



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 MobileNetVaImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[Any]=7  ,	UpperCamelCase			:				Any=3  ,	UpperCamelCase			:				Optional[Any]=1_8  ,	UpperCamelCase			:				List[str]=3_0  ,	UpperCamelCase			:				Optional[Any]=4_0_0  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Optional[Any]=None  ,	UpperCamelCase			:				Tuple=True  ,	UpperCamelCase			:				Dict=None  ,	)->Tuple:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						size if size is not None else {"shortest_edge": 2_0}
      __SCREAMING_SNAKE_CASE							:     int							=						crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      __SCREAMING_SNAKE_CASE							:     Any							=						parent
      __SCREAMING_SNAKE_CASE							:     int							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Tuple							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_size
      __SCREAMING_SNAKE_CASE							:     str							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     str							=						do_resize
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						size
      __SCREAMING_SNAKE_CASE							:     int							=						do_center_crop
      __SCREAMING_SNAKE_CASE							:     Any							=						crop_size






     def 						__snake_case     (							self			:				Dict					)->Dict:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(_a      ,     unittest.TestCase			):
     lowerCAmelCase       =      MobileNetVaImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				Dict					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						MobileNetVaImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				str					)->str:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				Tuple					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(_A  ,	"do_resize"					)					)
      self.assertTrue(hasattr(_A  ,	"size"					)					)
      self.assertTrue(hasattr(_A  ,	"do_center_crop"					)					)
      self.assertTrue(hasattr(_A  ,	"center_crop"					)					)





     def 						__snake_case     (							self			:				Tuple					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 2_0}					)
      self.assertEqual(image_processor.crop_size  ,	{"height": 1_8, "width": 1_8}					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class.from_dict(self.image_processor_dict  ,	size=4_2  ,	crop_size=8_4					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 4_2}					)
      self.assertEqual(image_processor.crop_size  ,	{"height": 8_4, "width": 8_4}					)





     def 						__snake_case     (							self			:				Dict					)->Union[str, Any]:
      pass





     def 						__snake_case     (							self			:				Dict					)->Optional[int]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_A					)
      for image in image_inputs:
       self.assertIsInstance(_A  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     str							=						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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     List[str]							=						image_processing(_A  ,	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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)





     def 						__snake_case     (							self			:				Optional[int]					)->int:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     Dict							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_A  ,	numpify=_A					)
      for image in image_inputs:
       self.assertIsInstance(_A  ,	np.ndarray					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processing(_A  ,	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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)






     def 						__snake_case     (							self			:				Dict					)->Optional[int]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=_A  ,	torchify=_A					)
      for image in image_inputs:
       self.assertIsInstance(_A  ,	torch.Tensor					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     Tuple							=						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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processing(_A  ,	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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)









# Copyright 2022 The HuggingFace 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 os
from typing import List


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if name != "training_function" and name != "main":
										raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""   )
					__lowerCAmelCase,						__lowerCAmelCase       =				[], False
					for line in lines:
										if not found_start and F"""def {name}""" in line:
															__lowerCAmelCase       =				True
															good_lines.append(UpperCAmelCase__   )
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					return [line for line in lines if not line.lstrip().startswith('#'   ) and line != "\n"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__ = None   ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(os.path.abspath(os.path.join('examples'		,       'nlp_example.py'   )   )		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					if secondary_filename is not None:
										with open(UpperCAmelCase__		,       'r'   ) as f:
															__lowerCAmelCase       =				f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
					else:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(
															    get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )

					__lowerCAmelCase       =				'train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'

					# Specific code in our script that differs from the full version, aka what is new
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					__lowerCAmelCase       =				iter(UpperCAmelCase__   )
					for i in range(len(UpperCAmelCase__   ) - 1   ):
										if i not in passed_idxs:
															__lowerCAmelCase       =				next(UpperCAmelCase__   )
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									__lowerCAmelCase       =				next(UpperCAmelCase__   )

    # Extract out just the new parts from the full_file_training_func
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(UpperCAmelCase__   ):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )

    # Finally, get the overall diff
					__lowerCAmelCase       =				[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										__lowerCAmelCase       =				[line for line in full_file_contents if line not in secondary_file_func]
										__lowerCAmelCase       =				[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example






