



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()
SCREAMING_SNAKE_CASE_:int             =						logging.get_logger(__name__)




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






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

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

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

    return config




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






    A :       Any          =   []
    # 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	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[Any]:



    """simple docstring"""






    A :       str          =   dct.pop(__a		)
    A :       List[Any]          =   val




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    A :       Dict          =   [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
    for i in range(len(backbone_config.depths		)		):
        A :       Optional[int]          =   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 :       Dict          =   state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
            A :       List[Any]          =   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 :       List[str]          =   in_proj_weight[:dim, :]
            A :       int          =   in_proj_bias[: dim]
            A :       int          =   in_proj_weight[
                dim : dim * 2, :
            ]
            A :       Any          =   in_proj_bias[
                dim : dim * 2
            ]
            A :       Optional[int]          =   in_proj_weight[
                -dim :, :
            ]
            A :       str          =   in_proj_bias[-dim :]
            # fmt: on




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[Any]:



    """simple docstring"""






    A :       int          =   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 :       List[Any]          =   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''		)
        A :       Dict          =   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 :       Union[str, Any]          =   in_proj_weight[: hidden_size, :]
        A :       List[Any]          =   in_proj_bias[:config.hidden_size]
        A :       Union[str, Any]          =   in_proj_weight[hidden_size : hidden_size * 2, :]
        A :       Dict          =   in_proj_bias[hidden_size : hidden_size * 2]
        A :       Dict          =   in_proj_weight[-hidden_size :, :]
        A :       Union[str, 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)
        A :       str          =   state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''		)
        A :       Optional[int]          =   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 :       Any          =   in_proj_weight[: hidden_size, :]
        A :       List[Any]          =   in_proj_bias[:config.hidden_size]
        A :       Any          =   in_proj_weight[hidden_size : hidden_size * 2, :]
        A :       List[Any]          =   in_proj_bias[hidden_size : hidden_size * 2]
        A :       Optional[Any]          =   in_proj_weight[-hidden_size :, :]
        A :       Any          =   in_proj_bias[-hidden_size :]
    # fmt: on




def 	__UpperCamelCase	(  )       ->  torch.Tensor:



    """simple docstring"""






    A :       Any          =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A :       Optional[Any]          =   Image.open(requests.get(__a      ,     stream=__a		).raw		)
    return im




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = False		)       ->  Union[str, Any]:



    """simple docstring"""






    A :       int          =   get_maskformer_config(__a		)

    # load original state_dict
    with open(__a      ,     """rb"""		) as f:
        A :       Any          =   pickle.load(__a		)
    A :       Optional[int]          =   data['''model''']

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

    # rename keys
    A :       Union[str, Any]          =   create_rename_keys(__a		)
    for src, dest in rename_keys:
        rename_key(__a      ,     __a      ,     __a		)
    read_in_swin_q_k_v(__a      ,     config.backbone_config		)
    read_in_decoder_q_k_v(__a      ,     __a		)

    # update to torch tensors
    for key, value in state_dict.items():
        A :       Optional[int]          =   torch.from_numpy(__a		)

    # load 🤗 model
    A :       Dict          =   MaskFormerForInstanceSegmentation(__a		)
    model.eval()

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

    A :       List[str]          =   model.load_state_dict(__a      ,     strict=__a		)
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(__a		) == 0, f'''Unexpected keys: {unexpected_keys}'''

    # verify results
    A :       List[Any]          =   prepare_img()
    if "vistas" in model_name:
        A :       Tuple          =   65
    elif "cityscapes" in model_name:
        A :       Optional[int]          =   6_5535
    else:
        A :       int          =   255
    A :       Any          =   True if '''ade''' in model_name else False
    A :       Optional[int]          =   MaskFormerImageProcessor(ignore_index=__a      ,     reduce_labels=__a		)

    A :       int          =   image_processor(__a      ,     return_tensors="""pt"""		)

    A :       Optional[Any]          =   model(**__a		)

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

    if model_name == "maskformer-swin-tiny-ade":
        A :       int          =   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]      ,     __a      ,     atol=1e-4		)
    print("""Looks ok!"""		)

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

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[int]             =						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."""
     )

     SCREAMING_SNAKE_CASE_:Dict             =						parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )










"""simple docstring"""






import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      @parameterized.expand([(None,), ('''foo.json''',)])
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		GenerationConfig(
              do_sample=lowercase_						,	temperature=0.7						,	length_penalty=1.0						,	bad_words_ids=[[1, 2, 3], [4, 5]]						,	)
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(lowercase_						,	config_name=lowercase_)
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		GenerationConfig.from_pretrained(lowercase_						,	config_name=lowercase_)

          # Checks parameters that were specified
          self.assertEqual(loaded_config.do_sample						,	lowercase_)
          self.assertEqual(loaded_config.temperature						,	0.7)
          self.assertEqual(loaded_config.length_penalty						,	1.0)
          self.assertEqual(loaded_config.bad_words_ids						,	[[1, 2, 3], [4, 5]])

          # Checks parameters that were not specified (defaults)
          self.assertEqual(loaded_config.top_k						,	50)
          self.assertEqual(loaded_config.max_length						,	20)
          self.assertEqual(loaded_config.max_time						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		AutoConfig.from_pretrained('''gpt2''')
          SCREAMING_SNAKE_CASE_      :  int    						=		GenerationConfig.from_model_config(lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		GenerationConfig()

          # The generation config has loaded a few non-default parameters from the model config
          self.assertNotEqual(lowercase_						,	lowercase_)

          # One of those parameters is eos_token_id -- check if it matches
          self.assertNotEqual(generation_config_from_model.eos_token_id						,	default_generation_config.eos_token_id)
          self.assertEqual(generation_config_from_model.eos_token_id						,	model_config.eos_token_id)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		GenerationConfig()
          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''max_new_tokens''': 1024,
              '''foo''': '''bar''',
          }
          SCREAMING_SNAKE_CASE_      :  str    						=		copy.deepcopy(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		generation_config.update(**lowercase_)

          # update_kwargs was not modified (no side effects)
          self.assertEqual(lowercase_						,	lowercase_)

          # update_kwargs was used to update the config on valid attributes
          self.assertEqual(generation_config.max_new_tokens						,	1024)

          # `.update()` returns a dictionary of unused kwargs
          self.assertEqual(lowercase_						,	{'''foo''': '''bar'''})
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GenerationConfig()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		'''bar'''

          with tempfile.TemporaryDirectory('''test-generation-config''') as tmp_dir:
              generation_config.save_pretrained(lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		GenerationConfig.from_pretrained(lowercase_)
          # update_kwargs was used to update the config on valid attributes
          self.assertEqual(new_config.foo						,	'''bar''')

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GenerationConfig.from_model_config(lowercase_)
          assert not hasattr(lowercase_						,	'''foo''')  # no new kwargs should be initialized if from config



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		GenerationConfig()
          self.assertEqual(default_config.temperature						,	1.0)
          self.assertEqual(default_config.do_sample						,	lowercase_)
          self.assertEqual(default_config.num_beams						,	1)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		GenerationConfig(
              do_sample=lowercase_						,	temperature=0.7						,	length_penalty=1.0						,	bad_words_ids=[[1, 2, 3], [4, 5]]						,	)
          self.assertEqual(config.temperature						,	0.7)
          self.assertEqual(config.do_sample						,	lowercase_)
          self.assertEqual(config.num_beams						,	1)

          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(lowercase_)
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		GenerationConfig.from_pretrained(lowercase_						,	temperature=1.0)

          self.assertEqual(loaded_config.temperature						,	1.0)
          self.assertEqual(loaded_config.do_sample						,	lowercase_)
          self.assertEqual(loaded_config.num_beams						,	1)  # default value




@is_staging_test
class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		TOKEN
          HfFolder.save_token(lowercase_)
      @classmethod
      def _SCREAMING_SNAKE_CASE (						cls     :  List[str]):




          '''simple docstring'''


          try:
              delete_repo(token=cls._token						,	repo_id='''test-generation-config''')
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token						,	repo_id='''valid_org/test-generation-config-org''')
          except HTTPError:
              pass
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		GenerationConfig(
              do_sample=lowercase_						,	temperature=0.7						,	length_penalty=1.0						,	)
          config.push_to_hub('''test-generation-config'''						,	use_auth_token=self._token)

          SCREAMING_SNAKE_CASE_      :  int    						=		GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(lowercase_						,	getattr(lowercase_						,	lowercase_))

        # Reset repo
          delete_repo(token=self._token						,	repo_id='''test-generation-config''')

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  lowercase_						,	repo_id='''test-generation-config'''						,	push_to_hub=lowercase_						,	use_auth_token=self._token)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(lowercase_						,	getattr(lowercase_						,	lowercase_))



      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		GenerationConfig(
              do_sample=lowercase_						,	temperature=0.7						,	length_penalty=1.0						,	)
          config.push_to_hub('''valid_org/test-generation-config-org'''						,	use_auth_token=self._token)

          SCREAMING_SNAKE_CASE_      :  Any    						=		GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(lowercase_						,	getattr(lowercase_						,	lowercase_))

        # Reset repo
          delete_repo(token=self._token						,	repo_id='''valid_org/test-generation-config-org''')

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  lowercase_						,	repo_id='''valid_org/test-generation-config-org'''						,	push_to_hub=lowercase_						,	use_auth_token=self._token)

          SCREAMING_SNAKE_CASE_      :  Any    						=		GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(lowercase_						,	getattr(lowercase_						,	lowercase_))


