

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


snake_case_        =			['''text''', '''image''', '''audio''']

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]      ):



     '''simple docstring'''





     lowercase__   :       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(3_000      )      )
          elif isinstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ):
               inputs.append(create_inputs(SCREAMING_SNAKE_CASE_      )      )
          else:
               raise ValueError(f"""Invalid type requested: {input_type}"""      )

     return inputs

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List      ):



     '''simple docstring'''





     lowercase__   :       str				=	[]

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

     return output_types


@is_tool_test
class 				SCREAMING_SNAKE_CASE__ :





    def 				snake_case_    (				self):
         self.assertTrue(hasattr(self.tool						,       'inputs'))
         self.assertTrue(hasattr(self.tool						,       'outputs'))

         lowercase__   :       List[Any]				=	self.tool.inputs
         for _input in inputs:
              if isinstance(_input						,       a):
                   for __input in _input:
                        self.assertTrue(__input in authorized_types)
              else:
                   self.assertTrue(_input in authorized_types)

         lowercase__   :       Dict				=	self.tool.outputs
         for _output in outputs:
              self.assertTrue(_output in authorized_types)





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	create_inputs(self.tool.inputs)
         lowercase__   :       Optional[int]				=	self.tool(*a)

         # There is a single output
         if len(self.tool.outputs) == 1:
              lowercase__   :       Optional[int]				=	[outputs]

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





    def 				snake_case_    (				self):
         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 				snake_case_    (				self):
         lowercase__   :       str				=	create_inputs(self.tool.inputs)
         lowercase__   :       List[str]				=	self.tool(*a)

         if not isinstance(a						,       a):
              lowercase__   :       List[str]				=	[outputs]

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

         for output, output_type in zip(a						,       self.tool.outputs):
              lowercase__   :       Union[str, Any]				=	AGENT_TYPE_MAPPING[output_type]
              self.assertTrue(isinstance(a						,       a))





    def 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	create_inputs(self.tool.inputs)

         lowercase__   :       Dict				=	[]

         for _input, input_type in zip(a						,       self.tool.inputs):
              if isinstance(a						,       a):
                   _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
         lowercase__   :       List[Any]				=	self.tool(*a)

         if not isinstance(a						,       a):
              lowercase__   :       Optional[Any]				=	[outputs]

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




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			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.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''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''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' 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:
              snake_case_        =			reader.read()
              snake_case_        =			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'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

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

       snake_case_        =			dict(model.config)

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

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

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

