




"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict







class  _SCREAMING_SNAKE_CASE       (     _lowercase				):



 '''simple docstring'''





 pass






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		Union[str, Any]  ,	__lowerCAmelCase			:		Dict=None  ,	__lowerCAmelCase			:		Tuple=False						)    ->       Dict:





 try:
  _UpperCamelCase      :	Tuple					      =  ast.parse(lowerCamelCase_						)
 except SyntaxError as e:
  print("The code generated by the agent is not valid.\n"  ,	lowerCamelCase_						)
  return
 if state is None:
  _UpperCamelCase      :	List[str]					      =  {}
 _UpperCamelCase      :	str					      =  None
 for idx, node in enumerate(expression.body						):
  try:
   _UpperCamelCase      :	Union[str, Any]					      =  evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
  except InterpretorError as e:
   _UpperCamelCase      :	str					      =  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(lowerCamelCase_						)
   break
  if line_result is not None:
   _UpperCamelCase      :	List[Any]					      =  line_result

 return result






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		int  ,	__lowerCAmelCase			:		List[str]						)    ->       str:





 if isinstance(lowerCamelCase_  ,	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(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.Call						):
  # Function call -> we return the value of the function call
  return evaluate_call(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.Constant						):
  # Constant -> just return the value
  return expression.value
 elif isinstance(lowerCamelCase_  ,	ast.Dict						):
  # Dict -> evaluate all keys and values
  _UpperCamelCase      :	Dict					      =  [evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						) for k in expression.keys]
  _UpperCamelCase      :	Tuple					      =  [evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						) for v in expression.values]
  return dict(zip(lowerCamelCase_  ,	lowerCamelCase_						)						)
 elif isinstance(lowerCamelCase_  ,	ast.Expr						):
  # Expression -> evaluate the content
  return evaluate_ast(expression.value  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.For						):
  # For loop -> execute the loop
  return evaluate_for(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.FormattedValue						):
  # Formatted value (part of f-string) -> evaluate the content and return
  return evaluate_ast(expression.value  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.If						):
  # If -> execute the right branch
  return evaluate_if(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif hasattr(lowerCamelCase_  ,	"Index"						) and isinstance(lowerCamelCase_  ,	ast.Index						):
  return evaluate_ast(expression.value  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.JoinedStr						):
  return "".join([str(evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)						) for v in expression.values]						)
 elif isinstance(lowerCamelCase_  ,	ast.List						):
  # List -> evaluate all elements
  return [evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						) for elt in expression.elts]
 elif isinstance(lowerCamelCase_  ,	ast.Name						):
  # Name -> pick up the value in the state
  return evaluate_name(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 elif isinstance(lowerCamelCase_  ,	ast.Subscript						):
  # Subscript -> return the value of the indexing
  return evaluate_subscript(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 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 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		str  ,	__lowerCAmelCase			:		List[Any]						)    ->       Optional[int]:
 _UpperCamelCase      :	Tuple					      =  assign.targets
 _UpperCamelCase      :	List[str]					      =  evaluate_ast(assign.value  ,	lowerCamelCase_  ,	lowerCamelCase_						)

 if len(lowerCamelCase_						) == 1:
  _UpperCamelCase      :	int					      =  result
 else:
  if len(lowerCamelCase_						) != len(lowerCamelCase_						):
   raise InterpretorError(f"Expected {len(lowerCamelCase_						)} values but got {len(lowerCamelCase_						)}."						)
  for var_name, r in zip(lowerCamelCase_  ,	lowerCamelCase_						):
   _UpperCamelCase      :	int					      =  r
 return result






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		List[str]  ,	__lowerCAmelCase			:		int						)    ->       Optional[int]:





 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						)}."						)
 _UpperCamelCase      :	int					      =  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})."						)

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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		List[Any]						)    ->       Tuple:
 _UpperCamelCase      :	Optional[int]					      =  evaluate_ast(subscript.slice  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 _UpperCamelCase      :	Optional[Any]					      =  evaluate_ast(subscript.value  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 if isinstance(lowerCamelCase_  ,	(list, tuple)						):
  return value[int(lowerCamelCase_						)]
 if index in value:
  return value[index]
 if isinstance(lowerCamelCase_  ,	lowerCamelCase_						) and isinstance(lowerCamelCase_  ,	lowerCamelCase_						):
  _UpperCamelCase      :	Union[str, Any]					      =  difflib.get_close_matches(lowerCamelCase_  ,	list(value.keys()						)						)
  if len(lowerCamelCase_						) > 0:
   return value[close_matches[0]]

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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		List[str]						)    ->       Optional[int]:





 if name.id in state:
  return state[name.id]
 _UpperCamelCase      :	List[str]					      =  difflib.get_close_matches(name.id  ,	list(state.keys()						)						)
 if len(lowerCamelCase_						) > 0:
  return state[close_matches[0]]
 raise InterpretorError(f"The variable `{name.id}` is not defined."						)






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Dict  ,	__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		List[str]						)    ->       List[str]:





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

 _UpperCamelCase      :	str					      =  evaluate_ast(condition.left  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 _UpperCamelCase      :	Union[str, Any]					      =  condition.ops[0]
 _UpperCamelCase      :	Union[str, Any]					      =  evaluate_ast(condition.comparators[0]  ,	lowerCamelCase_  ,	lowerCamelCase_						)

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






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[int]  ,	__lowerCAmelCase			:		Tuple  ,	__lowerCAmelCase			:		Tuple						)    ->       Optional[Any]:
 _UpperCamelCase      :	Dict					      =  None
 if evaluate_condition(if_statement.test  ,	lowerCamelCase_  ,	lowerCamelCase_						):
  for line in if_statement.body:
   _UpperCamelCase      :	Union[str, Any]					      =  evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
   if line_result is not None:
    _UpperCamelCase      :	Optional[int]					      =  line_result
 else:
  for line in if_statement.orelse:
   _UpperCamelCase      :	int					      =  evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
   if line_result is not None:
    _UpperCamelCase      :	List[Any]					      =  line_result
 return result






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[str]  ,	__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Optional[Any]						)    ->       List[str]:
 _UpperCamelCase      :	Dict					      =  None
 _UpperCamelCase      :	str					      =  evaluate_ast(for_loop.iter  ,	lowerCamelCase_  ,	lowerCamelCase_						)
 for counter in iterator:
  _UpperCamelCase      :	Tuple					      =  counter
  for expression in for_loop.body:
   _UpperCamelCase      :	Optional[int]					      =  evaluate_ast(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
   if line_result is not None:
    _UpperCamelCase      :	int					      =  line_result
 return result




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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE:    List[Any]     						=							logging.get_logger(__name__)

class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		A_    			=   ['''pixel_values''']



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






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   size if size is not None else {"height": 224, "width": 224}
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					_snake_case														=   do_resize
					_snake_case														=   do_rescale
					_snake_case														=   do_normalize
					_snake_case														=   size
					_snake_case														=   resample
					_snake_case														=   rescale_factor
					_snake_case														=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_snake_case														=   image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					Dict[str, int]	,       __SCREAMING_SNAKE_CASE     :					PILImageResampling = PILImageResampling.BILINEAR	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					Tuple	,       ):






					"""simple docstring"""
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )
					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(
					    __SCREAMING_SNAKE_CASE	,       size=(size["height"], size["width"])	,       resample=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					np.ndarray	,       __SCREAMING_SNAKE_CASE     :					float	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[str, ChannelDimension]] = None	,       **__SCREAMING_SNAKE_CASE     :					List[str] ):






					"""simple docstring"""
					return rescale(__SCREAMING_SNAKE_CASE	,       scale=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )



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






					"""simple docstring"""
					return normalize(__SCREAMING_SNAKE_CASE	,       mean=__SCREAMING_SNAKE_CASE	,       std=__SCREAMING_SNAKE_CASE	,       data_format=__SCREAMING_SNAKE_CASE	,       **__SCREAMING_SNAKE_CASE )







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






					"""simple docstring"""
					_snake_case														=   do_resize if do_resize is not None else self.do_resize
					_snake_case														=   do_rescale if do_rescale is not None else self.do_rescale
					_snake_case														=   do_normalize if do_normalize is not None else self.do_normalize
					_snake_case														=   resample if resample is not None else self.resample
					_snake_case														=   rescale_factor if rescale_factor is not None else self.rescale_factor
					_snake_case														=   image_mean if image_mean is not None else self.image_mean
					_snake_case														=   image_std if image_std is not None else self.image_std

					_snake_case														=   size if size is not None else self.size
					_snake_case														=   get_size_dict(__SCREAMING_SNAKE_CASE )

					_snake_case														=   make_list_of_images(__SCREAMING_SNAKE_CASE )

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

					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." )

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

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

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

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

					_snake_case														=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for image in images]

					_snake_case														=   {"pixel_values": images}
					return BatchFeature(data=__SCREAMING_SNAKE_CASE	,       tensor_type=__SCREAMING_SNAKE_CASE )

