


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__					  =  '''src/diffusers'''

# Matches is_xxx_available()
lowerCAmelCase__					  =  re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
lowerCAmelCase__					  =  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


lowerCAmelCase__					  =  '''\n{0} = None\n'''

lowerCAmelCase__					  =  '''\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'''


lowerCAmelCase__					  =  '''\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'''


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   int													=					_re_backend.findall(snake_case__						)
				if len(snake_case__						) == 0:
								return None

				return "_and_".join(snake_case__						)


def  __lowerCamelCase (       ):

				"""simple docstring"""


				with open(os.path.join(snake_case__					,			"__init__.py"						)					,			"r"					,			encoding="utf-8"					,			newline="\n"						) as f:
								lowercase__					:   Any													=					f.readlines()

				# Get to the point we do the actual imports for type checking
				lowercase__					:   Tuple													=					0
				lowercase__					:   Optional[Any]													=					{}
				# Go through the end of the file
				while line_index < len(snake_case__						):
								# If the line contains is_backend_available, we grab all objects associated with the `else` block
								lowercase__					:   Any													=					find_backend(lines[line_index]						)
								if backend is not None:
												while not lines[line_index].startswith("else:"						):
																line_index += 1
												line_index += 1
												lowercase__					:   str													=					[]
												# Until we unindent, add backend objects to the list
												while line_index < len(snake_case__						) and len(lines[line_index]						) > 1:
																lowercase__					:   int													=					lines[line_index]
																lowercase__					:   List[Any]													=					_re_single_line_import.search(snake_case__						)
																if single_line_import_search is not None:
																				objects.extend(single_line_import_search.groups()[0].split(", "						)						)
																elif line.startswith(" " * 8						):
																				objects.append(line[8:-2]						)
																line_index += 1

												if len(snake_case__						) > 0:
																lowercase__					:   Dict													=					objects
								else:
												line_index += 1

				return backend_specific_objects


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				if name.isupper():
								return DUMMY_CONSTANT.format(snake_case__						)
				elif name.islower():
								return DUMMY_FUNCTION.format(snake_case__					,			snake_case__						)
				else:
								return DUMMY_CLASS.format(snake_case__					,			snake_case__						)


def  __lowerCamelCase (       lowerCamelCase__=None						):

				"""simple docstring"""


				if backend_specific_objects is None:
								lowercase__					:   Dict													=					read_init()
				# For special correspondence backend to module name as used in the function requires_modulename
				lowercase__					:   Dict													=					{}

				for backend, objects in backend_specific_objects.items():
								lowercase__					:   Any													=					"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"						)						) + "]"
								lowercase__					:   Optional[Any]													=					"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
								dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
								dummy_file += "\n".join([create_dummy_object(snake_case__					,			snake_case__						) for o in objects]						)
								lowercase__					:   Tuple													=					dummy_file

				return dummy_files


def  __lowerCamelCase (       lowerCamelCase__=False						):

				"""simple docstring"""


				lowercase__					:   Any													=					create_dummy_files()
				# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
				lowercase__					:   Any													=					{"torch": "pt"}

				# Locate actual dummy modules and read their content.
				lowercase__					:   Optional[Any]													=					os.path.join(snake_case__					,			"utils"						)
				lowercase__					:   Optional[Any]													=					{
				    backend: os.path.join(snake_case__					,			F"""dummy_{short_names.get(snake_case__					,			snake_case__						)}_objects.py"""						)
				    for backend in dummy_files.keys()
				}

				lowercase__					:   Any													=					{}
				for backend, file_path in dummy_file_paths.items():
								if os.path.isfile(snake_case__						):
												with open(snake_case__					,			"r"					,			encoding="utf-8"					,			newline="\n"						) as f:
																lowercase__					:   Optional[int]													=					f.read()
								else:
												lowercase__					:   Any													=					""

				for backend in dummy_files.keys():
								if dummy_files[backend] != actual_dummies[backend]:
												if overwrite:
																print(
																    F"""Updating diffusers.utils.dummy_{short_names.get(snake_case__					,			snake_case__						)}_objects.py as the main """
																    "__init__ has new objects."						)
																with open(dummy_file_paths[backend]					,			"w"					,			encoding="utf-8"					,			newline="\n"						) as f:
																				f.write(dummy_files[backend]						)
												else:
																raise ValueError(
																    "The main __init__ has objects that are not present in "
																    F"""diffusers.utils.dummy_{short_names.get(snake_case__					,			snake_case__						)}_objects.py. Run `make fix-copies` """
																    "to fix this."						)


if __name__ == "__main__":
				lowerCAmelCase__					  =  argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				lowerCAmelCase__					  =  parser.parse_args()

				check_dummies(args.fix_and_overwrite)









import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
				from sagemaker import Session, TrainingJobAnalytics
				from sagemaker.huggingface import HuggingFace

@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER"""    ,      """False"""      )      ) is not True    ,      reason="""Skipping test because should only be run when releasing minor transformers version"""    ,      )
@pytest.mark.usefixtures("""sm_env"""      )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.g4dn.xlarge""",
            """results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
        },
    ]      )
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Union[str, Any]					):
									if self.framework == "pytorch":
													subprocess.run(
													    f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,       encoding="utf-8"				,       check=SCREAMING_SNAKE_CASE				,       )
									assert hasattr(self				,       "env"					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     str=1					):
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,       source_dir=self.env.test_path				,       role=self.env.role				,       image_uri=self.env.image_uri				,       base_job_name=f"""{self.env.base_job_name}-single"""				,       instance_count=SCREAMING_SNAKE_CASE				,       instance_type=self.instance_type				,       debugger_hook_config=SCREAMING_SNAKE_CASE				,       hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}				,       metric_definitions=self.env.metric_definitions				,       py_version="py36"				,       )







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									TrainingJobAnalytics(SCREAMING_SNAKE_CASE					).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""					)







					def 			snake_case					(							self :     str					):
									# create estimator
									lowercase__					:   Optional[int]													=					self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowercase__					:   Union[str, Any]													=					TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

									# extract kpis
									lowercase__					:   str													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]					)
									lowercase__					:   Tuple													=					list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]					)
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowercase__					:   Any													=					(
									    Session().describe_training_job(estimator.latest_training_job.name					).get("TrainingTimeInSeconds"				,       999_999					)
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy					)
									assert all(t <= self.results["eval_loss"] for t in eval_loss					)

									# dump tests result into json file to share in PR
									with open(f"""{estimator.latest_training_job.name}.json"""				,       "w"					) as outfile:
													json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}				,       SCREAMING_SNAKE_CASE					)




