




'''simple docstring'''





# 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 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   if name != "training_function" and name != "main":
      raise ValueError(f"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'""" )
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[], False
   for line in lines:
      if not found_start and f"""def {name}""" in line:
         SCREAMING_SNAKE_CASE__      :					int							    =			True
         good_lines.append(snake_case )
         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(snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

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







def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case = None ):




   '''simple docstring'''

   with open(snake_case						,  '''r''' ) as f:
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			f.readlines()
   with open(os.path.abspath(os.path.join('''examples'''						,  '''nlp_example.py''' ) )						,  '''r''' ) as f:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			f.readlines()
   with open(snake_case						,  '''r''' ) as f:
      SCREAMING_SNAKE_CASE__      :					int							    =			f.readlines()
   if secondary_filename is not None:
      with open(snake_case						,  '''r''' ) as f:
         SCREAMING_SNAKE_CASE__      :					str							    =			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:
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''main''' ) )
      SCREAMING_SNAKE_CASE__      :					str							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''main''' ) )
      SCREAMING_SNAKE_CASE__      :					Any							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''main''' ) )
      if secondary_filename is not None:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''main''' ) )
   else:
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''training_function''' ) )
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''training_function''' ) )
      SCREAMING_SNAKE_CASE__      :					Any							    =			clean_lines(get_function_contents_by_name(snake_case						,  '''training_function''' ) )
      if secondary_filename is not None:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			clean_lines(
             get_function_contents_by_name(snake_case						,  '''training_function''' ) )

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			'''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
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			[]
   SCREAMING_SNAKE_CASE__      :					str							    =			[]  # We keep track of the idxs just in case it's a repeated statement
   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			iter(snake_case )
   for i in range(len(snake_case ) - 1 ):
      if i not in passed_idxs:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			next(snake_case )
         if (line not in full_file_func) and (line.lstrip() != _dl_line):
            if "TESTING_MOCKED_DATALOADERS" not in line:
               new_feature_code.append(snake_case )
               passed_idxs.append(snake_case )
            else:
               # Skip over the `config['num_epochs'] = 2` statement
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			next(snake_case )

    # Extract out just the new parts from the full_file_training_func
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			[]
   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]  # We keep track of the idxs just in case it's a repeated statement
   for i, line in enumerate(snake_case ):
      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(snake_case )
               passed_idxs.append(snake_case )

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

   return diff_from_example







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 SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

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

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





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





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     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
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     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
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )






