

'''simple docstring'''

import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict


class   lowerCAmelCase     (      A   ):
       pass






def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  Dict[str, Callable],					lowercase__						:  Union[str, Any]=None,					lowercase__						:  Dict=False ):






     '''simple docstring'''





     try:
          __lowercase					=ast.parse(lowercase__ )
     except SyntaxError as e:
          print('The code generated by the agent is not valid.\n',					lowercase__ )
          return
     if state is None:
          __lowercase					={}
     __lowercase					=None
     for idx, node in enumerate(expression.body ):
          try:
               __lowercase					=evaluate_ast(lowercase__,					lowercase__,					lowercase__ )
          except InterpretorError as e:
               __lowercase					=F'''Evaluation of the code stopped at line {idx} before the end because of the following error'''
               if chat_mode:
                    msg += (
                        F'''. Copy paste the following error message and send it back to the agent:\nI get an error: \'{e}\''''
                    )
               else:
                    msg += F''':\n{e}'''
               print(lowercase__ )
               break
          if line_result is not None:
               __lowercase					=line_result

     return result

def 				__UpperCamelCase							(	lowercase__						:  ast.AST,					lowercase__						:  Dict[str, Any],					lowercase__						:  Dict[str, Callable] ):






     '''simple docstring'''





     if isinstance(lowercase__,					ast.Assign ):
          # Assignement -> we evaluate the assignement which should update the state
          # We return the variable assigned as it may be used to determine the final result.
          return evaluate_assign(lowercase__,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.Call ):
          # Function call -> we return the value of the function call
          return evaluate_call(lowercase__,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.Constant ):
          # Constant -> just return the value
          return expression.value
     elif isinstance(lowercase__,					ast.Dict ):
          # Dict -> evaluate all keys and values
          __lowercase					=[evaluate_ast(lowercase__,					lowercase__,					lowercase__ ) for k in expression.keys]
          __lowercase					=[evaluate_ast(lowercase__,					lowercase__,					lowercase__ ) for v in expression.values]
          return dict(zip(lowercase__,					lowercase__ ) )
     elif isinstance(lowercase__,					ast.Expr ):
          # Expression -> evaluate the content
          return evaluate_ast(expression.value,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.For ):
          # For loop -> execute the loop
          return evaluate_for(lowercase__,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.FormattedValue ):
          # Formatted value (part of f-string) -> evaluate the content and return
          return evaluate_ast(expression.value,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.If ):
          # If -> execute the right branch
          return evaluate_if(lowercase__,					lowercase__,					lowercase__ )
     elif hasattr(lowercase__,					'Index' ) and isinstance(lowercase__,					ast.Index ):
          return evaluate_ast(expression.value,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.JoinedStr ):
          return "".join([str(evaluate_ast(lowercase__,					lowercase__,					lowercase__ ) ) for v in expression.values] )
     elif isinstance(lowercase__,					ast.List ):
          # List -> evaluate all elements
          return [evaluate_ast(lowercase__,					lowercase__,					lowercase__ ) for elt in expression.elts]
     elif isinstance(lowercase__,					ast.Name ):
          # Name -> pick up the value in the state
          return evaluate_name(lowercase__,					lowercase__,					lowercase__ )
     elif isinstance(lowercase__,					ast.Subscript ):
          # Subscript -> return the value of the indexing
          return evaluate_subscript(lowercase__,					lowercase__,					lowercase__ )
     else:
          # For now we refuse anything else. Let's add things as we need them.
          raise InterpretorError(F'''{expression.__class__.__name__} is not supported.''' )

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  Dict,					lowercase__						:  Dict ):






     '''simple docstring'''





     __lowercase					=assign.targets
     __lowercase					=evaluate_ast(assign.value,					lowercase__,					lowercase__ )

     if len(lowercase__ ) == 1:
          __lowercase					=result
     else:
          if len(lowercase__ ) != len(lowercase__ ):
               raise InterpretorError(F'''Expected {len(lowercase__ )} values but got {len(lowercase__ )}.''' )
          for var_name, r in zip(lowercase__,					lowercase__ ):
               __lowercase					=r
     return result

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






     '''simple docstring'''





     if not isinstance(call.func,					ast.Name ):
          raise InterpretorError(
              F'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of '''
              F'''type {type(call.func )}.''' )
     __lowercase					=call.func.id
     if func_name not in tools:
          raise InterpretorError(
              F'''It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).''' )

     __lowercase					=tools[func_name]
     # Todo deal with args
     __lowercase					=[evaluate_ast(lowercase__,					lowercase__,					lowercase__ ) for arg in call.args]
     __lowercase					={keyword.arg: evaluate_ast(keyword.value,					lowercase__,					lowercase__ ) for keyword in call.keywords}
     return func(*lowercase__,					**lowercase__ )

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






     '''simple docstring'''





     __lowercase					=evaluate_ast(subscript.slice,					lowercase__,					lowercase__ )
     __lowercase					=evaluate_ast(subscript.value,					lowercase__,					lowercase__ )
     if isinstance(lowercase__,					(list, tuple) ):
          return value[int(lowercase__ )]
     if index in value:
          return value[index]
     if isinstance(lowercase__,					lowercase__ ) and isinstance(lowercase__,					lowercase__ ):
          __lowercase					=difflib.get_close_matches(lowercase__,					list(value.keys() ) )
          if len(lowercase__ ) > 0:
               return value[close_matches[0]]

     raise InterpretorError(F'''Could not index {value} with \'{index}\'.''' )

def 				__UpperCamelCase							(	lowercase__						:  Tuple,					lowercase__						:  Any,					lowercase__						:  Tuple ):






     '''simple docstring'''





     if name.id in state:
          return state[name.id]
     __lowercase					=difflib.get_close_matches(name.id,					list(state.keys() ) )
     if len(lowercase__ ) > 0:
          return state[close_matches[0]]
     raise InterpretorError(F'''The variable `{name.id}` is not defined.''' )

def 				__UpperCamelCase							(	lowercase__						:  int,					lowercase__						:  Tuple,					lowercase__						:  Any ):






     '''simple docstring'''





     if len(condition.ops ) > 1:
          raise InterpretorError('Cannot evaluate conditions with multiple operators' )

     __lowercase					=evaluate_ast(condition.left,					lowercase__,					lowercase__ )
     __lowercase					=condition.ops[0]
     __lowercase					=evaluate_ast(condition.comparators[0],					lowercase__,					lowercase__ )

     if isinstance(lowercase__,					ast.Eq ):
          return left == right
     elif isinstance(lowercase__,					ast.NotEq ):
          return left != right
     elif isinstance(lowercase__,					ast.Lt ):
          return left < right
     elif isinstance(lowercase__,					ast.LtE ):
          return left <= right
     elif isinstance(lowercase__,					ast.Gt ):
          return left > right
     elif isinstance(lowercase__,					ast.GtE ):
          return left >= right
     elif isinstance(lowercase__,					ast.Is ):
          return left is right
     elif isinstance(lowercase__,					ast.IsNot ):
          return left is not right
     elif isinstance(lowercase__,					ast.In ):
          return left in right
     elif isinstance(lowercase__,					ast.NotIn ):
          return left not in right
     else:
          raise InterpretorError(F'''Operator not supported: {comparator}''' )

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






     '''simple docstring'''





     __lowercase					=None
     if evaluate_condition(if_statement.test,					lowercase__,					lowercase__ ):
          for line in if_statement.body:
               __lowercase					=evaluate_ast(lowercase__,					lowercase__,					lowercase__ )
               if line_result is not None:
                    __lowercase					=line_result
     else:
          for line in if_statement.orelse:
               __lowercase					=evaluate_ast(lowercase__,					lowercase__,					lowercase__ )
               if line_result is not None:
                    __lowercase					=line_result
     return result







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






     '''simple docstring'''





     __lowercase					=None
     __lowercase					=evaluate_ast(for_loop.iter,					lowercase__,					lowercase__ )
     for counter in iterator:
          __lowercase					=counter
          for expression in for_loop.body:
               __lowercase					=evaluate_ast(lowercase__,					lowercase__,					lowercase__ )
               if line_result is not None:
                    __lowercase					=line_result
     return result





'''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      )


