





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ConvNextImageProcessor





class SCREAMING_SNAKE_CASE							(   unittest.TestCase   ):


  def __init__(       self   ,__snake_case   ,__snake_case=7   ,__snake_case=3   ,__snake_case=1_8   ,__snake_case=3_0   ,__snake_case=4_0_0   ,__snake_case=True   ,__snake_case=None   ,__snake_case=0.875   ,__snake_case=True   ,__snake_case=[0.5, 0.5, 0.5]   ,__snake_case=[0.5, 0.5, 0.5]   ,):



       """simple docstring"""





       A_					      =					size if size is not None else {'''shortest_edge''': 2_0}
       A_					      =					parent
       A_					      =					batch_size
       A_					      =					num_channels
       A_					      =					image_size
       A_					      =					min_resolution
       A_					      =					max_resolution
       A_					      =					do_resize
       A_					      =					size
       A_					      =					crop_pct
       A_					      =					do_normalize
       A_					      =					image_mean
       A_					      =					image_std




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class SCREAMING_SNAKE_CASE							(   lowerCAmelCase_    ,		unittest.TestCase   ):
  __lowerCAmelCase      :	str  	=      ConvNextImageProcessor if is_vision_available() else None


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					ConvNextImageProcessingTester(self   )


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self.image_processor_tester.prepare_image_processor_dict()


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''do_resize'''   )   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''size'''   )   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''crop_pct'''   )   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''do_normalize'''   )   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''image_mean'''   )   )
       self.assertTrue(hasattr(lowerCamelCase_   ,'''image_std'''   )   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size   ,{'''shortest_edge''': 2_0}   )

       A_					      =					self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2   )
       self.assertEqual(image_processor.size   ,{'''shortest_edge''': 4_2}   )


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       pass


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       A_					      =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCamelCase_   )
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_   ,Image.Image   )

       # Test not batched input
       A_					      =					image_processing(image_inputs[0]   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)

       # Test batched
       A_					      =					image_processing(lowerCamelCase_   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       A_					      =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCamelCase_   ,numpify=lowerCamelCase_   )
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_   ,np.ndarray   )

       # Test not batched input
       A_					      =					image_processing(image_inputs[0]   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)

       # Test batched
       A_					      =					image_processing(lowerCamelCase_   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)




  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       A_					      =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowerCamelCase_   ,torchify=lowerCamelCase_   )
       for image in image_inputs:
            self.assertIsInstance(lowerCamelCase_   ,torch.Tensor   )

       # Test not batched input
       A_					      =					image_processing(image_inputs[0]   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)

       # Test batched
       A_					      =					image_processing(lowerCamelCase_   ,return_tensors='''pt'''   ).pixel_values
       self.assertEqual(
           encoded_images.shape   ,(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''shortest_edge'''],
               self.image_processor_tester.size['''shortest_edge'''],
           )   ,)










# 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 UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  pass




def     UpperCAmelCase_						(						_UpperCAmelCase       :str     ,	_UpperCAmelCase       :Dict[str, Callable]     ,	_UpperCAmelCase       :Tuple=None     ,	_UpperCAmelCase       :Union[str, Any]=False							)  ->							Optional[int]:
     '''simple docstring'''


     try:
          A_					      =					ast.parse(_UpperCAmelCase							)
     except SyntaxError as e:
          print('''The code generated by the agent is not valid.\n'''     ,	_UpperCAmelCase							)
          return
     if state is None:
          A_					      =					{}
     A_					      =					None
     for idx, node in enumerate(expression.body							):
          try:
               A_					      =					evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
          except InterpretorError as e:
               A_					      =					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(_UpperCAmelCase							)
               break
          if line_result is not None:
               A_					      =					line_result

     return result

def     UpperCAmelCase_						(						_UpperCAmelCase       :ast.AST     ,	_UpperCAmelCase       :Dict[str, Any]     ,	_UpperCAmelCase       :Dict[str, Callable]							)  ->							Dict:
     '''simple docstring'''


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


     A_					      =					assign.targets
     A_					      =					evaluate_ast(assign.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)

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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :int							)  ->							Optional[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							)}.'							)
     A_					      =					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}).'							)

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

def     UpperCAmelCase_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :Tuple							)  ->							Union[str, Any]:
     '''simple docstring'''


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

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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Tuple							)  ->							Tuple:
     '''simple docstring'''


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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[int]     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :str							)  ->							Optional[int]:
     '''simple docstring'''


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

     A_					      =					evaluate_ast(condition.left     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     A_					      =					condition.ops[0]
     A_					      =					evaluate_ast(condition.comparators[0]     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)

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

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :str     ,	_UpperCAmelCase       :List[Any]							)  ->							Optional[Any]:
     '''simple docstring'''


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


def     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :List[Any]     ,	_UpperCAmelCase       :Tuple							)  ->							str:
     '''simple docstring'''


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



