



"""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 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					pass



def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							Dict[str, Callable]  ,      lowercase_   :							List[Any]=None  ,      lowercase_   :							Dict=False							)		->				Any:
				try:
								_lowerCamelCase					     = ast.parse(lowercase_							)
				except SyntaxError as e:
								print('''The code generated by the agent is not valid.\n'''  ,      lowercase_							)
								return
				if state is None:
								_lowerCamelCase					     = {}
				_lowerCamelCase					     = None
				for idx, node in enumerate(expression.body							):
								try:
												_lowerCamelCase					     = evaluate_ast(lowercase_  ,      lowercase_  ,      lowercase_							)
								except InterpretorError as e:
												_lowerCamelCase					     = 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:
												_lowerCamelCase					     = line_result

				return result



def 				lowerCAmelCase_(      lowercase_   :							ast.AST  ,      lowercase_   :							Dict[str, Any]  ,      lowercase_   :							Dict[str, Callable]							)		->				Tuple:
				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
								_lowerCamelCase					     = [evaluate_ast(lowercase_  ,      lowercase_  ,      lowercase_							) for k in expression.keys]
								_lowerCamelCase					     = [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 				lowerCAmelCase_(      lowercase_   :							Dict  ,      lowercase_   :							Any  ,      lowercase_   :							Optional[int]							)		->				Dict:
				_lowerCamelCase					     = assign.targets
				_lowerCamelCase					     = evaluate_ast(assign.value  ,      lowercase_  ,      lowercase_							)

				if len(lowercase_							) == 1:
								_lowerCamelCase					     = 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_							):
												_lowerCamelCase					     = r
				return result



def 				lowerCAmelCase_(      lowercase_   :							List[str]  ,      lowercase_   :							Any  ,      lowercase_   :							Tuple							)		->				Optional[Any]:
				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							)}."""							)
				_lowerCamelCase					     = 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})."""							)

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



def 				lowerCAmelCase_(      lowercase_   :							Any  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Any							)		->				int:
				_lowerCamelCase					     = evaluate_ast(subscript.slice  ,      lowercase_  ,      lowercase_							)
				_lowerCamelCase					     = 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_							):
								_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							List[Any]  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Union[str, Any]							)		->				Tuple:
				if name.id in state:
								return state[name.id]
				_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							Dict  ,      lowercase_   :							Union[str, Any]							)		->				List[str]:
				if len(condition.ops							) > 1:
								raise InterpretorError('''Cannot evaluate conditions with multiple operators'''							)

				_lowerCamelCase					     = evaluate_ast(condition.left  ,      lowercase_  ,      lowercase_							)
				_lowerCamelCase					     = condition.ops[0]
				_lowerCamelCase					     = 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 				lowerCAmelCase_(      lowercase_   :							Tuple  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Optional[int]							)		->				Union[str, Any]:
				_lowerCamelCase					     = None
				if evaluate_condition(if_statement.test  ,      lowercase_  ,      lowercase_							):
								for line in if_statement.body:
												_lowerCamelCase					     = evaluate_ast(lowercase_  ,      lowercase_  ,      lowercase_							)
												if line_result is not None:
																_lowerCamelCase					     = line_result
				else:
								for line in if_statement.orelse:
												_lowerCamelCase					     = evaluate_ast(lowercase_  ,      lowercase_  ,      lowercase_							)
												if line_result is not None:
																_lowerCamelCase					     = line_result
				return result




def 				lowerCAmelCase_(      lowercase_   :							Any  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Union[str, Any]							)		->				List[Any]:
				_lowerCamelCase					     = None
				_lowerCamelCase					     = evaluate_ast(for_loop.iter  ,      lowercase_  ,      lowercase_							)
				for counter in iterator:
								_lowerCamelCase					     = counter
								for expression in for_loop.body:
												_lowerCamelCase					     = evaluate_ast(lowercase_  ,      lowercase_  ,      lowercase_							)
												if line_result is not None:
																_lowerCamelCase					     = line_result
				return result





"""simple docstring"""
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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import BlipImageProcessor


class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=7 ,				lowerCamelCase__=3 ,				lowerCamelCase__=1_8 ,				lowerCamelCase__=3_0 ,				lowerCamelCase__=4_0_0 ,				lowerCamelCase__=True ,				lowerCamelCase__=None ,				lowerCamelCase__=True ,				lowerCamelCase__=False ,				lowerCamelCase__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,				lowerCamelCase__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,				lowerCamelCase__=True ,				):
									_lowerCamelCase					     = size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = num_channels
									_lowerCamelCase					     = image_size
									_lowerCamelCase					     = min_resolution
									_lowerCamelCase					     = max_resolution
									_lowerCamelCase					     = do_resize
									_lowerCamelCase					     = size
									_lowerCamelCase					     = do_normalize
									_lowerCamelCase					     = image_mean
									_lowerCamelCase					     = image_std
									_lowerCamelCase					     = do_pad
									_lowerCamelCase					     = do_convert_rgb

					def 	snake_case__					(			self      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									    "do_pad": self.do_pad,
									}





					def 	snake_case__					(			self ,				lowerCamelCase__=False ,				lowerCamelCase__=False ,				lowerCamelCase__=False      ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	image_inputs.append(
																	    np.random.randint(
																	        2_5_5 ,				size=(self.num_channels, self.max_resolution, self.max_resolution) ,				dtype=np.uinta      )      )
									else:
													_lowerCamelCase					     = []
													for i in range(self.batch_size      ):
																	_lowerCamelCase     ,   _lowerCamelCase					     = np.random.choice(np.arange(self.min_resolution ,				self.max_resolution      ) ,				2      )
																	image_inputs.append(np.random.randint(2_5_5 ,				size=(self.num_channels, width, height) ,				dtype=np.uinta      )      )

									if not numpify and not torchify:
													# PIL expects the channel dimension as last dimension
													_lowerCamelCase					     = [Image.fromarray(np.moveaxis(lowerCamelCase__ ,				0 ,				-1      )      ) for x in image_inputs]

									if torchify:
													_lowerCamelCase					     = [torch.from_numpy(lowerCamelCase__      ) for x in image_inputs]

									return image_inputs


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Any        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self      )

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				numpify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				np.ndarray      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__ ,				torchify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				torch.Tensor      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(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['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)


@require_torch
@require_vision
class 							lowerCamelCase_(      A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Tuple        =       BlipImageProcessor if is_vision_available() else None

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = BlipImageProcessingTester(self ,				num_channels=4      )
									_lowerCamelCase					     = 3

					@property
					def 	snake_case__					(			self      ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_resize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''size'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_normalize'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_mean'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''image_std'''      )      )
									self.assertTrue(hasattr(lowerCamelCase__ ,				'''do_convert_rgb'''      )      )

					def 	snake_case__					(			self      ):
									pass





					def 	snake_case__					(			self      ):
									# Initialize image_processor
									_lowerCamelCase					     = self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									_lowerCamelCase					     = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__ ,				Image.Image      )

									# Test not batched input
									_lowerCamelCase					     = image_processor(image_inputs[0] ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)

									# Test batched
									_lowerCamelCase					     = image_processor(lowerCamelCase__ ,				return_tensors='''pt'''      ).pixel_values
									self.assertEqual(
									    encoded_images.shape ,				(
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['''height'''],
									        self.image_processor_tester.size['''width'''],
									    ) ,				)
