import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


UpperCAmelCase     						=			{}
for task, _ in pipeline_test_mapping.items():
 UpperCAmelCase     						=			{'''pt''': None, '''tf''': None}


# DO **NOT** add item to this set (unless the reason is approved)
UpperCAmelCase     						=			{
    '''tests/models/esm/test_modeling_esmfold.py''',  # The pipeline test mapping is added to `test_modeling_esm.py`
}


def SCREAMING_SNAKE_CASE			(       lowercase_:		str			):

       if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "pt"
       elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "tf"
       elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
              return "flax"
       else:
              return None


def SCREAMING_SNAKE_CASE			(       lowercase_:		Union[str, Any]						,  lowercase_:		List[str]			):
       # Use the cached results
       if PIPELINE_TEST_MAPPING[task].get(lowercase_						,  lowercase_			) is not None:
              return PIPELINE_TEST_MAPPING[task][framework]

       _UpperCamelCase       :    Optional[int]					  =					pipeline_test_mapping[task]['test']
       _UpperCamelCase       :    Tuple					  =					None

       if framework == "pt":
              _UpperCamelCase       :    str					  =					getattr(lowercase_						,  'model_mapping'						,  lowercase_			)
       elif framework == "tf":
              _UpperCamelCase       :    int					  =					getattr(lowercase_						,  'tf_model_mapping'						,  lowercase_			)

       if mapping is not None:
              _UpperCamelCase       :    Optional[int]					  =					dict(mapping.items()			)

       # cache the results
       _UpperCamelCase       :    Dict					  =					mapping
       return mapping


def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		Union[str, Any]			):
       _UpperCamelCase       :    Optional[int]					  =					get_framework(lowercase_			)
       if framework is None:
              return None
       _UpperCamelCase       :    str					  =					get_mapping_for_task(lowercase_						,  lowercase_			)
       if mapping is None:
              return None

       _UpperCamelCase       :    Union[str, Any]					  =					list({model_class.config_class for model_class in test_class.all_model_classes}			)
       if len(lowercase_			) != 1:
              raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'			)

       # This could be a list/tuple of model classes, but it's rare.
       _UpperCamelCase       :    List[str]					  =					mapping.get(config_classes[0]						,  lowercase_			)
       if isinstance(lowercase_						,  (tuple, list)			):
              _UpperCamelCase       :    Optional[int]					  =					sorted(lowercase_						,  key=lambda lowercase_			: x.__name__			)

       return model_class


def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]			):
       _UpperCamelCase       :    int					  =					[(task, get_model_for_pipeline_test(lowercase_						,  lowercase_			)) for task in pipeline_test_mapping]
       _UpperCamelCase       :    Union[str, Any]					  =					sorted([(task, model) for task, model in mapping if model is not None]						,  key=lambda lowercase_			: x[0]			)

       return dict(lowercase_			)


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any			):
       _UpperCamelCase       :    Tuple					  =					get_framework(lowercase_			)
       if framework == "pt":
              _UpperCamelCase       :    Optional[int]					  =					'torch'
       _UpperCamelCase       :    Optional[int]					  =					'{}'

       _UpperCamelCase       :    Dict					  =					get_pipeline_model_mapping(lowercase_			)
       if len(lowercase_			) == 0:
              return ""

       _UpperCamelCase       :    int					  =					[]
       for task, model_classes in mapping.items():
              if isinstance(lowercase_						,  (tuple, list)			):
                     # A list/tuple of model classes
                     _UpperCamelCase       :    Optional[int]					  =					'(' + ', '.join([x.__name__ for x in model_classes]			) + ')'
              else:
                     # A single model class
                     _UpperCamelCase       :    str					  =					model_classes.__name__
              texts.append(F"\"{task}\": {value}"			)
       _UpperCamelCase       :    Any					  =					'{' + ', '.join(lowercase_			) + '}'
       _UpperCamelCase       :    List[str]					  =					F"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

       return text


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]			):
       _UpperCamelCase       :    int					  =					{'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
       if not issubclass(lowercase_						,  unittest.TestCase			):
              return False
       return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]			)			) > 0


def SCREAMING_SNAKE_CASE			(       lowercase_:		str			):
       _UpperCamelCase       :    Union[str, Any]					  =					[x for x in get_test_classes(lowercase_			) if is_valid_test_class(lowercase_			)]

       _UpperCamelCase       :    Optional[int]					  =					None
       for test_class in test_classes:
              # If a test class has defined `pipeline_model_mapping`, let's take it
              if getattr(lowercase_						,  'pipeline_model_mapping'						,  lowercase_			) is not None:
                     _UpperCamelCase       :    Union[str, Any]					  =					test_class
                     break
    # Take the test class with the shortest name (just a heuristic)
       if target_test_class is None and len(lowercase_			) > 0:
              _UpperCamelCase       :    Optional[Any]					  =					sorted(lowercase_						,  key=lambda lowercase_			: (len(x.__name__			), x.__name__)			)[0]

       return target_test_class


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any						,  lowercase_:		Tuple						,  lowercase_:		Optional[Any]			):
       _UpperCamelCase       :    int					  =					start_idx
       for idx, line in enumerate(lines[start_idx:]			):
              _UpperCamelCase       :    Dict					  =					len(lowercase_			) - len(line.lstrip()			)
              if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
                     _UpperCamelCase       :    Optional[int]					  =					start_idx + idx
              elif idx > 0 and indent <= indent_level:
                     # Outside the definition block of `pipeline_model_mapping`
                     break

       return end_idx


def SCREAMING_SNAKE_CASE			(       lowercase_:		int						,  lowercase_:		Optional[int]=False			):
       if getattr(lowercase_						,  'pipeline_model_mapping'						,  lowercase_			) is not None:
              if not overwrite:
                     return "", -1

       _UpperCamelCase       :    Optional[int]					  =					get_pipeline_model_mapping_string(lowercase_			)
       if len(lowercase_			) == 0:
              return "", -1
       _UpperCamelCase       :    Any					  =					line_to_add + '\n'

       # The code defined the class `test_class`
       _UpperCamelCase  , _UpperCamelCase       :    Optional[int]					  =					inspect.getsourcelines(lowercase_			)
       # `inspect` gives the code for an object, including decorator(s) if any.
       # We (only) need the exact line of the class definition.
       for idx, line in enumerate(lowercase_			):
              if line.lstrip().startswith('class '			):
                     _UpperCamelCase       :    Union[str, Any]					  =					class_lines[idx:]
                     class_start_line_no += idx
                     break
       _UpperCamelCase       :    Union[str, Any]					  =					class_start_line_no + len(lowercase_			) - 1

       # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
       # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
       # block that appears in a `test_class`.
       _UpperCamelCase       :    Any					  =					None
       # The indent level of the line at `class_lines[start_idx]` (if defined)
       _UpperCamelCase       :    List[str]					  =					0
       # To record if `pipeline_model_mapping` is found in `test_class`.
       _UpperCamelCase       :    Union[str, Any]					  =					None
       for idx, line in enumerate(lowercase_			):
              if line.strip().startswith('all_model_classes = '			):
                     _UpperCamelCase       :    Tuple					  =					len(lowercase_			) - len(line.lstrip()			)
                     _UpperCamelCase       :    Tuple					  =					idx
              elif line.strip().startswith('all_generative_model_classes = '			):
                     _UpperCamelCase       :    str					  =					len(lowercase_			) - len(line.lstrip()			)
                     _UpperCamelCase       :    List[str]					  =					idx
              elif line.strip().startswith('pipeline_model_mapping = '			):
                     _UpperCamelCase       :    List[Any]					  =					len(lowercase_			) - len(line.lstrip()			)
                     _UpperCamelCase       :    List[str]					  =					idx
                     _UpperCamelCase       :    Any					  =					line
                     break

       if start_idx is None:
              return "", -1
       # Find the ending index (inclusive) of the above found block.
       _UpperCamelCase       :    List[Any]					  =					find_block_ending(lowercase_						,  lowercase_						,  lowercase_			)

       # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
       # `is_timm_available()` instead of `is_torch_available()`.
       # Keep leading and trailing whitespaces
       _UpperCamelCase       :    Dict					  =					re.compile(R'\s(is_\S+?_available\(\))\s'			)
       for line in class_lines[start_idx : end_idx + 1]:
              _UpperCamelCase       :    List[str]					  =					r.search(lowercase_			)
              if backend_condition is not None:
                     # replace the leading and trailing whitespaces to the space character " ".
                     _UpperCamelCase       :    Dict					  =					' ' + backend_condition[0][1:-1] + ' '
                     _UpperCamelCase       :    Optional[Any]					  =					r.sub(lowercase_						,  lowercase_			)
                     break

       if def_line is None:
              # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
              # `all_model_classes` or `all_generative_model_classes`.
              _UpperCamelCase       :    List[str]					  =					end_idx
       else:
              # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
              _UpperCamelCase       :    int					  =					start_idx - 1
              # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
              for idx in range(lowercase_						,  end_idx + 1			):
                     # These lines are going to be removed before writing to the test file.
                     _UpperCamelCase       :    Optional[Any]					  =					None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
       _UpperCamelCase       :    Dict					  =					[x.__name__ for x in test_class.__bases__]
       if "PipelineTesterMixin" not in parent_classes:
              # Put `PipelineTesterMixin` just before `unittest.TestCase`
              _UpperCamelCase       :    Optional[Any]					  =					[x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
              if "TestCase" in parent_classes:
                     # Here we **assume** the original string is always with `unittest.TestCase`.
                     _parent_classes.append('unittest.TestCase'			)
              _UpperCamelCase       :    Any					  =					', '.join(_parent_classes			)
              for idx, line in enumerate(lowercase_			):
                     # Find the ending of the declaration of `test_class`
                     if line.strip().endswith('):'			):
                            # mark the lines of the declaration of `test_class` to be removed
                            for _idx in range(idx + 1			):
                                   _UpperCamelCase       :    int					  =					None  # noqa
                            break
        # Add the new, one-line, class declaration for `test_class`
              _UpperCamelCase       :    List[Any]					  =					F"class {test_class.__name__}({parent_classes}):\n"

       # Add indentation
       _UpperCamelCase       :    str					  =					' ' * indent_level + line_to_add
       # Insert `pipeline_model_mapping` to `class_lines`.
       # (The line at `target_idx` should be kept by definition!)
       _UpperCamelCase       :    Tuple					  =					class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
       # Remove the lines that are marked to be removed
       _UpperCamelCase       :    str					  =					[x for x in class_lines if x is not None]

       # Move from test class to module (in order to write to the test file)
       _UpperCamelCase       :    Optional[Any]					  =					inspect.getsourcelines(inspect.getmodule(lowercase_			)			)[0]
       # Be careful with the 1-off between line numbers and array indices
       _UpperCamelCase       :    Any					  =					module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
       _UpperCamelCase       :    Optional[int]					  =					''.join(lowercase_			)

       _UpperCamelCase       :    Any					  =					inspect.getsourcefile(lowercase_			)
       with open(lowercase_						,  'w'						,  encoding='UTF-8'						,  newline='\n'			) as fp:
              fp.write(lowercase_			)

       return line_to_add


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]						,  lowercase_:		Union[str, Any]=False			):
       _UpperCamelCase       :    List[Any]					  =					find_test_class(lowercase_			)
       if test_class:
              add_pipeline_model_mapping(lowercase_						,  overwrite=lowercase_			)


if __name__ == "__main__":
 UpperCAmelCase     						=			argparse.ArgumentParser()
 parser.add_argument(
     '''--test_file''', type=str, help='''A path to the test file, starting with the repository\'s `tests` directory.'''
 )
 parser.add_argument(
     '''--all''',
     action='''store_true''',
     help='''If to check and modify all test files.''',
 )
 parser.add_argument(
     '''--overwrite''',
     action='''store_true''',
     help='''If to overwrite a test class if it has already defined `pipeline_model_mapping`.''',
 )
 UpperCAmelCase     						=			parser.parse_args()

 if not args.all and not args.test_file:
  raise ValueError('''Please specify either `test_file` or pass `--all` to check/modify all test files.''')
 elif args.all and args.test_file:
  raise ValueError('''Only one of `--test_file` and `--all` could be specified.''')

 UpperCAmelCase     						=			[]
 if args.test_file:
  UpperCAmelCase     						=			[args.test_file]
 else:
  UpperCAmelCase     						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  for test_file in glob.glob(pattern):
   # `Flax` is not concerned at this moment
   if not test_file.startswith('''test_modeling_flax_'''):
    test_files.append(test_file)

 for test_file in test_files:
  if test_file in TEST_FILE_TO_IGNORE:
   print(f'''[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.''')
   continue
  add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase     						=			False




@nightly
@require_torch_gpu
class 				__lowerCAmelCase					(    unittest.TestCase  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						Optional[Any]					)	->							str:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Dict					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)

        _UpperCamelCase       :    Optional[int]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowerCamelCase					)
               _UpperCamelCase       :    int					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCamelCase					)

        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    str					  =					generator.manual_seed(0					)
        _UpperCamelCase       :    Tuple					  =					pipe(
            prompt='first prompt'    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2    ,     output_type='numpy'    ,     ).images

        assert np.abs(image - new_image					).sum() < 1e-5, "Models don't have the same forward pass"

 def    __snake_case   (     self    :						Tuple					)	->							Optional[int]:
        _UpperCamelCase       :    Optional[Any]					  =					VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'					)
        pipe.remove_unused_weights()
        pipe.to(lowerCamelCase					)
        pipe.set_progress_bar_config(disable=lowerCamelCase					)

        _UpperCamelCase       :    Union[str, Any]					  =					'cyberpunk 2077'
        _UpperCamelCase       :    str					  =					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'					)
        _UpperCamelCase       :    List[Any]					  =					torch.manual_seed(0					)
        _UpperCamelCase       :    Optional[Any]					  =					pipe(
            prompt=lowerCamelCase    ,     image=lowerCamelCase    ,     text_to_image_strength=0.7_5    ,     generator=lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=5_0    ,     output_type='numpy'    ,     ).images

        _UpperCamelCase       :    Any					  =					image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        _UpperCamelCase       :    Optional[int]					  =					np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2



