# Path: studio/model.py
# def reset_storage_providers():
# def get_config(config_file=None):
# def get_db_provider(config=None, blocking_auth=True):
# def get_queue(
#         queue_name=None,
#         cloud=None,
#         config=None,
#         logger=None,
#         close_after=None,
#         verbose=10):
# def shutdown_queue(queue, logger=None, delete_queue=True):
# def get_worker_manager(config, cloud=None, verbose=10):
# 
# Path: studio/experiments/experiment.py
# def create_experiment(
#         filename,
#         args,
#         experiment_name=None,
#         project=None,
#         artifacts=None,
#         resources_needed=None,
#         metric=None,
#         max_duration=None,
#         dependency_policy: DependencyPolicy = None,
#         from_compl_service: bool = False):
# 
#     if artifacts is None:
#         artifacts = {}
# 
#     key = experiment_name if experiment_name else \
#         str(int(time.time())) + "_" + str(uuid.uuid4())
# 
#     if dependency_policy is None:
#         raise ValueError("dependency_policy argument must be set.")
# 
#     packages = dependency_policy.generate(resources_needed)
# 
#     return Experiment(
#         key=key,
#         filename=filename,
#         args=args,
#         pythonenv=packages,
#         project=project,
#         artifacts=artifacts,
#         resources_needed=resources_needed,
#         metric=metric,
#         max_duration=max_duration,
#         from_compl_service=from_compl_service)
# 
# Path: studio/dependencies_policies/studio_dependencies_policy.py
# class StudioDependencyPolicy(DependencyPolicy):
#     """
#     StudioML policy for adjusting experiment dependencies
#     to specific execution environment and required resources.
#     """
# 
#     def generate(self, resources_needed):
#         if freeze is None:
#             raise ValueError(
#                 "freeze operation is not available for StudioDependencyPolicy")
# 
#         needs_gpu = self._needs_gpu(resources_needed)
#         packages = freeze.freeze()
#         result = []
#         for pkg in packages:
#             if self._is_special_reference(pkg):
#                 # git repo or directly installed package
#                 result.append(pkg)
#             elif '==' in pkg:
#                 # pypi package
#                 pkey = re.search(r'^.*?(?=\=\=)', pkg).group(0)
#                 pversion = re.search(r'(?<=\=\=).*\Z', pkg).group(0)
# 
#                 if needs_gpu and \
#                         (pkey == 'tensorflow' or pkey == 'tf-nightly'):
#                     pkey = pkey + '-gpu'
# 
#                 # TODO add installation logic for torch
#                 result.append(pkey + '==' + pversion)
#         return result
# 
#     def _is_special_reference(self, pkg: str):
#         if pkg.startswith('-e git+'):
#             return True
#         if 'git+https://' in pkg or 'file://' in pkg:
#             return True
#         return False
# 
#     def _needs_gpu(self, resources_needed):
#         return resources_needed is not None and \
#             int(resources_needed.get('gpus')) > 0


import unittest
import uuid
import os
        from pip._internal.operations import freeze
        from pip.operations import freeze
from studio import model
from studio.experiments.experiment import create_experiment
from studio.dependencies_policies.studio_dependencies_policy import StudioDependencyPolicy
try:
    try:
    except Exception:
except ImportError:
    freeze = None



def get_test_experiment():
    filename = 'test.py'
    args = ['a', 'b', 'c']
    experiment_name = 'test_experiment_' + str(uuid.uuid4())
    experiment = create_experiment(filename, args, experiment_name, dependency_policy=StudioDependencyPolicy())
    return experiment, experiment_name, filename, args


class ModelTest(unittest.TestCase):
    def test_create_experiment(self):
        _, experiment_name, filename, args = get_test_experiment()
        experiment_project = 'create_experiment_project'

# Path: studio/model.py
# def reset_storage_providers():
# def get_config(config_file=None):
# def get_db_provider(config=None, blocking_auth=True):
# def get_queue(
#         queue_name=None,
#         cloud=None,
#         config=None,
#         logger=None,
#         close_after=None,
#         verbose=10):
# def shutdown_queue(queue, logger=None, delete_queue=True):
# def get_worker_manager(config, cloud=None, verbose=10):
# 
# Path: studio/experiments/experiment.py
# def create_experiment(
#         filename,
#         args,
#         experiment_name=None,
#         project=None,
#         artifacts=None,
#         resources_needed=None,
#         metric=None,
#         max_duration=None,
#         dependency_policy: DependencyPolicy = None,
#         from_compl_service: bool = False):
# 
#     if artifacts is None:
#         artifacts = {}
# 
#     key = experiment_name if experiment_name else \
#         str(int(time.time())) + "_" + str(uuid.uuid4())
# 
#     if dependency_policy is None:
#         raise ValueError("dependency_policy argument must be set.")
# 
#     packages = dependency_policy.generate(resources_needed)
# 
#     return Experiment(
#         key=key,
#         filename=filename,
#         args=args,
#         pythonenv=packages,
#         project=project,
#         artifacts=artifacts,
#         resources_needed=resources_needed,
#         metric=metric,
#         max_duration=max_duration,
#         from_compl_service=from_compl_service)
# 
# Path: studio/dependencies_policies/studio_dependencies_policy.py
# class StudioDependencyPolicy(DependencyPolicy):
#     """
#     StudioML policy for adjusting experiment dependencies
#     to specific execution environment and required resources.
#     """
# 
#     def generate(self, resources_needed):
#         if freeze is None:
#             raise ValueError(
#                 "freeze operation is not available for StudioDependencyPolicy")
# 
#         needs_gpu = self._needs_gpu(resources_needed)
#         packages = freeze.freeze()
#         result = []
#         for pkg in packages:
#             if self._is_special_reference(pkg):
#                 # git repo or directly installed package
#                 result.append(pkg)
#             elif '==' in pkg:
#                 # pypi package
#                 pkey = re.search(r'^.*?(?=\=\=)', pkg).group(0)
#                 pversion = re.search(r'(?<=\=\=).*\Z', pkg).group(0)
# 
#                 if needs_gpu and \
#                         (pkey == 'tensorflow' or pkey == 'tf-nightly'):
#                     pkey = pkey + '-gpu'
# 
#                 # TODO add installation logic for torch
#                 result.append(pkey + '==' + pversion)
#         return result
# 
#     def _is_special_reference(self, pkg: str):
#         if pkg.startswith('-e git+'):
#             return True
#         if 'git+https://' in pkg or 'file://' in pkg:
#             return True
#         return False
# 
#     def _needs_gpu(self, resources_needed):
#         return resources_needed is not None and \
#             int(resources_needed.get('gpus')) > 0


# Path: tests/model_test.py
import unittest
import uuid
import os
        from pip._internal.operations import freeze
        from pip.operations import freeze
from studio import model
from studio.experiments.experiment import create_experiment
from studio.dependencies_policies.studio_dependencies_policy import StudioDependencyPolicy

try:
    try:
    except Exception:
except ImportError:
    freeze = None



def get_test_experiment():
    filename = 'test.py'
    args = ['a', 'b', 'c']
    experiment_name = 'test_experiment_' + str(uuid.uuid4())
    experiment = create_experiment(filename, args, experiment_name, dependency_policy=StudioDependencyPolicy())
    return experiment, experiment_name, filename, args


class ModelTest(unittest.TestCase):
    def test_create_experiment(self):
        _, experiment_name, filename, args = get_test_experiment()
        experiment_project = 'create_experiment_project'

        experiment = create_experiment(