

'''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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = SwinConfig.from_pretrained(
           """microsoft/swin-tiny-patch4-window7-224""",    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]			)
       A_		   = MaskFormerConfig(backbone_config=UpperCAmelCase__			)

       A_		   = """huggingface/label-files"""
       if "ade20k-full" in model_name:
              # this should be ok
              A_		   = 8_47
              A_		   = """maskformer-ade20k-full-id2label.json"""
       elif "ade" in model_name:
              # this should be ok
              A_		   = 1_50
              A_		   = """ade20k-id2label.json"""
       elif "coco-stuff" in model_name:
              # this should be ok
              A_		   = 1_71
              A_		   = """maskformer-coco-stuff-id2label.json"""
       elif "coco" in model_name:
              # TODO
              A_		   = 1_33
              A_		   = """coco-panoptic-id2label.json"""
       elif "cityscapes" in model_name:
              # this should be ok
              A_		   = 19
              A_		   = """cityscapes-id2label.json"""
       elif "vistas" in model_name:
              # this should be ok
              A_		   = 65
              A_		   = """mapillary-vistas-id2label.json"""

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

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

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

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

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

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

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

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

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

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

       return rename_keys
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     List[str]:
       A_		   = dct.pop(UpperCAmelCase__			)
       A_		   = val
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     Dict:
       A_		   = [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
       for i in range(len(backbone_config.depths			)			):
              A_		   = 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)
                     A_		   = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
                     A_		   = 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
                     A_		   = in_proj_weight[:dim, :]
                     A_		   = in_proj_bias[: dim]
                     A_		   = in_proj_weight[
                         dim : dim * 2, :
                     ]
                     A_		   = in_proj_bias[
                         dim : dim * 2
                     ]
                     A_		   = in_proj_weight[
                         -dim :, :
                     ]
                     A_		   = in_proj_bias[-dim :]
                     # fmt: on
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     int:
       # fmt: off
       A_		   = 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)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
              A_		   = 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
              A_		   = in_proj_weight[: hidden_size, :]
              A_		   = in_proj_bias[:config.hidden_size]
              A_		   = in_proj_weight[hidden_size : hidden_size * 2, :]
              A_		   = in_proj_bias[hidden_size : hidden_size * 2]
              A_		   = in_proj_weight[-hidden_size :, :]
              A_		   = 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)
              A_		   = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
              A_		   = 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
              A_		   = in_proj_weight[: hidden_size, :]
              A_		   = in_proj_bias[:config.hidden_size]
              A_		   = in_proj_weight[hidden_size : hidden_size * 2, :]
              A_		   = in_proj_bias[hidden_size : hidden_size * 2]
              A_		   = in_proj_weight[-hidden_size :, :]
              A_		   = in_proj_bias[-hidden_size :]
       # fmt: on
def  UpperCAmelCase__      ( )     ->     torch.Tensor:
       A_		   = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		   = Image.open(requests.get(UpperCAmelCase__,    stream=UpperCAmelCase__			).raw			)
       return im
@torch.no_grad()
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__ = False			)     ->     Any:
       A_		   = get_maskformer_config(UpperCAmelCase__			)

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

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

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

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

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

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

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

       # verify results
       A_		   = prepare_img()
       if "vistas" in model_name:
              A_		   = 65
       elif "cityscapes" in model_name:
              A_		   = 6_55_35
       else:
              A_		   = 2_55
       A_		   = True if """ade""" in model_name else False
       A_		   = MaskFormerImageProcessor(ignore_index=UpperCAmelCase__,    reduce_labels=UpperCAmelCase__			)

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

       A_		   = model(**UpperCAmelCase__			)

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

       if model_name == "maskformer-swin-tiny-ade":
              A_		   = torch.tensor(
                  [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]			)
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3],    UpperCAmelCase__,    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(UpperCAmelCase__			).mkdir(exist_ok=UpperCAmelCase__			)
              model.save_pretrained(UpperCAmelCase__			)
              image_processor.save_pretrained(UpperCAmelCase__			)

       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
      )




'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = tempfile.mkdtemp()

           # fmt: off
           A_		   = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
           # fmt: on
           A_		   = dict(zip(UpperCamelCase__						,			range(len(UpperCamelCase__    )    )    )    )
           A_		   = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
           A_		   = {"""unk_token""": """<unk>"""}

           A_		   = os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]    )
           A_		   = os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""merges_file"""]    )
           with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(UpperCamelCase__    ) + """\n"""    )
           with open(self.merges_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(UpperCamelCase__    )    )

           A_		   = {
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48145466, 0.4578275, 0.40821073],
               """image_std""": [0.26862954, 0.26130258, 0.27577711],
           }
           A_		   = os.path.join(self.tmpdirname						,			UpperCamelCase__    )
           with open(self.image_processor_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  json.dump(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return CLIPTokenizer.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           return CLIPImageProcessor.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



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





           '''simple docstring'''
           shutil.rmtree(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = [np.random.randint(255						,			size=(3, 30, 400)						,			dtype=np.uinta    )]

           A_		   = [Image.fromarray(np.moveaxis(UpperCamelCase__						,			0						,			-1    )    ) for x in image_inputs]

           return image_inputs



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()
           A_		   = self.get_image_processor()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_slow.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.from_pretrained(self.tmpdirname						,			use_fast=UpperCamelCase__    )

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_fast.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.from_pretrained(self.tmpdirname    )

           self.assertEqual(processor_slow.tokenizer.get_vocab()						,			tokenizer_slow.get_vocab()    )
           self.assertEqual(processor_fast.tokenizer.get_vocab()						,			tokenizer_fast.get_vocab()    )
           self.assertEqual(tokenizer_slow.get_vocab()						,			tokenizer_fast.get_vocab()    )
           self.assertIsInstance(processor_slow.tokenizer						,			UpperCamelCase__    )
           self.assertIsInstance(processor_fast.tokenizer						,			UpperCamelCase__    )

           self.assertEqual(processor_slow.image_processor.to_json_string()						,			image_processor.to_json_string()    )
           self.assertEqual(processor_fast.image_processor.to_json_string()						,			image_processor.to_json_string()    )
           self.assertIsInstance(processor_slow.image_processor						,			UpperCamelCase__    )
           self.assertIsInstance(processor_fast.image_processor						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = CLIPProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()    )
           processor.save_pretrained(self.tmpdirname    )

           A_		   = self.get_tokenizer(bos_token="""(BOS)"""						,			eos_token="""(EOS)"""    )
           A_		   = self.get_image_processor(do_normalize=UpperCamelCase__						,			padding_value=1.0    )

           A_		   = CLIPProcessor.from_pretrained(
               self.tmpdirname						,			bos_token="""(BOS)"""						,			eos_token="""(EOS)"""						,			do_normalize=UpperCamelCase__						,			padding_value=1.0    )

           self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()    )
           self.assertIsInstance(processor.tokenizer						,			UpperCamelCase__    )

           self.assertEqual(processor.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()    )
           self.assertIsInstance(processor.image_processor						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = self.prepare_image_inputs()

           A_		   = image_processor(UpperCamelCase__						,			return_tensors="""np"""    )
           A_		   = processor(images=UpperCamelCase__						,			return_tensors="""np"""    )

           for key in input_image_proc.keys():
                  self.assertAlmostEqual(input_image_proc[key].sum()						,			input_processor[key].sum()						,			delta=1e-2    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""

           A_		   = processor(text=UpperCamelCase__    )

           A_		   = tokenizer(UpperCamelCase__    )

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""
           A_		   = self.prepare_image_inputs()

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

           self.assertListEqual(list(inputs.keys()    )						,			["""input_ids""", """attention_mask""", """pixel_values"""]    )

           # test if it raises when no input is passed
           with pytest.raises(UpperCamelCase__    ):
                  processor()



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





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           A_		   = processor.batch_decode(UpperCamelCase__    )
           A_		   = tokenizer.batch_decode(UpperCamelCase__    )

           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )







    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""
           A_		   = self.prepare_image_inputs()

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

           self.assertListEqual(list(inputs.keys()    )						,			processor.model_input_names    )
