






import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_   :				Optional[Any]						=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_   :				str						=						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_   :				Optional[Any]						=						'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_   :				List[str]						=						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase       )
							BertModel.from_pretrained(_UpperCamelCase       )
							BertTokenizer.from_pretrained(_UpperCamelCase       )
							pipeline(task='fill-mask'    ,		model=_UpperCamelCase       )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_   :				str						=						[sys.executable, '-c', '\n'.join([load, run, mock]       )]

							# should succeed
							UpperCAmelCase_   :				List[Any]						=						self.get_env()
							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_   :				Union[str, Any]						=						'1'
							UpperCAmelCase_   :				int						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							# python one-liner segments
							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_   :				Union[str, Any]						=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

							UpperCAmelCase_   :				Any						=						'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

							UpperCAmelCase_   :				List[str]						=						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

							# Force fetching the files so that we can use the cache
							UpperCAmelCase_   :				List[str]						=						'hf-internal-testing/tiny-random-bert'
							BertConfig.from_pretrained(_UpperCamelCase       )
							BertModel.from_pretrained(_UpperCamelCase       )
							BertTokenizer.from_pretrained(_UpperCamelCase       )
							pipeline(task='fill-mask'    ,		model=_UpperCamelCase       )

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_   :				Union[str, Any]						=						[sys.executable, '-c', '\n'.join([load, run, mock]       )]

							# should succeed
							UpperCAmelCase_   :				Dict						=						self.get_env()
							UpperCAmelCase_   :				List[str]						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCAmelCase_   :				List[str]						=						'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

							UpperCAmelCase_   :				Tuple						=						'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

							UpperCAmelCase_   :				Any						=						'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_   :				Optional[Any]						=						[sys.executable, '-c', '\n'.join([load, run]       )]

							# should succeed
							UpperCAmelCase_   :				Any						=						self.get_env()
							UpperCAmelCase_   :				Dict						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )

							# next emulate no network
							UpperCAmelCase_   :				Dict						=						[sys.executable, '-c', '\n'.join([load, mock, run]       )]

							# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
							# env["TRANSFORMERS_OFFLINE"] = "0"
							# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
							# self.assertEqual(result.returncode, 1, result.stderr)

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_   :				List[str]						=						'1'
							UpperCAmelCase_   :				List[Any]						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Tuple						=						'\nfrom transformers import pipeline\n        '
							UpperCAmelCase_   :				str						=						'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

							UpperCAmelCase_   :				Any						=						'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
							UpperCAmelCase_   :				Tuple						=						self.get_env()
							UpperCAmelCase_   :				Optional[int]						=						'1'
							UpperCAmelCase_   :				Optional[int]						=						[sys.executable, '-c', '\n'.join([load, mock, run]       )]
							UpperCAmelCase_   :				Optional[Any]						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		1    ,		result.stderr       )
							self.assertIn(
							    'You cannot infer task automatically within `pipeline` when using offline mode'    ,		result.stderr.decode().replace('\n'    ,		''       )    ,		)



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[Any]						=						'\nfrom transformers import AutoModel\n        '
							UpperCAmelCase_   :				int						=						'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

							# baseline - just load from_pretrained with normal network
							UpperCAmelCase_   :				Any						=						[sys.executable, '-c', '\n'.join([load, run]       )]

							# should succeed
							UpperCAmelCase_   :				Any						=						self.get_env()
							UpperCAmelCase_   :				List[str]						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCAmelCase_   :				Tuple						=						'1'
							UpperCAmelCase_   :				List[str]						=						subprocess.run(_UpperCamelCase    ,		env=_UpperCamelCase    ,		check=_UpperCamelCase    ,		capture_output=_UpperCamelCase       )
							self.assertEqual(result.returncode    ,		0    ,		result.stderr       )
							self.assertIn('success'    ,		result.stdout.decode()       )














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

import black


__UpperCAmelCase     							=						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.
__UpperCAmelCase     							=						'    \"""\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   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase    ,		'src/diffusers/schedulers/scheduling_ddpm.py'       )    ,		os.path.join(self.diffusers_dir    ,		'schedulers/scheduling_ddpm.py'       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						os.path.join(self.diffusers_dir    ,		'new_code.py'       )
							with open(_UpperCamelCase    ,		'w'    ,		newline='\n'       ) as f:
												f.write(_UpperCamelCase       )
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase       )       ) == 0       )
							else:
												check_copies.is_copy_consistent(f.name    ,		overwrite=_UpperCamelCase       )
												with open(_UpperCamelCase    ,		'r'       ) as f:
																	self.assertTrue(f.read()    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# Base copy consistency
							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'    ,		_UpperCamelCase    ,		)

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

							# Copy consistency with a really long name
							UpperCAmelCase_   :				Optional[int]						=						'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'    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		)

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






