_eps"]					,	num_key_value_heads=a_					,	)
  config.save_pretrained(a_       )

  # Make space so we can load the model properly now.
  del state_dict
  del loaded
  gc.collect()

  print("Loading the checkpoint in a Llama model."       )
  _snake_case     		=				LlamaForCausalLM.from_pretrained(a_					,	torch_dtype=torch.floataa					,	low_cpu_mem_usage=a_       )
  # Avoid saving this as part of the config.
  del model.config._name_or_path

  print("Saving in the Transformers format."       )
  model.save_pretrained(a_					,	safe_serialization=a_       )
  shutil.rmtree(a_       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
  print(f'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''       )
  _snake_case     		=				tokenizer_class(a_       )
  tokenizer.save_pretrained(a_       )




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				argparse.ArgumentParser()
  parser.add_argument(
      "--input_dir"					,	help="Location of LLaMA weights, which contains tokenizer.model and model folders"					,	)
  parser.add_argument(
      "--model_size"					,	choices=["7B", "7Bf", "13B", "13Bf", "30B", "65B", "70B", "70Bf", "tokenizer_only"]					,	)
  parser.add_argument(
      "--output_dir"					,	help="Location to write HF model and tokenizer"					,	)
  parser.add_argument("--safe_serialization"					,	type=a_					,	help="Whether or not to save using `safetensors`."       )
  _snake_case     		=				parser.parse_args()
  if args.model_size != "tokenizer_only":
    write_model(
        model_path=args.output_dir					,	input_base_path=os.path.join(args.input_dir					,	args.model_size       )					,	model_size=args.model_size					,	safe_serialization=args.safe_serialization					,	)
  _snake_case     		=				os.path.join(args.input_dir					,	"tokenizer.model"       )
  write_tokenizer(args.output_dir					,	a_       )


if __name__ == "__main__":
 main()









'''simple docstring'''



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

 if is_vision_available():
  from transformers import OneFormerImageProcessor
  from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
  from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
 from PIL import Image




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__="shi-labs/oneformer_demo"       ):







  '''simple docstring'''



  with open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       ) as f:
    _snake_case     		=				json.load(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				{}
  _snake_case     		=				[]
  _snake_case     		=				[]
  for key, info in class_info.items():
    _snake_case     		=				info["name"]
    class_names.append(info["name"]       )
    if info["isthing"]:
      thing_ids.append(int(SCREAMING_SNAKE_CASE__       )       )
  _snake_case     		=				thing_ids
  _snake_case     		=				class_names
  return metadata


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=10						,   lowerCamelCase=False						,   lowerCamelCase=255						,   lowerCamelCase="shi-labs/oneformer_demo"						,   lowerCamelCase="ade20k_panoptic.json"						,   lowerCamelCase=10						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				{"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				class_info_file
       _snake_case     		=				prepare_metadata(lowerCamelCase						,   lowerCamelCase	)
       _snake_case     		=				num_text
       _snake_case     		=				repo_path

       # for the post_process_functions
       _snake_case     		=				2
       _snake_case     		=				10
       _snake_case     		=				10
       _snake_case     		=				3
       _snake_case     		=				4
       _snake_case     		=				num_labels
       _snake_case     		=				do_reduce_labels
       _snake_case     		=				ignore_index

     def 	UpperCamelCase(					self	):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "num_labels": self.num_labels,
           "do_reduce_labels": self.do_reduce_labels,
           "ignore_index": self.ignore_index,
           "class_info_file": self.class_info_file,
           "metadata": self.metadata,
           "num_text": self.num_text,
       }

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False	):
       if not batched:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   Image.Image	):
           _snake_case		,		_snake_case     		=				image.size
         else:
           _snake_case		,		_snake_case     		=				image.shape[1], image.shape[2]
         if w < h:
           _snake_case     		=				int(self.size["shortest_edge"] * h / w	)
           _snake_case     		=				self.size["shortest_edge"]
         elif w > h:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				int(self.size["shortest_edge"] * w / h	)
         else:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				self.size["shortest_edge"]

       else:
         _snake_case     		=				[]
         for image in image_inputs:
           _snake_case		,		_snake_case     		=				self.get_expected_values([image]	)
           expected_values.append((expected_height, expected_width)	)
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[0]	)[0]
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[1]	)[1]

       return expected_height, expected_width






     def 	UpperCamelCase(					self	):
       return OneFormerForUniversalSegmentationOutput(
           # +1 for null class
           class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)	)						,   masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)	)						,   )







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     UpperCAmelCase__       :						Tuple      =   image_processing_class

     def 	UpperCamelCase(					self	):
       _snake_case     		=				OneFormerImageProcessorTester(self	)

     @property
     def 	UpperCamelCase(					self	):
       return self.image_processing_tester.prepare_image_processor_dict()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_mean"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_std"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_normalize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_resize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "ignore_index"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "class_info_file"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "num_text"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "repo_path"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "metadata"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_reduce_labels"	)	)

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				prepare_image_inputs(self.image_processing_tester						,   equal_resolution=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   Image.Image	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   ["semantic"]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processor(
           lowerCamelCase						,   ["semantic"] * len(lowerCamelCase	)						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random numpy tensors
       _snake_case     		=				prepare_image_inputs(self.image_processing_tester						,   equal_resolution=lowerCamelCase						,   numpify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   np.ndarray	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   ["semantic"]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processor(
           lowerCamelCase						,   ["semantic"] * len(lowerCamelCase	)						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     def 	UpperCamelCase(					self	):
       # Initialize image_processor
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				prepare_image_inputs(self.image_processing_tester						,   equal_resolution=lowerCamelCase						,   torchify=lowerCamelCase	)
       for image in image_inputs:
         self.assertIsInstance(lowerCamelCase						,   torch.Tensor	)

       # Test not batched input
       _snake_case     		=				image_processor(image_inputs[0]						,   ["semantic"]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processing_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processing_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processor(
           lowerCamelCase						,   ["semantic"] * len(lowerCamelCase	)						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase="np"	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # prepare image and target
       _snake_case     		=				self.image_processing_tester.num_labels
       _snake_case     		=				None
       _snake_case     		=				None
       _snake_case     		=				prepare_image_inputs(self.image_processing_tester						,   equal_resolution=lowerCamelCase	)
       if with_segmentation_maps:
         _snake_case     		=				num_labels
         if is_instance_map:
           _snake_case     		=				list(range(lowerCamelCase	)	) * 2
           _snake_case     		=				dict(enumerate(lowerCamelCase	)	)
         _snake_case     		=				[
             np.random.randint(0						,   high * 2						,   (img.size[1], img.size[0])	).astype(np.uinta	) for img in image_inputs
         ]
         if segmentation_type == "pil":
           _snake_case     		=				[Image.fromarray(lowerCamelCase	) for annotation in annotations]

       _snake_case     		=				image_processor(
           lowerCamelCase						,   ["semantic"] * len(lowerCamelCase	)						,   lowerCamelCase						,   return_tensors="pt"						,   instance_id_to_semantic_id=lowerCamelCase						,   pad_and_return_pixel_mask=lowerCamelCase						,   )

       return inputs

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       def common(lowerCamelCase=False						,   lowerCamelCase=None	):
         _snake_case     		=				self.comm_get_image_processor_inputs(
             with_segmentation_maps=lowerCamelCase						,   is_instance_map=lowerCamelCase						,   segmentation_type=lowerCamelCase	)

         _snake_case     		=				inputs["mask_labels"]
         _snake_case     		=				inputs["class_labels"]
         _snake_case     		=				inputs["pixel_values"]
         _snake_case     		=				inputs["text_inputs"]

         # check the batch_size
         for mask_label, class_label, text_input in zip(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
           self.assertEqual(mask_label.shape[0]						,   class_label.shape[0]	)
           # this ensure padding has happened
           self.assertEqual(mask_label.shape[1:]						,   pixel_values.shape[2:]	)
           self.assertEqual(len(lowerCamelCase	)						,   self.image_processing_tester.num_text	)

       common()
       common(is_instance_map=lowerCamelCase	)
       common(is_instance_map=lowerCamelCase						,   segmentation_type="pil"	)
       common(is_instance_map=lowerCamelCase						,   segmentation_type="pil"	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				np.zeros((20, 50)	)
       _snake_case     		=				1
       _snake_case     		=				1
       _snake_case     		=				1

       _snake_case     		=				binary_mask_to_rle(lowerCamelCase	)
       self.assertEqual(len(lowerCamelCase	)						,   4	)
       self.assertEqual(rle[0]						,   21	)
       self.assertEqual(rle[1]						,   45	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,   max_seq_length=77						,   task_seq_length=77						,   class_info_file="ade20k_panoptic.json"						,   num_text=self.image_processing_tester.num_text						,   repo_path="shi-labs/oneformer_demo"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()

       _snake_case     		=				fature_extractor.post_process_semantic_segmentation(lowerCamelCase	)

       self.assertEqual(len(lowerCamelCase	)						,   self.image_processing_tester.batch_size	)
       self.assertEqual(
           segmentation[0].shape						,   (
               self.image_processing_tester.height,
               self.image_processing_tester.width,
           )						,   )

       _snake_case     		=				[(1, 4) for i in range(self.image_processing_tester.batch_size	)]
       _snake_case     		=				fature_extractor.post_process_semantic_segmentation(lowerCamelCase						,   target_sizes=lowerCamelCase	)

       self.assertEqual(segmentation[0].shape						,   target_sizes[0]	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,   max_seq_length=77						,   task_seq_length=77						,   class_info_file="ade20k_panoptic.json"						,   num_text=self.image_processing_tester.num_text						,   repo_path="shi-labs/oneformer_demo"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()
       _snake_case     		=				image_processor.post_process_instance_segmentation(lowerCamelCase						,   threshold=0	)

       self.assertTrue(len(lowerCamelCase	) == self.image_processing_tester.batch_size	)
       for el in segmentation:
         self.assertTrue("segmentation" in el	)
         self.assertTrue("segments_info" in el	)
         self.assertEqual(type(el["segments_info"]	)						,   lowerCamelCase	)
         self.assertEqual(
             el["segmentation"].shape						,   (self.image_processing_tester.height, self.image_processing_tester.width)	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes						,   max_seq_length=77						,   task_seq_length=77						,   class_info_file="ade20k_panoptic.json"						,   num_text=self.image_processing_tester.num_text						,   repo_path="shi-labs/oneformer_demo"						,   )
       _snake_case     		=				self.image_processing_tester.get_fake_oneformer_outputs()
       _snake_case     		=				image_processor.post_process_panoptic_segmentation(lowerCamelCase						,   threshold=0	)

       self.assertTrue(len(lowerCamelCase	) == self.image_processing_tester.batch_size	)
       for el in segmentation:
         self.assertTrue("segmentation" in el	)
         self.assertTrue("segments_info" in el	)
         self.assertEqual(type(el["segments_info"]	)						,   lowerCamelCase	)
         self.assertEqual(
             el["segmentation"].shape						,   (self.image_processing_tester.height, self.image_processing_tester.width)	)


