

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image


UpperCAmelCase__ :     Dict				    =    ["""text""", """image""", """audio"""]






def 					__lowercase      (     _A			)     ->						Dict:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   []

      for input_type in input_types:
            if input_type == "text":
                  inputs.append("""Text input"""			)
            elif input_type == "image":
                  inputs.append(
                      Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""			)			) / """000000039769.png"""			).resize((512, 512)			)			)
            elif input_type == "audio":
                  inputs.append(torch.ones(3000			)			)
            elif isinstance(_A						,    _A			):
                  inputs.append(create_inputs(_A			)			)
            else:
                  raise ValueError(F"Invalid type requested: {input_type}"			)

      return inputs






def 					__lowercase      (     _A			)     ->						Optional[int]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      List[Any]       =   []

      for output in outputs:
            if isinstance(_A						,    (str, AgentText)			):
                  output_types.append("""text"""			)
            elif isinstance(_A						,    (Image.Image, AgentImage)			):
                  output_types.append("""image"""			)
            elif isinstance(_A						,    (torch.Tensor, AgentAudio)			):
                  output_types.append("""audio"""			)
            else:
                  raise ValueError(F"Invalid output: {output}"			)

      return output_types







@is_tool_test
class        a__       :







  """simple docstring"""




  def  _lowercase      (			self    : Union[str, Any]		)					->List[Any]:
        """simple docstring"""







        self.assertTrue(hasattr(self.tool				,				"""inputs"""		)		)
        self.assertTrue(hasattr(self.tool				,				"""outputs"""		)		)

        SCREAMING_SNAKE_CASE :      int       =   self.tool.inputs
        for _input in inputs:
              if isinstance(_input				,				UpperCAmelCase__		):
                    for __input in _input:
                          self.assertTrue(__input in authorized_types		)
              else:
                    self.assertTrue(_input in authorized_types		)

        SCREAMING_SNAKE_CASE :      int       =   self.tool.outputs
        for _output in outputs:
              self.assertTrue(_output in authorized_types		)


  def  _lowercase      (			self    : Optional[int]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   create_inputs(self.tool.inputs		)
        SCREAMING_SNAKE_CASE :      Any       =   self.tool(*UpperCAmelCase__		)

        # There is a single output
        if len(self.tool.outputs		) == 1:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [outputs]

        self.assertListEqual(output_types(UpperCAmelCase__		)				,				self.tool.outputs		)


  def  _lowercase      (			self    : str		)					->Tuple:
        """simple docstring"""







        self.assertTrue(hasattr(self.tool				,				"""description"""		)		)
        self.assertTrue(hasattr(self.tool				,				"""default_checkpoint"""		)		)
        self.assertTrue(self.tool.description.startswith("""This is a tool that"""		)		)


  def  _lowercase      (			self    : Dict		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   create_inputs(self.tool.inputs		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   self.tool(*UpperCAmelCase__		)

        if not isinstance(UpperCAmelCase__				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      List[str]       =   [outputs]

        self.assertEqual(len(UpperCAmelCase__		)				,				len(self.tool.outputs		)		)

        for output, output_type in zip(UpperCAmelCase__				,				self.tool.outputs		):
              SCREAMING_SNAKE_CASE :      List[Any]       =   AGENT_TYPE_MAPPING[output_type]
              self.assertTrue(isinstance(UpperCAmelCase__				,				UpperCAmelCase__		)		)




  def  _lowercase      (			self    : Union[str, Any]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   create_inputs(self.tool.inputs		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   []

        for _input, input_type in zip(UpperCAmelCase__				,				self.tool.inputs		):
              if isinstance(UpperCAmelCase__				,				UpperCAmelCase__		):
                    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
              else:
                    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.tool(*UpperCAmelCase__		)

        if not isinstance(UpperCAmelCase__				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   [outputs]

        self.assertEqual(len(UpperCAmelCase__		)				,				len(self.tool.outputs		)		)







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




