




'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							tempfile.mkdtemp()

              # fmt: off
              __snake_case       	=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
              # fmt: on
              __snake_case       	=							dict(zip(_UpperCamelCase      , range(len(_UpperCamelCase   )   )   )   )
              __snake_case       	=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
              __snake_case       	=							{"""unk_token""": """<unk>"""}

              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""vocab_file"""]   )
              __snake_case       	=							os.path.join(self.tmpdirname      , VOCAB_FILES_NAMES["""merges_file"""]   )
              with open(self.vocab_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     fp.write(json.dumps(_UpperCamelCase   ) + """\n"""   )
              with open(self.merges_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     fp.write("""\n""".join(_UpperCamelCase   )   )

              __snake_case       	=							{
                  """do_resize""": True,
                  """size""": 20,
                  """do_center_crop""": True,
                  """crop_size""": 18,
                  """do_normalize""": True,
                  """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
                  """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
              }
              __snake_case       	=							os.path.join(self.tmpdirname      , _UpperCamelCase   )
              with open(self.image_processor_file      , """w"""      , encoding="""utf-8"""   ) as fp:
                     json.dump(_UpperCamelCase      , _UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              return CLIPTokenizer.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              return CLIPTokenizerFast.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return CLIPImageProcessor.from_pretrained(self.tmpdirname      , **_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              shutil.rmtree(self.tmpdirname   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							[np.random.randint(2_55      , size=(3, 30, 4_00)      , dtype=np.uinta   )]

              __snake_case       	=							[Image.fromarray(np.moveaxis(_UpperCamelCase      , 0      , -1   )   ) for x in image_inputs]

              return image_inputs


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							self.get_tokenizer()
              __snake_case       	=							self.get_rust_tokenizer()
              __snake_case       	=							self.get_image_processor()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_slow.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname      , use_fast=_UpperCamelCase   )

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )
              processor_fast.save_pretrained(self.tmpdirname   )
              __snake_case       	=							CLIPProcessor.from_pretrained(self.tmpdirname   )

              self.assertEqual(processor_slow.tokenizer.get_vocab()      , tokenizer_slow.get_vocab()   )
              self.assertEqual(processor_fast.tokenizer.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertEqual(tokenizer_slow.get_vocab()      , tokenizer_fast.get_vocab()   )
              self.assertIsInstance(processor_slow.tokenizer      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor_slow.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertEqual(processor_fast.image_processor.to_json_string()      , image_processor.to_json_string()   )
              self.assertIsInstance(processor_slow.image_processor      , _UpperCamelCase   )
              self.assertIsInstance(processor_fast.image_processor      , _UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							CLIPProcessor(tokenizer=self.get_tokenizer()      , image_processor=self.get_image_processor()   )
              processor.save_pretrained(self.tmpdirname   )

              __snake_case       	=							self.get_tokenizer(bos_token="""(BOS)"""      , eos_token="""(EOS)"""   )
              __snake_case       	=							self.get_image_processor(do_normalize=_UpperCamelCase      , padding_value=1.0   )

              __snake_case       	=							CLIPProcessor.from_pretrained(
                  self.tmpdirname      , bos_token="""(BOS)"""      , eos_token="""(EOS)"""      , do_normalize=_UpperCamelCase      , padding_value=1.0   )

              self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer_add_kwargs.get_vocab()   )
              self.assertIsInstance(processor.tokenizer      , _UpperCamelCase   )

              self.assertEqual(processor.image_processor.to_json_string()      , image_processor_add_kwargs.to_json_string()   )
              self.assertIsInstance(processor.image_processor      , _UpperCamelCase   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							image_processor(_UpperCamelCase      , return_tensors="""np"""   )
              __snake_case       	=							processor(images=_UpperCamelCase      , return_tensors="""np"""   )

              for key in input_image_proc.keys():
                     self.assertAlmostEqual(input_image_proc[key].sum()      , input_processor[key].sum()      , delta=1E-2   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""

              __snake_case       	=							processor(text=_UpperCamelCase   )

              __snake_case       	=							tokenizer(_UpperCamelCase   )

              for key in encoded_tok.keys():
                     self.assertListEqual(encoded_tok[key]      , encoded_processor[key]   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , ["""input_ids""", """attention_mask""", """pixel_values"""]   )

              # test if it raises when no input is passed
              with pytest.raises(_UpperCamelCase   ):
                     processor()


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              __snake_case       	=							processor.batch_decode(_UpperCamelCase   )
              __snake_case       	=							tokenizer.batch_decode(_UpperCamelCase   )

              self.assertListEqual(_UpperCamelCase      , _UpperCamelCase   )






       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							self.get_image_processor()
              __snake_case       	=							self.get_tokenizer()

              __snake_case       	=							CLIPProcessor(tokenizer=_UpperCamelCase      , image_processor=_UpperCamelCase   )

              __snake_case       	=							"""lower newer"""
              __snake_case       	=							self.prepare_image_inputs()

              __snake_case       	=							processor(text=_UpperCamelCase      , images=_UpperCamelCase   )

              self.assertListEqual(list(inputs.keys()   )      , processor.model_input_names   )

