

'''simple docstring'''

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 				__UpperCamelCase							(	lowercase__						:  Tuple ):






     '''simple docstring'''





     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 				__UpperCamelCase							(	lowercase__						:  Any,					lowercase__						:  List[Any] ):






     '''simple docstring'''





     if PIPELINE_TEST_MAPPING[task].get(lowercase__,					lowercase__ ) is not None:
          return PIPELINE_TEST_MAPPING[task][framework]

     __lowercase					=pipeline_test_mapping[task]['test']
     __lowercase					=None

     if framework == "pt":
          __lowercase					=getattr(lowercase__,					'model_mapping',					lowercase__ )
     elif framework == "tf":
          __lowercase					=getattr(lowercase__,					'tf_model_mapping',					lowercase__ )

     if mapping is not None:
          __lowercase					=dict(mapping.items() )

     # cache the results
     __lowercase					=mapping
     return mapping

def 				__UpperCamelCase							(	lowercase__						:  List[str],					lowercase__						:  List[str] ):






     '''simple docstring'''





     __lowercase					=get_framework(lowercase__ )
     if framework is None:
          return None
     __lowercase					=get_mapping_for_task(lowercase__,					lowercase__ )
     if mapping is None:
          return None

     __lowercase					=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.
     __lowercase					=mapping.get(config_classes[0],					lowercase__ )
     if isinstance(lowercase__,					(tuple, list) ):
          __lowercase					=sorted(lowercase__,					key=lambda lowercase__ : x.__name__ )

     return model_class

def 				__UpperCamelCase							(	lowercase__						:  List[Any] ):






     '''simple docstring'''





     __lowercase					=[(task, get_model_for_pipeline_test(lowercase__,					lowercase__ )) for task in pipeline_test_mapping]
     __lowercase					=sorted([(task, model) for task, model in mapping if model is not None],					key=lambda lowercase__ : x[0] )

     return dict(lowercase__ )

def 				__UpperCamelCase							(	lowercase__						:  Union[str, Any] ):






     '''simple docstring'''





     __lowercase					=get_framework(lowercase__ )
     if framework == "pt":
          __lowercase					='torch'
     __lowercase					='{}'

     __lowercase					=get_pipeline_model_mapping(lowercase__ )
     if len(lowercase__ ) == 0:
          return ""

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

     return text

def 				__UpperCamelCase							(	lowercase__						:  Optional[Any] ):






     '''simple docstring'''





     __lowercase					={'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 				__UpperCamelCase							(	lowercase__						:  List[Any] ):






     '''simple docstring'''





     __lowercase					=[x for x in get_test_classes(lowercase__ ) if is_valid_test_class(lowercase__ )]

     __lowercase					=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:
               __lowercase					=test_class
               break
    # Take the test class with the shortest name (just a heuristic)
     if target_test_class is None and len(lowercase__ ) > 0:
          __lowercase					=sorted(lowercase__,					key=lambda lowercase__ : (len(x.__name__ ), x.__name__) )[0]

     return target_test_class

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  Optional[Any],					lowercase__						:  Union[str, Any] ):






     '''simple docstring'''





     __lowercase					=start_idx
     for idx, line in enumerate(lines[start_idx:] ):
          __lowercase					=len(lowercase__ ) - len(line.lstrip() )
          if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
               __lowercase					=start_idx + idx
          elif idx > 0 and indent <= indent_level:
               # Outside the definition block of `pipeline_model_mapping`
               break

     return end_idx

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  List[str]=False ):






     '''simple docstring'''





     if getattr(lowercase__,					'pipeline_model_mapping',					lowercase__ ) is not None:
          if not overwrite:
               return "", -1

     __lowercase					=get_pipeline_model_mapping_string(lowercase__ )
     if len(lowercase__ ) == 0:
          return "", -1
     __lowercase					=line_to_add + '\n'

     # The code defined the class `test_class`
     __lowercase		,     __lowercase					=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 ' ):
               __lowercase					=class_lines[idx:]
               class_start_line_no += idx
               break
     __lowercase					=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`.
     __lowercase					=None
     # The indent level of the line at `class_lines[start_idx]` (if defined)
     __lowercase					=0
     # To record if `pipeline_model_mapping` is found in `test_class`.
     __lowercase					=None
     for idx, line in enumerate(lowercase__ ):
          if line.strip().startswith('all_model_classes = ' ):
               __lowercase					=len(lowercase__ ) - len(line.lstrip() )
               __lowercase					=idx
          elif line.strip().startswith('all_generative_model_classes = ' ):
               __lowercase					=len(lowercase__ ) - len(line.lstrip() )
               __lowercase					=idx
          elif line.strip().startswith('pipeline_model_mapping = ' ):
               __lowercase					=len(lowercase__ ) - len(line.lstrip() )
               __lowercase					=idx
               __lowercase					=line
               break

     if start_idx is None:
          return "", -1
     # Find the ending index (inclusive) of the above found block.
     __lowercase					=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
     __lowercase					=re.compile(R'\s(is_\S+?_available\(\))\s' )
     for line in class_lines[start_idx : end_idx + 1]:
          __lowercase					=r.search(lowercase__ )
          if backend_condition is not None:
               # replace the leading and trailing whitespaces to the space character " ".
               __lowercase					=' ' + backend_condition[0][1:-1] + ' '
               __lowercase					=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`.
          __lowercase					=end_idx
     else:
          # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
          __lowercase					=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.
               __lowercase					=None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
     __lowercase					=[x.__name__ for x in test_class.__bases__]
     if "PipelineTesterMixin" not in parent_classes:
          # Put `PipelineTesterMixin` just before `unittest.TestCase`
          __lowercase					=[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' )
          __lowercase					=', '.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 ):
                         __lowercase					=None  # noqa
                    break
        # Add the new, one-line, class declaration for `test_class`
          __lowercase					=F'''class {test_class.__name__}({parent_classes}):\n'''

     # Add indentation
     __lowercase					=' ' * indent_level + line_to_add
     # Insert `pipeline_model_mapping` to `class_lines`.
     # (The line at `target_idx` should be kept by definition!)
     __lowercase					=class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
     # Remove the lines that are marked to be removed
     __lowercase					=[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)
     __lowercase					=inspect.getsourcelines(inspect.getmodule(lowercase__ ) )[0]
     # Be careful with the 1-off between line numbers and array indices
     __lowercase					=module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
     __lowercase					=''.join(lowercase__ )

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

     return line_to_add

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  Union[str, Any]=False ):






     '''simple docstring'''





     __lowercase					=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)





'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


UpperCAmelCase				   =  logging.get_logger(__name__)


class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= ["pixel_values"]







       def __init__(							self			: Dict						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: PILImageResampling = PILImageResampling.BICUBIC						,      __lowercase			: bool = True						,      __lowercase			: Union[int, float] = 1 / 255						,      __lowercase			: bool = True						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      **__lowercase			: Any						,      ):







            """simple docstring"""







            super().__init__(**__lowercase      )
            __lowercase					=crop_size if crop_size is not None else {'height': 256, 'width': 256}
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=size if size is not None else {'height': 224, 'width': 224}
            __lowercase					=get_size_dict(__lowercase      )

            __lowercase					=do_center_crop
            __lowercase					=crop_size
            __lowercase					=do_resize
            __lowercase					=size
            __lowercase					=resample
            __lowercase					=do_rescale
            __lowercase					=rescale_factor
            __lowercase					=do_normalize
            __lowercase					=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __lowercase					=image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def 			snake_case   (							self			: int						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: Optional[int] = None						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[Any]						,      ):







            """simple docstring"""







            __lowercase					=self.size if size is None else size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )

            __lowercase		,     __lowercase					=get_image_size(__lowercase      )
            __lowercase					=min(__lowercase						,      __lowercase      )
            __lowercase					=(size['height'] / crop_size['height']) * min_dim
            __lowercase					=(size['width'] / crop_size['width']) * min_dim
            return center_crop(__lowercase						,      size=(cropped_height, cropped_width)						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: str						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: PILImageResampling = PIL.Image.BICUBIC						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=get_size_dict(__lowercase      )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
            return resize(
                __lowercase						,      size=(size['height'], size['width'])						,      resample=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: np.ndarray						,      __lowercase			: Union[int, float]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Dict						,      ):







            """simple docstring"""







            return rescale(__lowercase						,      scale=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: np.ndarray						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: int						,      ):







            """simple docstring"""







            return normalize(__lowercase						,      mean=__lowercase						,      std=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: ImageInput						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: PILImageResampling = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[float] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[str, TensorType]] = None						,      __lowercase			: ChannelDimension = ChannelDimension.FIRST						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=do_center_crop if do_center_crop is not None else self.do_center_crop
            __lowercase					=crop_size if crop_size is not None else self.crop_size
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=do_resize if do_resize is not None else self.do_resize
            __lowercase					=size if size is not None else self.size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=resample if resample is not None else self.resample
            __lowercase					=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase					=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase					=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase					=image_mean if image_mean is not None else self.image_mean
            __lowercase					=image_std if image_std is not None else self.image_std

            __lowercase					=make_list_of_images(__lowercase      )

            if not valid_images(__lowercase      ):
                 raise ValueError(
                     'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                     'torch.Tensor, tf.Tensor or jax.ndarray.'      )

            if do_center_crop and crop_size is None:
                 raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'      )

            if do_resize and size is None:
                 raise ValueError('Size must be specified if do_resize is True.'      )

            if do_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'      )

            # All transformations expect numpy arrays.
            __lowercase					=[to_numpy_array(__lowercase      ) for image in images]

            if do_center_crop:
                 __lowercase					=[self.center_crop(__lowercase						,      __lowercase						,      size=__lowercase      ) for image in images]

            if do_resize:
                 __lowercase					=[self.resize(image=__lowercase						,      size=__lowercase						,      resample=__lowercase      ) for image in images]

            if do_rescale:
                 __lowercase					=[self.rescale(image=__lowercase						,      scale=__lowercase      ) for image in images]

            if do_normalize:
                 __lowercase					=[self.normalize(image=__lowercase						,      mean=__lowercase						,      std=__lowercase      ) for image in images]

            __lowercase					=[to_channel_dimension_format(__lowercase						,      __lowercase      ) for image in images]

            __lowercase					={'pixel_values': images}
            return BatchFeature(data=__lowercase						,      tensor_type=__lowercase      )


