

'''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       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> Optional[Any]:


	"""simple docstring"""





	if name != "training_function" and name != "main":
		raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
	UpperCAmelCase_ , UpperCAmelCase_			:	int      =   [], False
	for line in lines:
		if not found_start and F'''def {name}''' in line:
			UpperCAmelCase_			:	List[str]      =   True
			good_lines.append(_SCREAMING_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(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Optional[Any]:


	"""simple docstring"""





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





def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    bool     ,							_SCREAMING_SNAKE_CASE					:    str = None							)			-> int:


	"""simple docstring"""





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

	UpperCAmelCase_			:	List[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
	UpperCAmelCase_			:	Dict      =   []
	UpperCAmelCase_			:	Any      =   []  # We keep track of the idxs just in case it's a repeated statement
	UpperCAmelCase_			:	Optional[int]      =   iter(_SCREAMING_SNAKE_CASE							)
	for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
		if i not in passed_idxs:
			UpperCAmelCase_			:	str      =   next(_SCREAMING_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(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					UpperCAmelCase_			:	List[Any]      =   next(_SCREAMING_SNAKE_CASE							)

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

    # Finally, get the overall diff
	UpperCAmelCase_			:	Union[str, Any]      =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		UpperCAmelCase_			:	int      =   [line for line in full_file_contents if line not in secondary_file_func]
		UpperCAmelCase_			:	str      =   [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 TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       Any ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case       ,				'''image_mean''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''image_std''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_resize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  image_processor(video_inputs[0]       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)

          # Test batched
          __UpperCamelCase     						=  image_processor(snake_case       ,				return_tensors='''pt''' ).pixel_values
          self.assertEqual(
              encoded_videos.shape       ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['''height'''],
                  self.image_processor_tester.crop_size['''width'''],
              )       ,				)






