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id int64 0 10.8M	vector listlengths 1.54k 1.54k	ast_depth int64 3 164	ast_data stringlengths 297 510k	code stringlengths 60 56.5k
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"_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_format_security_group_default_rule", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "rule_fmt", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "rule_fmt", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "security_group_default_rules"}}, "value": {"_type": "Name", "_fields": {"id": "ret", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "context", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_all_default_rules", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "security_group_api", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Raise", "_fields": {"exc": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "HTTPNotFound", "value": {"_type": "Name", "_fields": {"id": "exc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "explanation", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format_message", "value": {"_type": "Name", "_fields": {"id": "ex", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}, "cause": null}}], "name": "ex", "type": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SecurityGroupDefaultRuleNotFound", "value": {"_type": "Name", "_fields": {"id": "exception", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "finalbody": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "ret", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "index", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def index(self, req): context = req.environ['nova.context'] authorize(context) ret = {'security_group_default_rules': []} try: for rule in self.security_group_api.get_all_default_rules(context): rule_fmt = self._format_security_group_default_rule(rule) ret['security_group_default_rules'].append(rule_fmt) except exception.SecurityGroupDefaultRuleNotFound as ex: raise exc.HTTPNotFound(explanation=ex.format_message()) return ret
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"_fields": {"arg": "token", "annotation": null, "type_comment": null}}], "kwarg": {"_type": "arg", "_fields": {"arg": "kwargs", "annotation": null, "type_comment": null}}, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "profile_url", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Name", "_fields": {"id": "requests", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "params", "value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "access_token"}}], "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "token", "value": {"_type": "Name", "_fields": {"id": "token", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "resp", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "json", "value": {"_type": "Name", "_fields": {"id": "resp", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "extra_data", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "extra_data", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "sociallogin_from_response", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_provider", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "keywords": []}}}}], "name": "complete_login", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "BaiduOAuth2Adapter", "bases": [{"_type": "Name", "_fields": {"id": "OAuth2Adapter", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class BaiduOAuth2Adapter(OAuth2Adapter): provider_id = BaiduProvider.id access_token_url = 'https://openapi.baidu.com/oauth/2.0/token' authorize_url = 'https://openapi.baidu.com/oauth/2.0/authorize' profile_url = 'https://openapi.baidu.com/rest/2.0/passport/users/getLoggedInUser' def complete_login(self, request, app, token, **kwargs): resp = requests.get(self.profile_url, params={'access_token': token.token}) extra_data = resp.json() return self.get_provider().sociallogin_from_response(request, extra_data)
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input_so_far='', last_state=0) if program: program = program.replace("\r\n", "\n") state.input_so_far = program if state.input_so_far[-1:] != "\n": state.input_so_far += "\n" else: newline = self.request.get('input') if newline != "" or state.last_state == 1: state.input_so_far += newline + "\n" interp = ri.ReplayInterpreter(state.input_so_far) output_so_far = interp.run() interp_state = interp.state else: interp_state = -1 state = SoFar() state.input_so_far = "" state.put() sofar = state.key().name() output_so_far = "" if interp_state < 0: output_so_far += "\n>>>" interp_state = 0 elif interp_state <= 1: pass elif interp_state == 2: output_so_far += "\n" elif interp_state == 3: output_so_far += "\n*BYE*" state.last_state = interp_state state.put() template_values = { 'sofar': sofar, 'output': output_so_far, 'input_so_far': state.input_so_far, 'alive': interp_state != 3, } path = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(path, 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"post", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "MainPage", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "RequestHandler", "value": {"_type": "Name", "_fields": {"id": "webapp", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class MainPage(webapp.RequestHandler): def _doit(self): if self.request.get('edit'): return self._edit() program = self.request.get('program') sofar = self.request.get('sofar') interp_state = 0 if sofar: state = SoFar.get_or_insert(sofar, input_so_far='', last_state=0) if program: program = program.replace("\r\n", "\n") state.input_so_far = program if state.input_so_far[-1:] != "\n": state.input_so_far += "\n" else: newline = self.request.get('input') if newline != "" or state.last_state == 1: state.input_so_far += newline + "\n" interp = ri.ReplayInterpreter(state.input_so_far) output_so_far = interp.run() interp_state = interp.state else: interp_state = -1 state = SoFar() state.input_so_far = "" state.put() sofar = state.key().name() output_so_far = "" if interp_state < 0: output_so_far += "\n>>>" interp_state = 0 elif interp_state <= 1: pass elif interp_state == 2: output_so_far += "\n" elif interp_state == 3: output_so_far += "\n*BYE*" state.last_state = interp_state state.put() template_values = { 'sofar': sofar, 'output': output_so_far, 'input_so_far': state.input_so_far, 'alive': interp_state != 3, } path = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(path, template_values)) def _edit(self): sofar = self.request.get('sofar') interp_state = 0 if sofar: state = SoFar.get_or_insert(sofar, input_so_far='', last_state=0) while state.input_so_far[-2:] == "\n\n": state.input_so_far = state.input_so_far[:-1] else: state = SoFar() state.input_so_far = "" state.put() sofar = state.key().name() template_values = { 'sofar': sofar, 'input_so_far': state.input_so_far, } path = os.path.join(os.path.dirname(__file__), 'edit.html') self.response.out.write(template.render(path, template_values)) def get(self): self._doit() def post(self): self._doit()
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null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "ProjectManager", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Manager", "value": {"_type": "Name", "_fields": {"id": "models", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class ProjectManager(models.Manager): def migrate(self, project_set, course, user, object_map): SherdNote = models.get_model('djangosherd', 'SherdNote') Asset = models.get_model('assetmgr', 'Asset') for project_json in project_set: old_project = Project.objects.get(id=project_json['id']) project_body = old_project.body citations = SherdNote.objects.references_in_string(project_body, user) new_project = Project.objects.migrate_one(old_project, course, user) for old_note in citations: new_note = None new_asset = None try: if old_note.id in object_map['notes']: new_note = object_map['notes'][old_note.id] else: if old_note.asset.id in object_map['assets']: new_asset = object_map['assets'][old_note.asset.id] else: # migrate the asset new_asset = Asset.objects.migrate_one( old_note.asset, course, user) object_map['assets'][old_note.asset.id] = new_asset # migrate the note new_note = SherdNote.objects.migrate_one(old_note, new_asset, user) object_map['notes'][old_note.id] = new_note # Update the citations in the body with the new id(s) project_body = \ new_note.update_references_in_string( project_body, old_note) project_body = \ new_note.asset.update_references_in_string( project_body, old_note.asset) except Asset.DoesNotExist: # todo: The asset was deleted, but is still referenced. pass new_project.body = project_body new_project.save() object_map['projects'][old_project.id] = new_project return object_map def migrate_one(self, project, course, user): x = Project(title=project.title, course=course, author=user) x.save() collaboration_context = Collaboration.objects.get( content_type=ContentType.objects.get_for_model(Course), object_pk=str(course.pk)) policy = project.collaboration()._policy Collaboration.objects.create( user=x.author, title=x.title, content_object=x, context=collaboration_context, policy=policy.policy_name,) return x def visible_by_course(self, request, course): projects = Project.objects.filter(course=course) projects = projects.order_by('-modified', 'title') return [p for p in projects if p.visible(request)] def visible_by_course_and_user(self, request, course, user): projects = Project.objects.filter(Q(author=user, course=course) \| Q(participants=user, course=course) ).distinct() projects = projects.order_by('-modified', 'title') return [p for p in projects if p.visible(request)] def unresponded_assignments(self, request, user): course = request.course a = list(Project.objects.filter(course.faculty_filter, due_date__isnull=False). order_by("due_date", "-modified", "title")) a.extend(Project.objects.filter(course.faculty_filter, due_date__isnull=True). order_by("-modified", "title")) assignments = [] project_type = ContentType.objects.get_for_model(Project) for assignment in a: if (assignment.visible(request) and assignment.is_unanswered_assignment(request, user, project_type)): assignments.append(assignment) return assignments
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null, "value": "projects"}}, "value": {"_type": "Name", "_fields": {"id": "object_map", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}], "iter": {"_type": "Name", "_fields": {"id": "project_set", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "project_json", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "object_map", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "migrate", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def migrate(self, project_set, course, user, object_map): SherdNote = models.get_model('djangosherd', 'SherdNote') Asset = models.get_model('assetmgr', 'Asset') for project_json in project_set: old_project = Project.objects.get(id=project_json['id']) project_body = old_project.body citations = SherdNote.objects.references_in_string(project_body, user) new_project = Project.objects.migrate_one(old_project, course, user) for old_note in citations: new_note = None new_asset = None try: if old_note.id in object_map['notes']: new_note = object_map['notes'][old_note.id] else: if old_note.asset.id in object_map['assets']: new_asset = object_map['assets'][old_note.asset.id] else: # migrate the asset new_asset = Asset.objects.migrate_one( old_note.asset, course, user) object_map['assets'][old_note.asset.id] = new_asset # migrate the note new_note = SherdNote.objects.migrate_one(old_note, new_asset, user) object_map['notes'][old_note.id] = new_note # Update the citations in the body with the new id(s) project_body = \ new_note.update_references_in_string( project_body, old_note) project_body = \ new_note.asset.update_references_in_string( project_body, old_note.asset) except Asset.DoesNotExist: # todo: The asset was deleted, but is still referenced. pass new_project.body = project_body new_project.save() object_map['projects'][old_project.id] = new_project return object_map
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Collaboration.objects.get( content_type=ContentType.objects.get_for_model(Course), object_pk=str(course.pk)) policy = project.collaboration()._policy Collaboration.objects.create( user=x.author, title=x.title, content_object=x, context=collaboration_context, policy=policy.policy_name,) return x
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710	[ -0.031148167327046394, 0.024109428748488426, 0.014849163591861725, -0.035848453640937805, 0.01667315512895584, -0.018391916528344154, -0.04564656317234039, 0.007494735065847635, 0.014007321558892727, 0.01634577289223671, 0.0019686834421008825, -0.048733316361904144, -0.022203592583537102, ...	11	{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "request", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "user", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "course", "value": {"_type": "Name", "_fields": {"id": 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"assignment", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Name", "_fields": {"id": "assignments", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "visible", "value": {"_type": "Name", "_fields": {"id": "assignment", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "project_type", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "is_unanswered_assignment", "value": {"_type": "Name", "_fields": {"id": "assignment", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}, "orelse": []}}], "iter": {"_type": "Name", "_fields": {"id": "a", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "assignment", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "assignments", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "unresponded_assignments", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def unresponded_assignments(self, request, user): course = request.course a = list(Project.objects.filter(course.faculty_filter, due_date__isnull=False). order_by("due_date", "-modified", "title")) a.extend(Project.objects.filter(course.faculty_filter, due_date__isnull=True). order_by("-modified", "title")) assignments = [] project_type = ContentType.objects.get_for_model(Project) for assignment in a: if (assignment.visible(request) and assignment.is_unanswered_assignment(request, user, project_type)): assignments.append(assignment) return assignments
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712	[ 0.04743161052465439, 0.00504547543823719, 0.024005696177482605, -0.016647998243570328, -0.0015719658695161343, 0.03227447718381882, -0.045499008148908615, -0.04961269348859787, 0.006491477135568857, -0.0016073393635451794, -0.025662213563919067, -0.010967524722218513, 0.03611207753419876, ...	9	{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "collaboration", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "policy_name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_policy", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "policy_name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_policy", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Name", "_fields": {"id": "SHORT_NAME", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [{"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": "u", "value": "Submitted"}}}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "submitted", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [{"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": "u", "value": "Private"}}}}]}}]}}], "name": "visibility_short", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def visibility_short(self): col = self.collaboration() if col: return SHORT_NAME.get(col._policy.policy_name, col._policy.policy_name) elif self.submitted: return u"Submitted" else: return u"Private"
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"attr": "remove", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "user_set", "value": {"_type": "Name", "_fields": {"id": "colgrp", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "iter": {"_type": "Name", "_fields": {"id": "already_grp", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "oldp", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "part", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}, {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "group_id", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "count", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "user_set", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "group", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}]}}, {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "NotIn", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "author", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "part", "ctx": {"_type": "Load", "_fields": {}}}}]}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "part", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}]}}]}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "title", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "title", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "policy", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "policy", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "save", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "method", "value": {"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "POST"}}]}}, {"_type": "BoolOp", "_fields": {"op": {"_type": "Or", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "policy", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "policy", "ctx": {"_type": "Load", "_fields": {}}}}]}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "title", "value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "title", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}]}}]}}, "orelse": []}}], "test": {"_type": "Name", "_fields": {"id": "sync_group", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "col", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "collaboration", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def collaboration(self, request=None, sync_group=False): col = None policy = None if request and request.method == "POST": policy = request.POST.get('publish', 'PrivateEditorsAreOwners') try: col = Collaboration.get_associated_collab(self) except Collaboration.DoesNotExist: if policy is None: policy = "PrivateEditorsAreOwners" if request is not None: col = Collaboration.objects.create( user=self.author, title=self.title, content_object=self, context=request.collaboration_context, policy=policy,) if col is None: # iff collab did not exist and request is None return if sync_group: part = self.participants.all() if (len(part) > 1 or (col.group_id and col.group.user_set.count() > 1) or (self.author not in part and len(part) > 0)): colgrp = col.have_group() already_grp = set(colgrp.user_set.all()) for p in part: if p in already_grp: already_grp.discard(p) else: colgrp.user_set.add(p) for oldp in already_grp: colgrp.user_set.remove(oldp) if request and request.method == "POST" and \ (col.policy != policy or col.title != self.title): col.title = self.title col.policy = policy col.save() return col
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"_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SUCCESS", "value": {"_type": "Name", "_fields": {"id": "messages", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "find_message_and_dismiss", "value": {"_type": "Name", "_fields": {"id": "keypair_page", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "cleanup", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "cleanup", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "addCleanup", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "setUp", "returns": null, 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"comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}, "orelse": {"_type": "Constant", "_fields": {"kind": null, "value": null}}}}}}]}}}}]}}}}, {"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "to_runner_api", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "metrics_container", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}]}}}}], "name": "progress_metrics", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def progress_metrics(self): # type: () -> beam_fn_api_pb2.Metrics.PTransform return beam_fn_api_pb2.Metrics.PTransform( processed_elements=beam_fn_api_pb2.Metrics.PTransform.ProcessedElements( measured=beam_fn_api_pb2.Metrics.PTransform.Measured( total_time_spent=( self.scoped_start_state.sampled_seconds() + self.scoped_process_state.sampled_seconds() + self.scoped_finish_state.sampled_seconds()), # Multi-output operations should override this. output_element_counts=( # If there is exactly one output, we can unambiguously # fix its name later, which we do. # TODO(robertwb): Plumb the actual name here. {'ONLY_OUTPUT': self.receivers[0].opcounter .element_counter.value()} if len(self.receivers) == 1 else None))), user=self.metrics_container.to_runner_api())
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"type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "current_element_progress", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "consumer", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "current_element_progress", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "SingletonConsumerSet", "bases": [{"_type": "Name", "_fields": {"id": "ConsumerSet", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class SingletonConsumerSet(ConsumerSet): def __init__( self, counter_factory, step_name, output_index, consumers, coder): assert len(consumers) == 1 super(SingletonConsumerSet, self).__init__( counter_factory, step_name, output_index, consumers, coder) self.consumer = consumers[0] def receive(self, windowed_value): # type: (WindowedValue) -> None self.update_counters_start(windowed_value) self.consumer.process(windowed_value) self.update_counters_finish() def try_split(self, fraction_of_remainder): return self.consumer.try_split(fraction_of_remainder) def current_element_progress(self): return self.consumer.current_element_progress()
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{}}, "attr": "scoped_finish_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "int64_counter", "value": {"_type": "Name", "_fields": {"id": "monitoring_infos", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "ptransform", "value": {"_type": "Name", "_fields": {"id": "transform_id", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TOTAL_MSECS_URN", "value": {"_type": "Name", "_fields": {"id": "monitoring_infos", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "total_time_spent_msecs", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": 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"comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "mis", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "mi", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}}}], "name": "execution_time_monitoring_infos", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def execution_time_monitoring_infos(self, transform_id): # type: (str) -> Dict[FrozenSet, metrics_pb2.MonitoringInfo] total_time_spent_msecs = ( self.scoped_start_state.sampled_msecs_int() + self.scoped_process_state.sampled_msecs_int() + self.scoped_finish_state.sampled_msecs_int()) mis = [ monitoring_infos.int64_counter( monitoring_infos.START_BUNDLE_MSECS_URN, self.scoped_start_state.sampled_msecs_int(), ptransform=transform_id ), monitoring_infos.int64_counter( monitoring_infos.PROCESS_BUNDLE_MSECS_URN, self.scoped_process_state.sampled_msecs_int(), ptransform=transform_id ), monitoring_infos.int64_counter( monitoring_infos.FINISH_BUNDLE_MSECS_URN, self.scoped_finish_state.sampled_msecs_int(), ptransform=transform_id ), monitoring_infos.int64_counter( monitoring_infos.TOTAL_MSECS_URN, total_time_spent_msecs, ptransform=transform_id ), ] return {monitoring_infos.to_key(mi) : mi for mi in mis}
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{"ctx": {"_type": "Load", "_fields": {}}, "attr": "spec", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scoped_start_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}], "name": "start", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "ReadOperation", "bases": [{"_type": "Name", "_fields": {"id": "Operation", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class ReadOperation(Operation): def start(self): with self.scoped_start_state: super(ReadOperation, self).start() range_tracker = self.spec.source.source.get_range_tracker( self.spec.source.start_position, self.spec.source.stop_position) for value in self.spec.source.source.read(range_tracker): if isinstance(value, WindowedValue): windowed_value = value else: windowed_value = _globally_windowed_value.with_value(value) self.output(windowed_value)
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"targets": [{"_type": "Name", "_fields": {"id": "windowed_value", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "WindowedValue", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "isinstance", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "with_value", "value": {"_type": "Name", "_fields": {"id": "_globally_windowed_value", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "windowed_value", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "windowed_value", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "output", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "range_tracker", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "read", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "source", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "source", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "spec", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scoped_start_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}], "name": "start", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def start(self): with self.scoped_start_state: super(ReadOperation, self).start() range_tracker = self.spec.source.source.get_range_tracker( self.spec.source.start_position, self.spec.source.stop_position) for value in self.spec.source.source.read(range_tracker): if isinstance(value, WindowedValue): windowed_value = value else: windowed_value = _globally_windowed_value.with_value(value) self.output(windowed_value)
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[{"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "WindowedValue", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "isinstance", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "with_value", "value": {"_type": "Name", "_fields": {"id": "_globally_windowed_value", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "windowed_value", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "windowed_value", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "output", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "range_tracker", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "read", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "source", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "value", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scoped_process_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}], "name": "process", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "ImpulseReadOperation", "bases": [{"_type": "Name", "_fields": {"id": "Operation", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class ImpulseReadOperation(Operation): def __init__(self, name_context, counter_factory, state_sampler, consumers, source, output_coder): super(ImpulseReadOperation, self).__init__( name_context, None, counter_factory, state_sampler) self.source = source self.receivers = [ ConsumerSet.create( self.counter_factory, self.name_context.step_name, 0, next(iter(consumers.values())), output_coder)] def process(self, unused_impulse): # type: (WindowedValue) -> None with self.scoped_process_state: range_tracker = self.source.get_range_tracker(None, None) for value in self.source.read(range_tracker): if isinstance(value, WindowedValue): windowed_value = value else: windowed_value = _globally_windowed_value.with_value(value) self.output(windowed_value)
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"_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "timer_specs", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "user_state_context", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}, {"_type": "If", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tags_and_types", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_read_side_inputs", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": 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"_fields": {}}}}, "keywords": []}}, "orelse": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dofn_runner", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "DoFnRunnerReceiver", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "dofn_receiver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scoped_start_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}], "name": "setup", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def setup(self): # type: () -> None with self.scoped_start_state: super(DoOperation, self).setup() # See fn_data in dataflow_runner.py fn, args, kwargs, tags_and_types, window_fn = ( pickler.loads(self.spec.serialized_fn)) state = common.DoFnState(self.counter_factory) state.step_name = self.name_context.logging_name() # Tag to output index map used to dispatch the side output values emitted # by the DoFn function to the appropriate receivers. The main output is # tagged with None and is associated with its corresponding index. self.tagged_receivers = _TaggedReceivers( self.counter_factory, self.name_context.logging_name()) output_tag_prefix = PropertyNames.OUT + '_' for index, tag in enumerate(self.spec.output_tags): if tag == PropertyNames.OUT: original_tag = None # type: Optional[str] elif tag.startswith(output_tag_prefix): original_tag = tag[len(output_tag_prefix):] else: raise ValueError('Unexpected output name for operation: %s' % tag) self.tagged_receivers[original_tag] = self.receivers[index] if self.user_state_context: self.user_state_context.update_timer_receivers(self.tagged_receivers) self.timer_specs = { spec.name: spec for spec in userstate.get_dofn_specs(fn)[1] } if self.side_input_maps is None: if tags_and_types: self.side_input_maps = list(self._read_side_inputs(tags_and_types)) else: self.side_input_maps = [] self.dofn_runner = common.DoFnRunner( fn, args, kwargs, self.side_input_maps, window_fn, tagged_receivers=self.tagged_receivers, step_name=self.name_context.logging_name(), state=state, user_state_context=self.user_state_context, operation_name=self.name_context.metrics_name()) self.dofn_runner.setup() self.dofn_receiver = (self.dofn_runner if isinstance(self.dofn_runner, Receiver) else DoFnRunnerReceiver(self.dofn_runner))
727	[ 0.024066589772701263, 0.009274031966924667, -0.00447470648214221, -0.027581430971622467, -0.02266736887395382, -0.00695692328736186, 0.02845454402267933, -0.008675165474414825, -0.020988304167985916, -0.01381310261785984, -0.013107895851135254, -0.019152527675032616, 0.019208496436476707, ...	13	{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "transform_id", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "transform_id", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": 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{"_type": "Name", "_fields": {"id": "metric", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "int64_distribution", "value": {"_type": "Name", "_fields": {"id": "monitoring_infos", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "ptransform", "value": {"_type": "Name", "_fields": {"id": "transform_id", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "tag", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "tag", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "str", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "sampled_byte_count", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "sampled_byte_count", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "sampled_byte_count", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "to_key", "value": {"_type": "Name", "_fields": {"id": "monitoring_infos", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "value": {"_type": "Name", "_fields": {"id": "infos", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "tagged_receivers", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "tag", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "receiver", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "tagged_receivers", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "infos", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "monitoring_infos", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def monitoring_infos(self, transform_id): # type: (str) -> Dict[FrozenSet, metrics_pb2.MonitoringInfo] infos = super(DoOperation, self).monitoring_infos(transform_id) if self.tagged_receivers: for tag, receiver in self.tagged_receivers.items(): mi = monitoring_infos.int64_counter( monitoring_infos.ELEMENT_COUNT_URN, receiver.opcounter.element_counter.value(), ptransform=transform_id, tag=str(tag) ) infos[monitoring_infos.to_key(mi)] = mi (unused_mean, sum, count, min, max) = ( receiver.opcounter.mean_byte_counter.value()) metric = metrics_pb2.Metric( distribution_data=metrics_pb2.DistributionData( int_distribution_data=metrics_pb2.IntDistributionData( count=count, sum=sum, min=min, max=max ) ) ) sampled_byte_count = monitoring_infos.int64_distribution( monitoring_infos.SAMPLED_BYTE_SIZE_URN, metric, ptransform=transform_id, tag=str(tag) ) infos[monitoring_infos.to_key(sampled_byte_count)] = sampled_byte_count return infos
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"_fields": {}}}}}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "mean", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "AugAssign", "_fields": {"op": {"_type": "Add", "_fields": {}}, "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "mean", "ctx": {"_type": "Load", "_fields": {}}}}}}, "target": {"_type": "Name", "_fields": {"id": "total", "ctx": {"_type": "Store", "_fields": {}}}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": []}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "values", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "tagged_receivers", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "receiver", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "total", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "_total_output_bytes", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "SdfProcessSizedElements", "bases": [{"_type": "Name", "_fields": {"id": "DoOperation", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class SdfProcessSizedElements(DoOperation): def __init__(self, args, kwargs): super(SdfProcessSizedElements, self).__init__(args, *kwargs) self.lock = threading.RLock() self.element_start_output_bytes = None def process(self, o): # type: (WindowedValue) -> None with self.scoped_process_state: try: with self.lock: self.element_start_output_bytes = self._total_output_bytes() for receiver in self.tagged_receivers.values(): receiver.opcounter.restart_sampling() # Actually processing the element can be expensive; do it without # the lock. delayed_application = self.dofn_runner.process_with_sized_restriction(o) if delayed_application: self.execution_context.delayed_applications.append( (self, delayed_application)) finally: with self.lock: self.element_start_output_bytes = None def try_split(self, fraction_of_remainder): split = self.dofn_runner.try_split(fraction_of_remainder) if split: primary, residual = split return (self, primary), (self, residual) def current_element_progress(self): with self.lock: if self.element_start_output_bytes is not None: progress = self.dofn_runner.current_element_progress() if progress is not None: return progress.with_completed( self._total_output_bytes() - self.element_start_output_bytes) def progress_metrics(self): # type: () -> beam_fn_api_pb2.Metrics.PTransform with self.lock: metrics = super(SdfProcessSizedElements, self).progress_metrics() current_element_progress = self.current_element_progress() if current_element_progress: metrics.active_elements.measured.input_element_counts[ self.input_info[1]] = 1 metrics.active_elements.fraction_remaining = ( current_element_progress.fraction_remaining) return metrics def _total_output_bytes(self): total = 0 for receiver in self.tagged_receivers.values(): elements = receiver.opcounter.element_counter.value() if elements > 0: mean = (receiver.opcounter.mean_byte_counter.value())[0] total += elements mean return total
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"attr": "values", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "tagged_receivers", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "receiver", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lock", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "o", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "process_with_sized_restriction", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dofn_runner", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "delayed_application", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "delayed_application", "ctx": {"_type": "Load", "_fields": {}}}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "delayed_applications", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "execution_context", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}}}], "test": {"_type": "Name", "_fields": {"id": "delayed_application", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": []}}], "orelse": [], "handlers": [], "finalbody": [{"_type": "With", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "element_start_output_bytes", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lock", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}]}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scoped_process_state", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "optional_vars": null}}], "type_comment": null}}], "name": "process", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def process(self, o): # type: (WindowedValue) -> None with self.scoped_process_state: try: with self.lock: self.element_start_output_bytes = self._total_output_bytes() for receiver in self.tagged_receivers.values(): receiver.opcounter.restart_sampling() # Actually processing the element can be expensive; do it without # the lock. delayed_application = self.dofn_runner.process_with_sized_restriction(o) if delayed_application: self.execution_context.delayed_applications.append( (self, delayed_application)) finally: with self.lock: self.element_start_output_bytes = None
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"ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "output_key", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "PGBKCVOperation", "bases": [{"_type": "Name", "_fields": {"id": "Operation", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class PGBKCVOperation(Operation): def __init__(self, name_context, spec, counter_factory, state_sampler): super(PGBKCVOperation, self).__init__( name_context, spec, counter_factory, state_sampler) # Combiners do not accept deferred side-inputs (the ignored fourth # argument) and therefore the code to handle the extra args/kwargs is # simpler than for the DoFn's of ParDo. fn, args, kwargs = pickler.loads(self.spec.combine_fn)[:3] self.combine_fn = curry_combine_fn(fn, args, kwargs) self.combine_fn_add_input = self.combine_fn.add_input base_compact = ( core.CombineFn.compact if sys.version_info >= (3,) else core.CombineFn.compact.__func__) if self.combine_fn.compact.__func__ is base_compact: self.combine_fn_compact = None else: self.combine_fn_compact = self.combine_fn.compact # Optimization for the (known tiny accumulator, often wide keyspace) # combine functions. # TODO(b/36567833): Bound by in-memory size rather than key count. self.max_keys = ( 1000 * 1000 if isinstance(fn, (combiners.CountCombineFn, combiners.MeanCombineFn)) or # TODO(b/36597732): Replace this 'or' part by adding the 'cy' optimized # combiners to the short list above. (isinstance(fn, core.CallableWrapperCombineFn) and fn._fn in (min, max, sum)) else 100 * 1000) # pylint: disable=protected-access self.key_count = 0 self.table = {} def process(self, wkv): # type: (WindowedValue) -> None with self.scoped_process_state: key, value = wkv.value # pylint: disable=unidiomatic-typecheck # Optimization for the global window case. if len(wkv.windows) == 1 and type(wkv.windows[0]) is _global_window_type: wkey = 0, key # type: Tuple[Hashable, Any] else: wkey = tuple(wkv.windows), key entry = self.table.get(wkey, None) if entry is None: if self.key_count >= self.max_keys: target = self.key_count * 9 // 10 old_wkeys = [] # TODO(robertwb): Use an LRU cache? for old_wkey, old_wvalue in self.table.items(): old_wkeys.append(old_wkey) # Can't mutate while iterating. self.output_key(old_wkey, old_wvalue[0]) self.key_count -= 1 if self.key_count <= target: break for old_wkey in reversed(old_wkeys): del self.table[old_wkey] self.key_count += 1 # We save the accumulator as a one element list so we can efficiently # mutate when new values are added without searching the cache again. entry = self.table[wkey] = [self.combine_fn.create_accumulator()] entry[0] = self.combine_fn_add_input(entry[0], value) def finish(self): for wkey, value in self.table.items(): self.output_key(wkey, value[0]) self.table = {} self.key_count = 0 def output_key(self, wkey, accumulator): windows, key = wkey if self.combine_fn_compact is None: value = accumulator else: value = self.combine_fn_compact(accumulator) if windows == 0: self.output(_globally_windowed_value.with_value((key, value))) else: self.output( WindowedValue((key, value), windows[0].max_timestamp(), windows))
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self.table.items(): old_wkeys.append(old_wkey) # Can't mutate while iterating. self.output_key(old_wkey, old_wvalue[0]) self.key_count -= 1 if self.key_count <= target: break for old_wkey in reversed(old_wkeys): del self.table[old_wkey] self.key_count += 1 # We save the accumulator as a one element list so we can efficiently # mutate when new values are added without searching the cache again. entry = self.table[wkey] = [self.combine_fn.create_accumulator()] entry[0] = self.combine_fn_add_input(entry[0], value)
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"type_ignores": []}}	def __init__(self, name_context, spec, counter_factory, state_sampler): super(PGBKCVOperation, self).__init__( name_context, spec, counter_factory, state_sampler) # Combiners do not accept deferred side-inputs (the ignored fourth # argument) and therefore the code to handle the extra args/kwargs is # simpler than for the DoFn's of ParDo. fn, args, kwargs = pickler.loads(self.spec.combine_fn)[:3] self.combine_fn = curry_combine_fn(fn, args, kwargs) self.combine_fn_add_input = self.combine_fn.add_input base_compact = ( core.CombineFn.compact if sys.version_info >= (3,) else core.CombineFn.compact.__func__) if self.combine_fn.compact.__func__ is base_compact: self.combine_fn_compact = None else: self.combine_fn_compact = self.combine_fn.compact # Optimization for the (known tiny accumulator, often wide keyspace) # combine functions. # TODO(b/36567833): Bound by in-memory size rather than key count. self.max_keys = ( 1000 * 1000 if isinstance(fn, (combiners.CountCombineFn, combiners.MeanCombineFn)) or # TODO(b/36597732): Replace this 'or' part by adding the 'cy' optimized # combiners to the short list above. (isinstance(fn, core.CallableWrapperCombineFn) and fn._fn in (min, max, sum)) else 100 * 1000) # pylint: disable=protected-access self.key_count = 0 self.table = {}
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[]}}	def output_key(self, wkey, accumulator): windows, key = wkey if self.combine_fn_compact is None: value = accumulator else: value = self.combine_fn_compact(accumulator) if windows == 0: self.output(_globally_windowed_value.with_value((key, value))) else: self.output( WindowedValue((key, value), windows[0].max_timestamp(), windows))
743	[ 0.05547597259283066, 0.04574880003929138, 0.03577733784914017, -0.0031008135993033648, -0.019898507744073868, -0.005896265152841806, 0.0050107152201235294, 0.008705596439540386, -0.02126430906355381, -0.03009204939007759, 0.006279355846345425, -0.028470855206251144, 0.013924513012170792, -...	9	{"_type": "Module", "_fields": {"body": [{"_type": "ClassDef", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "quota_set"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TemplateElement", "value": {"_type": "Name", "_fields": {"id": "xmlutil", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "selector", "value": {"_type": "Constant", "_fields": {"kind": null, "value": "quota_set"}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "id"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set", "value": {"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "For", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "resource", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "SubTemplateElement", "value": {"_type": "Name", "_fields": {"id": "xmlutil", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "elem", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "resource", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "text", "value": {"_type": "Name", "_fields": {"id": "elem", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "iter": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "resources", "value": {"_type": "Name", "_fields": {"id": "QUOTAS", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "resource", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": 1}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "MasterTemplate", "value": {"_type": "Name", "_fields": {"id": "xmlutil", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "construct", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "QuotaTemplate", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TemplateBuilder", "value": {"_type": "Name", "_fields": {"id": "xmlutil", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class QuotaTemplate(xmlutil.TemplateBuilder): def construct(self): root = xmlutil.TemplateElement('quota_set', selector='quota_set') root.set('id') for resource in QUOTAS.resources: elem = xmlutil.SubTemplateElement(root, resource) elem.text = resource return xmlutil.MasterTemplate(root, 1)
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{"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "disable_losefocus_temporary", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Delete", "_fields": {"targets": [{"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Del", "_fields": {}}}}]}}], "name": "close", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", 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{"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "horizontal-position"}}, {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "h_align", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dialog", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 50}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "hide-tab-bar"}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "chk_hide_tab_bar", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "hide-tab-bar-fullscreen"}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "chk_hide_tab_bar_fullscreen", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "update_ui", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "disable_losefocus_temporary", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "close", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "update", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "WinDialog", "bases": [], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class WinDialog: def __init__(self, sender, active_terminal): win_pref_ui_file = os.path.join(TerraHandler.get_resources_path(), 'win_pref.ui') if not os.path.exists(win_pref_ui_file): msg = t('UI data file is missing: {}') sys.exit(msg.format(win_pref_ui_file)) ConfigManager.disable_losefocus_temporary = True self.sender = sender self.active_terminal = active_terminal self.builder = Gtk.Builder() self.builder.set_translation_domain('terra') self.builder.add_from_file(win_pref_ui_file) self.dialog = self.builder.get_object('win_dialog') self.window = self.sender.get_container().parent self.dialog.v_align = self.builder.get_object('v_align') self.dialog.v_align.set_active(int(ConfigManager.get_conf(self.window.name, 'vertical-position')) / 50) self.dialog.h_align = self.builder.get_object('h_align') self.dialog.h_align.set_active(int(ConfigManager.get_conf(self.window.name, 'horizontal-position')) / 50) self.chk_hide_tab_bar = self.builder.get_object('chk_hide_tab_bar') self.chk_hide_tab_bar.set_active(ConfigManager.get_conf(self.window.name, 'hide-tab-bar')) self.chk_hide_tab_bar_fullscreen = self.builder.get_object('chk_hide_tab_bar_fullscreen') self.chk_hide_tab_bar_fullscreen.set_active(ConfigManager.get_conf(self.window.name, 'hide-tab-bar-fullscreen')) self.dialog.btn_cancel = self.builder.get_object('btn_cancel') self.dialog.btn_ok = self.builder.get_object('btn_ok') self.dialog.btn_cancel.connect('clicked', lambda w: self.close()) self.dialog.btn_ok.connect('clicked', lambda w: self.update()) self.dialog.connect('delete-event', lambda w, x: self.close()) self.dialog.connect('destroy', lambda w: self.close()) self.dialog.show_all() def on_keypress(self, widget, event): if Gdk.keyval_name(event.keyval) == 'Return': self.update() def close(self): self.dialog.destroy() self.active_terminal.grab_focus() ConfigManager.disable_losefocus_temporary = False del self def update(self): ConfigManager.set_conf(self.window.name, 'vertical-position', self.dialog.v_align.get_active() * 50) ConfigManager.set_conf(self.window.name, 'horizontal-position', self.dialog.h_align.get_active() * 50) ConfigManager.set_conf(self.window.name, 'hide-tab-bar', self.chk_hide_tab_bar.get_active()) ConfigManager.set_conf(self.window.name, 'hide-tab-bar-fullscreen', self.chk_hide_tab_bar_fullscreen.get_active()) self.window.update_ui() ConfigManager.disable_losefocus_temporary = False self.close()
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"attr": "dialog", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "__init__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def __init__(self, sender, active_terminal): win_pref_ui_file = os.path.join(TerraHandler.get_resources_path(), 'win_pref.ui') if not os.path.exists(win_pref_ui_file): msg = t('UI data file is missing: {}') sys.exit(msg.format(win_pref_ui_file)) ConfigManager.disable_losefocus_temporary = True self.sender = sender self.active_terminal = active_terminal self.builder = Gtk.Builder() self.builder.set_translation_domain('terra') self.builder.add_from_file(win_pref_ui_file) self.dialog = self.builder.get_object('win_dialog') self.window = self.sender.get_container().parent self.dialog.v_align = self.builder.get_object('v_align') self.dialog.v_align.set_active(int(ConfigManager.get_conf(self.window.name, 'vertical-position')) / 50) self.dialog.h_align = self.builder.get_object('h_align') self.dialog.h_align.set_active(int(ConfigManager.get_conf(self.window.name, 'horizontal-position')) / 50) self.chk_hide_tab_bar = self.builder.get_object('chk_hide_tab_bar') self.chk_hide_tab_bar.set_active(ConfigManager.get_conf(self.window.name, 'hide-tab-bar')) self.chk_hide_tab_bar_fullscreen = self.builder.get_object('chk_hide_tab_bar_fullscreen') self.chk_hide_tab_bar_fullscreen.set_active(ConfigManager.get_conf(self.window.name, 'hide-tab-bar-fullscreen')) self.dialog.btn_cancel = self.builder.get_object('btn_cancel') self.dialog.btn_ok = self.builder.get_object('btn_ok') self.dialog.btn_cancel.connect('clicked', lambda w: self.close()) self.dialog.btn_ok.connect('clicked', lambda w: self.update()) self.dialog.connect('delete-event', lambda w, x: self.close()) self.dialog.connect('destroy', lambda w: self.close()) self.dialog.show_all()
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"Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "horizontal-position"}}, {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "h_align", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dialog", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 50}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "hide-tab-bar"}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "chk_hide_tab_bar", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "hide-tab-bar-fullscreen"}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_active", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "chk_hide_tab_bar_fullscreen", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "set_conf", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "update_ui", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "window", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "disable_losefocus_temporary", "value": {"_type": "Name", "_fields": {"id": "ConfigManager", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "close", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "update", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def update(self): ConfigManager.set_conf(self.window.name, 'vertical-position', self.dialog.v_align.get_active() * 50) ConfigManager.set_conf(self.window.name, 'horizontal-position', self.dialog.h_align.get_active() * 50) ConfigManager.set_conf(self.window.name, 'hide-tab-bar', self.chk_hide_tab_bar.get_active()) ConfigManager.set_conf(self.window.name, 'hide-tab-bar-fullscreen', self.chk_hide_tab_bar_fullscreen.get_active()) self.window.update_ui() ConfigManager.disable_losefocus_temporary = False self.close()
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"_fields": {"id": "diff", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "16.7-Square-mi (43.3 square km)"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "16,500-sq. mi"}}, "value": {"_type": "Name", "_fields": {"id": "diff", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "16,500-sq. mi (42,735 square km)"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "body_html"}}, "value": {"_type": "Name", "_fields": {"id": "res", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "body_html"}}, "value": {"_type": "Name", "_fields": {"id": "item", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_f_to_c", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "AreaTestCase", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TestCase", "value": {"_type": "Name", "_fields": {"id": "unittest", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class AreaTestCase(unittest.TestCase): def test_f_to_c(self): text = ( "Total area is 100.50 square miles for this land. " "Total area is 15.7 square mi for this land. " "Total area is 1 Square Mile for this land. " "Total area is 1-16 sq-mi for this land. " "Total area is 16.7-Square-mi for this land. " "Total area is 16,500-sq. mi for this land. " ) item = {"body_html": text} res, diff = square_mile_to_metric(item) self.assertEqual(diff["100.50 square miles"], "100.50 square miles (260.3 square km)") self.assertEqual(diff["15.7 square mi"], "15.7 square mi (40.7 square km)") self.assertEqual(diff["1 Square Mile"], "1 Square Mile (259 ha)") self.assertEqual(diff["1-16 sq-mi"], "1-16 sq-mi (2.6-41.4 square km)") self.assertEqual(diff["16.7-Square-mi"], "16.7-Square-mi (43.3 square km)") self.assertEqual(diff["16,500-sq. mi"], "16,500-sq. mi (42,735 square km)") self.assertEqual(res["body_html"], item["body_html"])
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test_f_to_c(self): text = ( "Total area is 100.50 square miles for this land. " "Total area is 15.7 square mi for this land. " "Total area is 1 Square Mile for this land. " "Total area is 1-16 sq-mi for this land. " "Total area is 16.7-Square-mi for this land. " "Total area is 16,500-sq. mi for this land. " ) item = {"body_html": text} res, diff = square_mile_to_metric(item) self.assertEqual(diff["100.50 square miles"], "100.50 square miles (260.3 square km)") self.assertEqual(diff["15.7 square mi"], "15.7 square mi (40.7 square km)") self.assertEqual(diff["1 Square Mile"], "1 Square Mile (259 ha)") self.assertEqual(diff["1-16 sq-mi"], "1-16 sq-mi (2.6-41.4 square km)") self.assertEqual(diff["16.7-Square-mi"], "16.7-Square-mi (43.3 square km)") self.assertEqual(diff["16,500-sq. mi"], "16,500-sq. mi (42,735 square km)") self.assertEqual(res["body_html"], item["body_html"])
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[]}}	class RhsmRepositoryReleaseModuleTestCase(ModuleTestCase): module = rhsm_release def setUp(self): super(RhsmRepositoryReleaseModuleTestCase, self).setUp() # Mainly interested that the subscription-manager calls are right # based on the module args, so patch out run_command in the module. # returns (rc, out, err) structure self.mock_run_command = patch('ansible.modules.packaging.os.rhsm_release.' 'AnsibleModule.run_command') self.module_main_command = self.mock_run_command.start() # Module does a get_bin_path check before every run_command call self.mock_get_bin_path = patch('ansible.modules.packaging.os.rhsm_release.' 'AnsibleModule.get_bin_path') self.get_bin_path = self.mock_get_bin_path.start() self.get_bin_path.return_value = '/testbin/subscription-manager' def tearDown(self): self.mock_run_command.stop() self.mock_get_bin_path.stop() super(RhsmRepositoryReleaseModuleTestCase, self).tearDown() def module_main(self, exit_exc): with self.assertRaises(exit_exc) as exc: self.module.main() return exc.exception.args[0] def test_release_set(self): # test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': '7.5'}) self.module_main_command.side_effect = [ # first call, get_release: returns different version so set_release is called (0, '7.4', ''), # second call, set_release: just needs to exit with 0 rc (0, '', ''), ] result = self.module_main(AnsibleExitJson) self.assertTrue(result['changed']) self.assertEqual('7.5', result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), call('/testbin/subscription-manager release --set 7.5', check_rc=True), ]) def test_release_set_idempotent(self): # test that the module does not attempt to change the release when # the current release matches the user-specified target release set_module_args({'release': '7.5'}) self.module_main_command.side_effect = [ # first call, get_release: returns same version, set_release is not called (0, '7.5', ''), ] result = self.module_main(AnsibleExitJson) self.assertFalse(result['changed']) self.assertEqual('7.5', result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), ]) def test_release_unset(self): # test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': None}) self.module_main_command.side_effect = [ # first call, get_release: returns version so set_release is called (0, '7.5', ''), # second call, set_release: just needs to exit with 0 rc (0, '', ''), ] result = self.module_main(AnsibleExitJson) self.assertTrue(result['changed']) self.assertIsNone(result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), call('/testbin/subscription-manager release --unset', check_rc=True), ]) def test_release_unset_idempotent(self): # test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': None}) self.module_main_command.side_effect = [ # first call, get_release: returns no version, set_release is not called (0, 'Release not set', ''), ] result = self.module_main(AnsibleExitJson) self.assertFalse(result['changed']) self.assertIsNone(result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), ]) def test_release_insane(self): # test that insane values for release trigger fail_json insane_value = 'this is an insane release value' set_module_args({'release': insane_value}) result = self.module_main(AnsibleFailJson) # also ensure that the fail msg includes the insane value self.assertIn(insane_value, result['msg']) def test_release_matcher(self): # throw a few values at the release matcher -- only sane_values should match sane_values = ['1Server', '10Server', '1.10', '10.0'] insane_values = [ '6server', # lowercase 's' '100Server', # excessively long 'x' component '100.0', # excessively long 'x' component '6.100', # excessively long 'y' component '100.100', # excessively long 'x' and 'y' components ] matches = self.module.release_matcher.findall(' '.join(sane_values + insane_values)) # matches should be returned in the same order they were parsed, # so sorting shouldn't be necessary here self.assertEqual(matches, sane_values)
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test that the module does not attempt to change the release when # the current release matches the user-specified target release set_module_args({'release': '7.5'}) self.module_main_command.side_effect = [ # first call, get_release: returns same version, set_release is not called (0, '7.5', ''), ] result = self.module_main(AnsibleExitJson) self.assertFalse(result['changed']) self.assertEqual('7.5', result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), ])
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{"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "AnsibleExitJson", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module_main", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "changed"}}, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertTrue", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "7.5"}}, {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "current_release"}}, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "/testbin/subscription-manager release --show"}}], "func": {"_type": "Name", "_fields": {"id": "call", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "check_rc", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "/testbin/subscription-manager release --set 7.5"}}], "func": {"_type": "Name", "_fields": {"id": "call", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "check_rc", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_has_calls", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module_main_command", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "test_release_set", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def test_release_set(self): # test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': '7.5'}) self.module_main_command.side_effect = [ # first call, get_release: returns different version so set_release is called (0, '7.4', ''), # second call, set_release: just needs to exit with 0 rc (0, '', ''), ] result = self.module_main(AnsibleExitJson) self.assertTrue(result['changed']) self.assertEqual('7.5', result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), call('/testbin/subscription-manager release --set 7.5', check_rc=True), ])
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test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': None}) self.module_main_command.side_effect = [ # first call, get_release: returns no version, set_release is not called (0, 'Release not set', ''), ] result = self.module_main(AnsibleExitJson) self.assertFalse(result['changed']) self.assertIsNone(result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), ])
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{"_type": "Load", "_fields": {}}}}}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "AnsibleExitJson", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module_main", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "changed"}}, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertTrue", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "current_release"}}, "value": {"_type": "Name", "_fields": {"id": "result", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIsNone", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "/testbin/subscription-manager release --show"}}], "func": {"_type": "Name", "_fields": {"id": "call", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "check_rc", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "/testbin/subscription-manager release --unset"}}], "func": {"_type": "Name", "_fields": {"id": "call", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "check_rc", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_has_calls", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "module_main_command", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "name": "test_release_unset", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def test_release_unset(self): # test that the module attempts to change the release when the current # release is not the same as the user-specific target release set_module_args({'release': None}) self.module_main_command.side_effect = [ # first call, get_release: returns version so set_release is called (0, '7.5', ''), # second call, set_release: just needs to exit with 0 rc (0, '', ''), ] result = self.module_main(AnsibleExitJson) self.assertTrue(result['changed']) self.assertIsNone(result['current_release']) self.module_main_command.assert_has_calls([ call('/testbin/subscription-manager release --show', check_rc=True), call('/testbin/subscription-manager release --unset', check_rc=True), ])
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null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "flags", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "update", "value": {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "lpcxpresso/%s_project.tmpl"}}, "right": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lower", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "target", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": ".project"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_file", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "lpcxpresso/%s_cproject.tmpl"}}, "right": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lower", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "target", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": ".cproject"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_file", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "generate", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "LPCXpresso", "bases": [{"_type": "Name", "_fields": {"id": "Exporter", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class LPCXpresso(Exporter): NAME = 'LPCXpresso' TOOLCHAIN = 'GCC_ARM' MBED_CONFIG_HEADER_SUPPORTED = True TARGETS = [ 'LPC1768', 'LPC4088', 'LPC4088_DM', 'LPC4330_M4', 'LPC1114', 'LPC11U35_401', 'LPC11U35_501', 'UBLOX_C027', 'ARCH_PRO', 'LPC1549', 'LPC11U68', 'LPCCAPPUCCINO', 'LPC824', 'LPC11U37H_401', ] def generate(self): libraries = [] for lib in self.resources.libraries: l, _ = splitext(basename(lib)) libraries.append(l[3:]) ctx = { 'name': self.project_name, 'include_paths': self.resources.inc_dirs, 'linker_script': self.resources.linker_script, 'object_files': self.resources.objects, 'libraries': libraries, 'symbols': self.toolchain.get_symbols() } ctx.update(self.flags) self.gen_file('lpcxpresso/%s_project.tmpl' % self.target.lower(), ctx, '.project') self.gen_file('lpcxpresso/%s_cproject.tmpl' % self.target.lower(), ctx, '.cproject')
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{"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "lib", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "basename", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "splitext", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "l", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "_", "ctx": {"_type": "Store", "_fields": {}}}}]}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": {"_type": "Constant", "_fields": {"kind": null, "value": 3}}, "upper": null}}, "value": {"_type": "Name", "_fields": 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"value": "linker_script"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "object_files"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "libraries"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "symbols"}}], "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "project_name", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "inc_dirs", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "resources", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "linker_script", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "resources", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "objects", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "resources", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Name", "_fields": {"id": "libraries", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_symbols", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "toolchain", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "flags", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "update", "value": {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "lpcxpresso/%s_project.tmpl"}}, "right": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lower", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "target", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": ".project"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_file", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "lpcxpresso/%s_cproject.tmpl"}}, "right": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "lower", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "target", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Name", "_fields": {"id": "ctx", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": ".cproject"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "gen_file", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "generate", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def generate(self): libraries = [] for lib in self.resources.libraries: l, _ = splitext(basename(lib)) libraries.append(l[3:]) ctx = { 'name': self.project_name, 'include_paths': self.resources.inc_dirs, 'linker_script': self.resources.linker_script, 'object_files': self.resources.objects, 'libraries': libraries, 'symbols': self.toolchain.get_symbols() } ctx.update(self.flags) self.gen_file('lpcxpresso/%s_project.tmpl' % self.target.lower(), ctx, '.project') self.gen_file('lpcxpresso/%s_cproject.tmpl' % self.target.lower(), ctx, '.cproject')
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vm = None if vm_name: vm = module.get_vm(vapp_name, vm_name) if operation == 'poweron': operation = 'powerOn' elif operation == 'poweroff': operation = 'powerOff' cmd = 'power:%s' % operation module.get_vapp(vapp_name).execute(cmd, 'post', targetVM=vm)
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{"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "action", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "undeploy", "value": {"_type": "Name", "_fields": {"id": "vapp", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "orelse": []}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "state", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "undeployed"}}]}}, "orelse": []}}]}}], "name": "set_state", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def set_state(module): state = module.params['state'] vapp = module.get_vapp(module.params['vapp_name']) if state == 'deployed': 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{"_type": "Load", "_fields": {}}}}}}]}}}}], "name": "main", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def main(): argument_spec = dict( vapp_name=dict(required=True), vdc_name=dict(required=True), template_name=dict(), catalog_name=dict(default='Public Catalog'), network_name=dict(), network_mode=dict(default='pool', choices=['dhcp', 'static', 'pool']), vm_name=dict(), vm_cpus=dict(), vm_memory=dict(), operation=dict(default=DEFAULT_VAPP_OPERATION, choices=VAPP_OPERATIONS), state=dict(default='present', choices=VAPP_STATES) ) module = VcaAnsibleModule(argument_spec=argument_spec, supports_check_mode=True) state = module.params['state'] operation = module.params['operation'] instance = get_instance(module) result = dict(changed=False) if instance and state == 'absent': if not module.check_mode: delete(module) result['changed'] = True elif state != 'absent': if instance['state'] == 'absent': if not module.check_mode: create(module) result['changed'] = True elif instance['state'] != state and state != 'present': if not module.check_mode: set_state(module) result['changed'] = True if operation != instance.get('status') and operation != 'noop': if not module.check_mode: do_operation(module) result['changed'] = True return module.exit(**result)
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{"args": [{"_type": "Name", "_fields": {"id": "datafile", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "r"}}], "func": {"_type": "Name", "_fields": {"id": "open", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "optional_vars": {"_type": "Name", "_fields": {"id": "f", "ctx": {"_type": "Store", "_fields": {}}}}}}], "type_comment": null}}], "name": "build", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def build(self, datafile, continous_features): with open(datafile, 'r') as f: for line in f: features = line.rstrip('\n').split('\t') for i in range(0, self.num_feature): val = features[continous_features[i]] if val != '': val = int(val) if val > continous_clip[i]: val = continous_clip[i] self.min[i] = min(self.min[i], val) self.max[i] = max(self.max[i], val)
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{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "url", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "url", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "resolve", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "source", "bases": [], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class source: def __init__(self): self.priority = 1 self.language = ['en'] self.domains = ['rlslog.me'] self.base_link = 'http://www.rlslog.me' self.search_link = '/?s=%s' self.scraper = cfscrape.create_scraper() def movie(self, imdb, title, localtitle, aliases, year): url = {'imdb': imdb, 'title': title, 'year': year} url = urllib.urlencode(url) return url def tvshow(self, imdb, tvdb, tvshowtitle, localtvshowtitle, aliases, year): url = {'imdb': imdb, 'tvdb': tvdb, 'tvshowtitle': tvshowtitle, 'year': year} url = urllib.urlencode(url) return url def episode(self, url, imdb, tvdb, title, premiered, season, episode): if url == None: return url = urlparse.parse_qs(url) url = dict([(i, url[i][0]) if url[i] else (i, '') for i in url]) url['title'], url['premiered'], url['season'], url['episode'] = title, premiered, season, episode url = urllib.urlencode(url) return url def sources(self, url, hostDict, hostprDict): try: hostDict = hostDict + hostprDict sources = [] query_bases = [] options = [] if url is None: return sources if not debrid.status(): return sources data = urlparse.parse_qs(url) data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data]) title = (data['tvshowtitle'] if 'tvshowtitle' in data else data['title']) hdlr = 'S%02dE%02d' % (int(data['season']), int(data['episode'])) if 'tvshowtitle' in data else data['year'] # tvshowtitle if 'tvshowtitle' in data: query_bases.append('%s ' % (data['tvshowtitle'].replace("-", ""))) if 'year' in data: query_bases.append('%s %s ' % (data['tvshowtitle'], data['year'])) options.append('S%02dE%02d' % (int(data['season']), int(data['episode']))) options.append('S%02d' % (int(data['season']))) else: query_bases.append('%s %s ' % (data['title'], data['year'])) query_bases.append('%s ' % (data['title'])) query_bases.append('2160p') query_bases.append('') for option in options: for query_base in query_bases: q = re.sub('(\\\\|/\| -\|:\|;\|\*\|\?\|"\|\'\|<\|>\|\\|)', '', query_base + option) q = q.replace(" ", " ").replace(" ", "+") url = self.base_link + self.search_link % q html = self.scraper.get(url) if html.status_code == 200: posts = client.parseDOM(html.content, "h3", attrs={"class": "elegantwp-fp09-post-title"}) for post in posts: url = client.parseDOM(post, "a", ret='href') if len(url) > 0: html = self.scraper.get(url[0]) if html.status_code == 200: quotes = client.parseDOM(html.content, "div", attrs={"class": "entry-content " "clearfix"}) log_utils.log('best-moviez.ws - href: \n' + str(quotes)) for quote in quotes: hrefs = client.parseDOM(quote, "a", ret='href') if not hrefs: continue for href in hrefs: if any(x in href for x in ['.rar', '.zip', '.iso']): continue quality = source_utils.check_sd_url(href) href = href.encode('utf-8') valid, host = source_utils.is_host_valid(href, hostDict) if not valid: continue if hdlr in href.upper() and cleantitle.get(title) in cleantitle.get(href): sources.append( {'source': host, 'quality': quality, 'language': 'en', 'url': href, 'direct': False, 'debridonly': False}) if len(sources) > 0: return sources return sources except: return sources def resolve(self, url): return url
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"type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "sources", "ctx": {"_type": "Load", "_fields": {}}}}}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "sources", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": null, "type": null}}], "finalbody": []}}], "name": "sources", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def sources(self, url, hostDict, hostprDict): try: hostDict = hostDict + hostprDict sources = [] query_bases = [] options = [] if url is None: return sources if not debrid.status(): return sources data = urlparse.parse_qs(url) data = dict([(i, data[i][0]) if data[i] else (i, '') for i in data]) title = (data['tvshowtitle'] if 'tvshowtitle' in data else data['title']) hdlr = 'S%02dE%02d' % (int(data['season']), int(data['episode'])) if 'tvshowtitle' in data else data['year'] # tvshowtitle if 'tvshowtitle' in data: query_bases.append('%s ' % (data['tvshowtitle'].replace("-", ""))) if 'year' in data: query_bases.append('%s %s ' % (data['tvshowtitle'], data['year'])) options.append('S%02dE%02d' % (int(data['season']), int(data['episode']))) options.append('S%02d' % (int(data['season']))) else: query_bases.append('%s %s ' % (data['title'], data['year'])) query_bases.append('%s ' % (data['title'])) query_bases.append('2160p') query_bases.append('') for option in options: for query_base in query_bases: q = re.sub('(\\\\|/\| -\|:\|;\|\*\|\?\|"\|\'\|<\|>\|\\|)', '', query_base + option) q = q.replace(" ", " ").replace(" ", "+") url = self.base_link + self.search_link % q html = self.scraper.get(url) if html.status_code == 200: posts = client.parseDOM(html.content, "h3", attrs={"class": "elegantwp-fp09-post-title"}) for post in posts: url = client.parseDOM(post, "a", ret='href') if len(url) > 0: html = self.scraper.get(url[0]) if html.status_code == 200: quotes = client.parseDOM(html.content, "div", attrs={"class": "entry-content " "clearfix"}) log_utils.log('best-moviez.ws - href: \n' + str(quotes)) for quote in quotes: hrefs = client.parseDOM(quote, "a", ret='href') if not hrefs: continue for href in hrefs: if any(x in href for x in ['.rar', '.zip', '.iso']): continue quality = source_utils.check_sd_url(href) href = href.encode('utf-8') valid, host = source_utils.is_host_valid(href, hostDict) if not valid: continue if hdlr in href.upper() and cleantitle.get(title) in cleantitle.get(href): sources.append( {'source': host, 'quality': quality, 'language': 'en', 'url': href, 'direct': False, 'debridonly': False}) if len(sources) > 0: return sources return sources except: return sources
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"value": "value"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": null}}, {"_type": "Name", "_fields": {"id": "val", "ctx": {"_type": "Load", "_fields": {}}}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "add_cookie", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "driver", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "should have thrown exceptions."}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fail", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Pass", "_fields": {}}], "name": null, "type": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "UnableToSetCookieException", "value": {"_type": "Name", "_fields": {"id": "exceptions", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Pass", "_fields": {}}], "name": null, "type": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "InvalidCookieDomainException", "value": {"_type": "Name", "_fields": {"id": "exceptions", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "finalbody": []}}], "name": "test_should_throw_an_exception_the_name_is_null", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "CookieTest", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "WebDriverBaseTest", "value": {"_type": "Name", "_fields": {"id": "base_test", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class CookieTest(base_test.WebDriverBaseTest): def setUp(self): self.driver.get(self.webserver.where_is("cookie/res/cookie_container.html")) def test_can_create_a_well_formed_cookie( self ): name = 'foo' value = 'bar' self.driver.add_cookie({ 'name': name, 'value': value }) def test_cookies_should_allow_secure_to_be_set( self ): name = 'foo' value = 'bar' secure = True self.driver.add_cookie({ 'name': name, 'value': value, 'path': '/', 'secure': secure}) self.assertTrue(self.driver.get_cookie(name)[0]['secure']) def test_secure_defaults_to_false( self ): name = 'foo' value = 'bar' self.driver.add_cookie({ 'name': name, 'value': value}) self.assertFalse(self.driver.get_cookie(name)[0]['secure']) def test_should_throw_an_exception_when_semicolon_exists_in_the_cookie_attribute(self): invalid_name = 'foo;bar' value = 'foobar' try: self.driver.add_cookie({ 'name': invalid_name, 'value': value }) self.fail( 'should have thrown exceptions.' ) except exceptions.UnableToSetCookieException: pass except exceptions.InvalidCookieDomainException: pass def test_should_throw_an_exception_the_name_is_null(self): val = 'foobar' try: self.driver.add_cookie({ 'name': None, 'value': val }) self.fail( 'should have thrown exceptions.' ) except exceptions.UnableToSetCookieException: pass except exceptions.InvalidCookieDomainException: pass
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{"id": "GObject", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class Player (GObject.GObject): __gsignals__ = { 'end': (GObject.SignalFlags.RUN_FIRST, None, ()), 'error': (GObject.SignalFlags.RUN_FIRST, None, (object,)) } def __init__(self): GObject.GObject.__init__(self) self.player = Gst.ElementFactory.make("playbin", "player") fakesink = Gst.ElementFactory.make("fakesink", "fakesink") self.player.set_property("video-sink", fakesink) bus = self.player.get_bus() bus.connect("message", self.onMessage) def onMessage(self, bus, message): if message.type == Gst.MessageType.ERROR: # Sound seams sometimes to work, even though errors are dropped. # Therefore we really can't do anything to test. # self.emit("error", message) self.player.set_state(Gst.State.NULL) simpleMessage, advMessage = message.parse_error() log.warning("Gstreamer error '%s': %s" % (simpleMessage, advMessage)) self._del() elif message.type == Gst.MessageType.EOS: self.player.set_state(Gst.State.NULL) self.emit("end") return True def play(self, uri): self.player.set_state(Gst.State.READY) self.player.set_property("uri", uri) self.player.set_state(Gst.State.PLAYING)
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[{"_type": "Eq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "type", "value": {"_type": "Name", "_fields": {"id": "message", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EOS", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "MessageType", "value": {"_type": "Name", "_fields": {"id": "Gst", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}, "orelse": []}}]}}, {"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}], "name": "onMessage", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def onMessage(self, bus, message): if message.type == Gst.MessageType.ERROR: # Sound seams sometimes to work, even though errors are dropped. # Therefore we really can't do anything to test. # self.emit("error", message) self.player.set_state(Gst.State.NULL) simpleMessage, advMessage = message.parse_error() log.warning("Gstreamer error '%s': %s" % (simpleMessage, advMessage)) self._del() elif message.type == Gst.MessageType.EOS: self.player.set_state(Gst.State.NULL) self.emit("end") return True
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[(k,k) for k in prefix_defaults.keys()] setattr(EmailForm, 'location_type', SelectField('Ort', choices=choices)) form = EmailForm() if form.validate_on_submit(): # mesh ips - 3 for dualband (2x wifi + 1x lan) else 2 mesh_num = 3 if router['dualband'] else 2 pool_mesh = current_app.config['API_POOL_MESH'] prefixes_mesh = [(pool_mesh, 32)]*mesh_num # hna network pool_hna = current_app.config['API_POOL_HNA'] prefixes_hna = [(pool_hna, prefix_defaults[form.location_type.data])] r = request_create(form.hostname.data, form.email.data, prefixes_mesh + prefixes_hna, [], router_id = router_id) try: url = url_for(".wizard_activate", request_id=r.id, signed_token=r.token_activation, _external=True) subject = "[Freifunk Berlin] Aktivierung - %s" % r.name data = { 'name': r.name, 'router': r.router['name'], 'url': url } send_email(r.email, subject, "activation.txt", data) except: # if send_mail fails we delete the already saved request db.session.delete(r) db.session.commit() raise return render_template('confirmation.html') return render_template('wizard/form.html', form = form, router = router)
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"value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "ListComp", "_fields": {"elt": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_parse_element", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "extend", "value": {"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "__getitem__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "element", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "id"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findtext", "value": {"_type": "Name", "_fields": {"id": "element", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "name"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findtext", "value": {"_type": "Name", "_fields": {"id": "element", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "Artist", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "_parse_element", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "SimilarDocumentSet", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "DocumentSet", "value": {"_type": "Name", "_fields": {"id": "document", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class SimilarDocumentSet(document.DocumentSet): def __init__(self, identifier): super(SimilarDocumentSet, self).__init__(identifier, 'get_similar', 'similar/artist') def __len__(self): if self._len is None: self._len = 15 return self._len def __getitem__(self, k): if isinstance(k, (int, long)): if k > 15: raise util.EchoNestAPIError(5, 'Invalid parameter: "rows" must be less than or equal to 15') return self._parse_element(self._cache[0].findall(self.element_path)[k]) elif not isinstance(k, slice): raise TypeError if k.stop > 15: raise util.EchoNestAPIError(5, 'Invalid parameter: "rows" must be less than or equal to 15') start = k.start or 0 stop = k.stop or len(self) items = [] elements = self._cache[0].findall(self.element_path)[start:stop] items.extend([self._parse_element(e) for e in elements]) return items def _parse_element(self, element): return Artist(element.findtext('id'), element.findtext('name'));
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"_fields": {}}, "attr": "side_effect", "value": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "keyword", "_fields": {"arg": "priority", "value": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "n", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}, "right": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "priority", "value": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}]}}}}}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "new_commands", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "enumerate", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "n", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "new_command", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}], "iter": {"_type": "Name", "_fields": {"id": "rules", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "name": "make_corrected_commands", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def make_corrected_commands(command, rules, settings): for rule in rules: new_commands = rule.get_new_command(command, settings) if not isinstance(new_commands, list): new_commands = [new_commands] for n, new_command in enumerate(new_commands): yield types.CorrectedCommand(script=new_command, side_effect=rule.side_effect, priority=(n + 1) * rule.priority)
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"Invalid parameter: \"rows\" must be less than or equal to 15"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "EchoNestAPIError", "value": {"_type": "Name", "_fields": {"id": "util", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "cause": null}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "k", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 15}}]}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "k", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "element_path", "value": {"_type": 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"Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}, "targets": [{"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Slice", "_fields": {"step": null, "lower": {"_type": "Name", "_fields": {"id": "start", "ctx": {"_type": "Load", "_fields": {}}}}, "upper": {"_type": "Name", "_fields": {"id": "stop", "ctx": {"_type": "Load", "_fields": {}}}}}}, "value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "element_path", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "findall", "value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": 0}}, "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_cache", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}, "keywords": []}}}}, "targets": [{"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "ListComp", "_fields": {"elt": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_parse_element", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "elements", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "extend", "value": {"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "__getitem__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def __getitem__(self, k): if isinstance(k, (int, long)): if k > 15: raise util.EchoNestAPIError(5, 'Invalid parameter: "rows" must be less than or equal to 15') return self._parse_element(self._cache[0].findall(self.element_path)[k]) elif not isinstance(k, slice): raise TypeError if k.stop > 15: raise util.EchoNestAPIError(5, 'Invalid parameter: "rows" must be less than or equal to 15') start = k.start or 0 stop = k.stop or len(self) items = [] elements = self._cache[0].findall(self.element_path)[start:stop] items.extend([self._parse_element(e) for e in elements]) return items
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"_fields": {"id": "settings", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_matched_rules", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "matched", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "GeneratorExp", "_fields": {"elt": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Load", "_fields": {}}}}}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "matched", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "rule", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "join", "value": {"_type": "Constant", "_fields": {"kind": null, "value": ", "}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "format", "value": {"_type": "Constant", "_fields": {"kind": "u", "value": "Matched rules: {}"}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "settings", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "debug", "value": {"_type": "Name", "_fields": {"id": "logs", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "command", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "matched", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "settings", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "make_corrected_commands", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "corrected_commands", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "corrected_commands", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "remove_duplicates", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "sorted", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "key", "value": {"_type": "Lambda", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "corrected_command", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "priority", "value": {"_type": "Name", "_fields": {"id": "corrected_command", "ctx": {"_type": "Load", "_fields": {}}}}}}}}}}]}}}}], "name": "get_corrected_commands", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def get_corrected_commands(command, user_dir, settings): rules = get_rules(user_dir, settings) logs.debug( u'Loaded rules: {}'.format(', '.join(rule.name for rule in rules)), settings) matched = get_matched_rules(command, rules, settings) logs.debug( u'Matched rules: {}'.format(', '.join(rule.name for rule in matched)), settings) corrected_commands = make_corrected_commands(command, matched, settings) return sorted(remove_duplicates(corrected_commands), key=lambda corrected_command: corrected_command.priority)
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"type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Lt", "_fields": {}}], "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Sub", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "x_num", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "y_num", "ctx": {"_type": "Load", "_fields": {}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": [{"_type": "Return", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}]}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "IsNot", 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[{"_type": "Name", "_fields": {"id": "y", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "to_string", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}]}}}}}}], "test": {"_type": "Name", "_fields": {"id": "IS_PY3K", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "x", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "to_string", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "y", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "to_string", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "cmp", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}], "name": null, "type": null}}], "finalbody": []}}], "name": "compare_object_attrs", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def compare_object_attrs(x, y): try: if x == y: return 0 x_num = to_number(x) y_num = to_number(y) if x_num is not None and y_num is not None: if x_num - y_num<0: return -1 else: return 1 if '__len__' == x: return -1 if '__len__' == y: return 1 return x.__cmp__(y) except: if IS_PY3K: return (to_string(x) > to_string(y)) - (to_string(x) < to_string(y)) else: return cmp(to_string(x), to_string(y))
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"_fields": {}}, "attr": "__name__", "value": {"_type": "Name", "_fields": {"id": "first_arg_class", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "clsname", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Name", "_fields": {"id": "func_code", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "func_code", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "code", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}, "orelse": []}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "first_arg_class", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "func_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "hasattr", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": []}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "first_arg_name", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "f_locals", "value": {"_type": "Name", "_fields": {"id": "frame", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": []}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "co_varnames", "value": {"_type": "Name", "_fields": {"id": "code", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "len", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "clsname", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "get_clsname_for_code", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def get_clsname_for_code(code, frame): clsname = None if len(code.co_varnames) > 0: # We are checking the first argument of the function # (`self` or `cls` for methods). first_arg_name = code.co_varnames[0] if first_arg_name in frame.f_locals: first_arg_obj = frame.f_locals[first_arg_name] if inspect.isclass(first_arg_obj): # class method first_arg_class = first_arg_obj else: # instance method first_arg_class = first_arg_obj.__class__ func_name = code.co_name if hasattr(first_arg_class, func_name): method = getattr(first_arg_class, func_name) func_code = None if hasattr(method, 'func_code'): # Python2 func_code = method.func_code elif hasattr(method, '__code__'): # Python3 func_code = method.__code__ if func_code and func_code == code: clsname = first_arg_class.__name__ return clsname
792	[ 0.029915468767285347, 0.027882378548383713, -0.006952442228794098, -0.019471678882837296, 0.03524022921919823, 0.03190015256404877, -0.014909327030181885, -0.03475615754723549, -0.009390939958393574, 0.04649483412504196, 0.010480095632374287, -0.07503070682287216, 0.012017014436423779, 0.0...	9	{"_type": "Module", "_fields": {"body": [{"_type": "ClassDef", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "obj", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": {"_type": "arg", "_fields": {"arg": "args", "annotation": null, "type_comment": null}}, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "obj", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": 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{"_type": "Load", "_fields": {}}, "attr": "obj", "value": {"_type": "Name", "_fields": {"id": "other", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "mycmp", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}}}], "name": "__ge__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "other", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Return", "_fields": {"value": {"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "obj", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "obj", "value": {"_type": "Name", "_fields": {"id": "other", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Name", "_fields": {"id": "mycmp", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}]}}}}], "name": "__ne__", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "K", "bases": [{"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}	class K(object): def __init__(self, obj, *args): self.obj = obj def __lt__(self, other): return mycmp(self.obj, other.obj) < 0 def __gt__(self, other): return mycmp(self.obj, other.obj) > 0 def __eq__(self, other): return mycmp(self.obj, other.obj) == 0 def __le__(self, other): return mycmp(self.obj, other.obj) <= 0 def __ge__(self, other): return mycmp(self.obj, other.obj) >= 0 def __ne__(self, other): return mycmp(self.obj, other.obj) != 0
793	[ 0.001978151500225067, -0.020405089482665062, 0.06807985156774521, -0.011360615491867065, 0.019849635660648346, -0.0005931179039180279, 0.004862846340984106, -0.005769390147179365, -0.034249529242515564, -0.0029659171123057604, -0.02399982325732708, -0.052694808691740036, 0.004131847526878118...	11	{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "project_roots_cache", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "pathsep", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "split", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "IDE_PROJECT_ROOTS"}}, {"_type": "Constant", "_fields": {"kind": null, "value": ""}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "getenv", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "roots", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "IDE_PROJECT_ROOTS %s\n"}}, "right": {"_type": "Name", "_fields": {"id": "roots", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "debug", "value": {"_type": "Name", "_fields": {"id": "pydev_log", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}, "targets": [{"_type": "Name", "_fields": {"id": "new_roots", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "normcase", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "path", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Name", "_fields": {"id": "new_roots", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "iter": {"_type": "Name", "_fields": {"id": "roots", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "new_roots", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "append", "value": {"_type": "Name", "_fields": {"id": "project_roots_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Name", "_fields": {"id": "project_roots_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}, "value": {"_type": "Name", "_fields": {"id": "project_roots_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "name": "_get_project_roots", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def _get_project_roots(project_roots_cache=[]): # Note: the project_roots_cache is the same instance among the many calls to the method if not project_roots_cache: roots = os.getenv('IDE_PROJECT_ROOTS', '').split(os.pathsep) pydev_log.debug("IDE_PROJECT_ROOTS %s\n" % roots) new_roots = [] for root in roots: new_roots.append(os.path.normcase(root)) project_roots_cache.append(new_roots) return project_roots_cache[-1]
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Note: the project_roots_cache is the same instance among the many calls to the method if not library_roots_cache: roots = os.getenv('LIBRARY_ROOTS', '').split(os.pathsep) pydev_log.debug("LIBRARY_ROOTS %s\n" % roots) new_roots = [] for root in roots: new_roots.append(os.path.normcase(root)) library_roots_cache.append(new_roots) return library_roots_cache[-1]
795	[ 0.006601160392165184, 0.012297901324927807, 0.0432136133313179, -0.04027976468205452, -0.00689344247803092, 0.010020308196544647, 0.01434938982129097, -0.046147461980581284, -0.034588538110256195, 0.006325422786176205, -0.0018005671445280313, -0.0483974814414978, 0.01645602658390999, 0.026...	12	{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "filename", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "filename_to_not_in_scope_cache", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [{"_type": "Dict", "_fields": {"keys": [], "values": []}}], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Try", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "filename_to_not_in_scope_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "orelse": [], "handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Name", "_fields": {"id": "_get_project_roots", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "project_roots", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}, "targets": [{"_type": "Name", "_fields": {"id": "original_filename", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "abspath", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "path", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "isabs", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "path", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}, {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "<"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "startswith", "value": {"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}, "orelse": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "normcase", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "path", "value": {"_type": "Name", "_fields": {"id": "os", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "original_filename", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "filename_to_not_in_scope_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Break", "_fields": {}}], "test": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": 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"func": {"_type": "Name", "_fields": {"id": "_get_library_roots", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "library_roots", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "original_filename", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "filename_to_not_in_scope_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "test": {"_type": "BoolOp", "_fields": {"op": {"_type": "And", "_fields": {}}, "values": [{"_type": "Compare", "_fields": {"ops": [{"_type": "NotEq", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": ""}}]}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "startswith", "value": {"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}, "orelse": []}}], "iter": {"_type": "Name", "_fields": {"id": "library_roots", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "root", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "original_filename", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "filename_to_not_in_scope_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "Return", "_fields": {"value": {"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "original_filename", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "filename_to_not_in_scope_cache", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "name": null, "type": null}}], "finalbody": []}}], "name": "not_in_project_roots", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}	def not_in_project_roots(filename, filename_to_not_in_scope_cache={}): # Note: the filename_to_not_in_scope_cache is the same instance among the many calls to the method try: return filename_to_not_in_scope_cache[filename] except: project_roots = _get_project_roots() original_filename = filename if not os.path.isabs(filename) and not filename.startswith('<'): filename = os.path.abspath(filename) filename = os.path.normcase(filename) for root in project_roots: if filename.startswith(root): filename_to_not_in_scope_cache[original_filename] = False break else: # for else (only called if the break wasn't reached). filename_to_not_in_scope_cache[original_filename] = True if not filename_to_not_in_scope_cache[original_filename]: # additional check if interpreter is situated in a project directory library_roots = _get_library_roots() for root in library_roots: if root != '' and filename.startswith(root): filename_to_not_in_scope_cache[original_filename] = True # at this point it must be loaded. return filename_to_not_in_scope_cache[original_filename]
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max_steps=3, max_digits=2, rnn_units=256, canvas_size=50, windows_size=28, vae_latent_dimensions=50, vae_recognition_units=(512, 256), vae_generative_units=(256, 512), scale_prior_mean=-1.0, scale_prior_variance=0.1, shift_prior_mean=0.0, shift_prior_variance=1.0, vae_prior_mean=0.0, vae_prior_variance=1.0, vae_likelihood_std=0.3, scale_hidden_units=64, shift_hidden_units=64, z_pres_hidden_units=64, z_pres_prior_log_odds=-2.0, z_pres_temperature=1.0, stopping_threshold=0.99, learning_rate=1e-3, gradient_clipping_norm=100.0, cnn=True, cnn_filters=8, num_summary_images=60, train=False, reuse=False, scope="air", annealing_schedules=None): self.input_images = input_images self.target_num_digits = target_num_digits self.batch_size = tf.shape(input_images)[0] self.max_steps = max_steps self.max_digits = max_digits self.rnn_units = rnn_units self.canvas_size = canvas_size self.windows_size = windows_size self.vae_latent_dimensions = vae_latent_dimensions self.vae_recognition_units = vae_recognition_units self.vae_generative_units = vae_generative_units self.scale_prior_mean = scale_prior_mean self.scale_prior_variance = scale_prior_variance self.shift_prior_mean = shift_prior_mean self.shift_prior_variance = shift_prior_variance self.vae_prior_mean = vae_prior_mean self.vae_prior_variance = vae_prior_variance self.vae_likelihood_std = vae_likelihood_std self.scale_hidden_units = scale_hidden_units self.shift_hidden_units = shift_hidden_units self.z_pres_hidden_units = z_pres_hidden_units self.z_pres_prior_log_odds = z_pres_prior_log_odds self.z_pres_temperature = z_pres_temperature self.stopping_threshold = stopping_threshold self.learning_rate = learning_rate self.gradient_clipping_norm = gradient_clipping_norm self.num_summary_images = num_summary_images self.cnn = cnn self.cnn_filters = cnn_filters self.train = train self.num_summaries = [] self.img_summaries = [] self.var_summaries = [] self.grad_summaries = [] with tf.variable_scope(scope, reuse=reuse): self.global_step = tf.get_variable(name="global_step", shape=[], dtype=tf.int32, initializer=tf.constant_initializer(0), trainable=False) self.scale_prior_log_variance = tf.log(scale_prior_variance, name="scale_prior_log_variance") self.shift_prior_log_variance = tf.log(shift_prior_variance, name="shift_prior_log_variance") self.vae_prior_log_variance = tf.log(vae_prior_variance, name="vae_prior_log_variance") if annealing_schedules is not None: for param, schedule in annealing_schedules.items(): # replacing some of the parameters by annealed # versions, if schedule is provided for those setattr(self, param, self._create_annealed_tensor( param, schedule, self.global_step )) self.rec_num_digits = None self.rec_scales = None self.rec_shifts = None self.reconstruction = None self.loss = None self.accuracy = None self.training = None self._create_model()
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__init__(self, input_images, target_num_digits, max_steps=3, max_digits=2, rnn_units=256, canvas_size=50, windows_size=28, vae_latent_dimensions=50, vae_recognition_units=(512, 256), vae_generative_units=(256, 512), scale_prior_mean=-1.0, scale_prior_variance=0.1, shift_prior_mean=0.0, shift_prior_variance=1.0, vae_prior_mean=0.0, vae_prior_variance=1.0, vae_likelihood_std=0.3, scale_hidden_units=64, shift_hidden_units=64, z_pres_hidden_units=64, z_pres_prior_log_odds=-2.0, z_pres_temperature=1.0, stopping_threshold=0.99, learning_rate=1e-3, gradient_clipping_norm=100.0, cnn=True, cnn_filters=8, num_summary_images=60, train=False, reuse=False, scope="air", annealing_schedules=None): self.input_images = input_images self.target_num_digits = target_num_digits self.batch_size = tf.shape(input_images)[0] self.max_steps = max_steps self.max_digits = max_digits self.rnn_units = rnn_units self.canvas_size = canvas_size self.windows_size = windows_size self.vae_latent_dimensions = vae_latent_dimensions self.vae_recognition_units = vae_recognition_units self.vae_generative_units = vae_generative_units self.scale_prior_mean = scale_prior_mean self.scale_prior_variance = scale_prior_variance self.shift_prior_mean = shift_prior_mean self.shift_prior_variance = shift_prior_variance self.vae_prior_mean = vae_prior_mean self.vae_prior_variance = vae_prior_variance self.vae_likelihood_std = vae_likelihood_std self.scale_hidden_units = scale_hidden_units self.shift_hidden_units = shift_hidden_units self.z_pres_hidden_units = z_pres_hidden_units self.z_pres_prior_log_odds = z_pres_prior_log_odds self.z_pres_temperature = z_pres_temperature self.stopping_threshold = stopping_threshold self.learning_rate = learning_rate self.gradient_clipping_norm = gradient_clipping_norm self.num_summary_images = num_summary_images self.cnn = cnn self.cnn_filters = cnn_filters self.train = train self.num_summaries = [] self.img_summaries = [] self.var_summaries = [] self.grad_summaries = [] with tf.variable_scope(scope, reuse=reuse): self.global_step = tf.get_variable(name="global_step", shape=[], dtype=tf.int32, initializer=tf.constant_initializer(0), trainable=False) self.scale_prior_log_variance = tf.log(scale_prior_variance, name="scale_prior_log_variance") self.shift_prior_log_variance = tf.log(shift_prior_variance, name="shift_prior_log_variance") self.vae_prior_log_variance = tf.log(vae_prior_variance, name="vae_prior_log_variance") if annealing_schedules is not None: for param, schedule in annealing_schedules.items(): # replacing some of the parameters by annealed # versions, if schedule is provided for those setattr(self, param, self._create_annealed_tensor( param, schedule, self.global_step )) self.rec_num_digits = None self.rec_scales = None self.rec_shifts = None self.reconstruction = None self.loss = None self.accuracy = None self.training = None self._create_model() @staticmethod def _create_annealed_tensor(param, schedule, global_step, eps=10e-10): value = tf.train.exponential_decay( learning_rate=schedule["init"], global_step=global_step, decay_steps=schedule["iters"], decay_rate=schedule["factor"], staircase=False if "staircase" not in schedule else schedule["staircase"], name=param ) if "min" in schedule: value = tf.maximum( value, schedule["min"], name=param + "_max" ) if "max" in schedule: value = tf.minimum( value, schedule["max"], name=param + "_min" ) if "log" in schedule and schedule["log"]: value = tf.log( value + eps, name=param + "_log" ) return value @staticmethod def _sample_from_mvn(mean, diag_variance): # sampling from the multivariate normal # with given mean and diagonal covaraince standard_normal = tf.random_normal(tf.shape(mean)) return mean + standard_normal * tf.sqrt(diag_variance) @staticmethod def _draw_colored_bounding_boxes(images, boxes, steps): channels = [images, images, images] for s in range(3): # empty canvas with s-th bounding box step_box = tf.expand_dims(boxes[:, s, :, :], 3) for c in range(3): if s == c: # adding the box to c-th channel # if the number of attention steps is greater than s channels[c] = tf.where( tf.greater(steps, s), tf.minimum(channels[c] + step_box, tf.ones_like(images)), channels[c] ) else: # subtracting the box from channels other than c-th # if the number of attention steps is greater than s channels[c] = tf.where( tf.greater(steps, s), tf.maximum(channels[c] - step_box, tf.zeros_like(images)), channels[c] ) # concatenating all three channels to obtain # potentially three R, G, and B bounding boxes return tf.concat(channels, axis=3) def _summarize_by_digit_count(self, tensor, digits, name): # converting to float in case of int tensors float_tensor = tf.cast(tensor, tf.float32) for i in range(self.max_digits+1): # summarizing the scalar for only those # images that have exactly i digits self.num_summaries.append( tf.summary.scalar( name + "_" + str(i) + "_dig", tf.reduce_mean(tf.boolean_mask( float_tensor, tf.equal(digits, i) )) ) ) # summarizing the scalar for all images self.num_summaries.append( tf.summary.scalar( name + "_all_dig", tf.reduce_mean(float_tensor) ) ) def _summarize_by_step(self, tensor, steps, name, one_more_step=False, all_steps=False): # padding (if required) the number of rows in the tensor # up to self.max_steps to avoid possible "out of range" errors # in case if there were less than self.max_steps steps globally tensor = tf.pad(tensor, [[0, 0], [0, self.max_steps - tf.shape(tensor)[1]]]) for i in range(self.max_steps): if all_steps: # summarizing the entire (i+1)-st step without # differentiating between actual step counts self._summarize_by_digit_count( tensor[:, i], self.target_num_digits, name + "_" + str(i+1) + "_step" ) else: # considering one more step if required by one_more_step=True # by subtracting 1 from loop variable i (e.g. 0 steps > -1) mask = tf.greater(steps, i - (1 if one_more_step else 0)) # summarizing (i+1)-st step only for those # batch items that actually had (i+1)-st step self._summarize_by_digit_count( tf.boolean_mask(tensor[:, i], mask), tf.boolean_mask(self.target_num_digits, mask), name + "_" + str(i+1) + "_step" ) def _visualize_reconstructions(self, original, reconstruction, st_back, steps, zoom): # enlarging the original images large_original = tf.image.resize_images( tf.reshape(original, [-1, self.canvas_size, self.canvas_size, 1]), [zoom * self.canvas_size, zoom * self.canvas_size] ) # enlarging the reconstructions large_reconstruction = tf.image.resize_images( tf.reshape(reconstruction, [-1, self.canvas_size, self.canvas_size, 1]), [zoom * self.canvas_size, zoom * self.canvas_size] ) # padding (if required) the number of backward ST matrices up to # self.max_steps to avoid possible misalignment errors in case # if there were less than self.max_steps steps globally st_back = tf.pad(st_back, [ [0, 0], [0, self.max_steps - tf.shape(st_back)[1]], [0, 0], [0, 0] ]) # drawing the attention windows # using backward ST matrices num_images = tf.shape(original)[0] boxes = tf.reshape( tf.clip_by_value( transformer( tf.expand_dims( tf.image.draw_bounding_boxes( tf.zeros( [num_images * self.max_steps, self.windows_size, self.windows_size, 1], dtype=reconstruction.dtype ), tf.tile( [[[0.0, 0.0, 1.0, 1.0]]], [num_images * self.max_steps, 1, 1] ) ), 3 ), st_back, [zoom * self.canvas_size, zoom * self.canvas_size] ), 0.0, 1.0 ), [num_images, self.max_steps, zoom * self.canvas_size, zoom * self.canvas_size] ) # sharpening the borders # of the attention windows boxes = tf.where( tf.greater(boxes, 0.01), tf.ones_like(boxes), tf.zeros_like(boxes) ) # concatenating resulting original and reconstructed images with # bounding boxes drawn on them and a thin white stripe between them return tf.concat([ self._draw_colored_bounding_boxes(large_original, boxes, steps), # original images with boxes tf.ones([tf.shape(large_original)[0], zoom * self.canvas_size, 4, 3]), # thin white stripe between self._draw_colored_bounding_boxes(large_reconstruction, boxes, steps), # reconstructed images with boxes ], axis=2) def _create_model(self): # condition of tf.while_loop def cond(step, stopping_sum, _): return tf.logical_and( tf.less(step, self.max_steps), tf.reduce_any(tf.less(stopping_sum, self.stopping_threshold)) ) # body of tf.while_loop def body(step, stopping_sum, prev_state, running_recon, running_loss, running_digits, scales_ta, shifts_ta, z_pres_probs_ta, z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, st_backward_ta, windows_ta, latents_ta): with tf.variable_scope("rnn") as scope: # RNN time step outputs, next_state = cell(self.rnn_input, prev_state, scope=scope) with tf.variable_scope("scale"): # sampling scale with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_mean = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.scale_hidden_units, scope=scope) with tf.variable_scope("output") as scope: scale_log_variance = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope) scale_variance = tf.exp(scale_log_variance) scale = tf.nn.sigmoid(self._sample_from_mvn(scale_mean, scale_variance)) scales_ta = scales_ta.write(scales_ta.size(), scale) s = scale[:, 0] with tf.variable_scope("shift"): # sampling shift with tf.variable_scope("mean"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_mean = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) with tf.variable_scope("log_variance"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.shift_hidden_units, scope=scope) with tf.variable_scope("output") as scope: shift_log_variance = layers.fully_connected(hidden, 2, activation_fn=None, scope=scope) shift_variance = tf.exp(shift_log_variance) shift = tf.nn.tanh(self._sample_from_mvn(shift_mean, shift_variance)) shifts_ta = shifts_ta.write(shifts_ta.size(), shift) x, y = shift[:, 0], shift[:, 1] with tf.variable_scope("st_forward"): # ST: theta of forward transformation theta = tf.stack([ tf.concat([tf.stack([s, tf.zeros_like(s)], axis=1), tf.expand_dims(x, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), s], axis=1), tf.expand_dims(y, 1)], axis=1), ], axis=1) # ST forward transformation: canvas -> window window = transformer( tf.expand_dims(tf.reshape(self.input_images, [-1, self.canvas_size, self.canvas_size]), 3), theta, [self.windows_size, self.windows_size] )[:, :, :, 0] with tf.variable_scope("vae"): # reconstructing the window in VAE vae_recon, vae_mean, vae_log_variance, vae_latent = vae( tf.reshape(window, [-1, self.windows_size self.windows_size]), self.windows_size ** 2, self.vae_recognition_units, self.vae_latent_dimensions, self.vae_generative_units, self.vae_likelihood_std ) # collecting individual reconstruction windows # for each of the inferred digits on the canvas windows_ta = windows_ta.write(windows_ta.size(), vae_recon) # collecting individual latent variable values # for each of the inferred digits on the canvas latents_ta = latents_ta.write(latents_ta.size(), vae_latent) with tf.variable_scope("st_backward"): # ST: theta of backward transformation theta_recon = tf.stack([ tf.concat([tf.stack([1.0 / s, tf.zeros_like(s)], axis=1), tf.expand_dims(-x / s, 1)], axis=1), tf.concat([tf.stack([tf.zeros_like(s), 1.0 / s], axis=1), tf.expand_dims(-y / s, 1)], axis=1), ], axis=1) # collecting backward transformation matrices of ST # to be used for visualizing the attention windows st_backward_ta = st_backward_ta.write(st_backward_ta.size(), theta_recon) # ST backward transformation: window -> canvas window_recon = transformer( tf.expand_dims(tf.reshape(vae_recon, [-1, self.windows_size, self.windows_size]), 3), theta_recon, [self.canvas_size, self.canvas_size] )[:, :, :, 0] with tf.variable_scope("z_pres"): # sampling relaxed (continuous) value of z_pres flag # from Concrete distribution (closer to 1 - more digits, # closer to 0 - no more digits) with tf.variable_scope("log_odds"): with tf.variable_scope("hidden") as scope: hidden = layers.fully_connected(outputs, self.z_pres_hidden_units, scope=scope) with tf.variable_scope("output") as scope: z_pres_log_odds = layers.fully_connected(hidden, 1, activation_fn=None, scope=scope)[:, 0] with tf.variable_scope("gumbel"): # sampling pre-sigmoid value from concrete distribution # with given location (z_pres_log_odds) and temperature z_pres_pre_sigmoid = concrete_binary_pre_sigmoid_sample( z_pres_log_odds, self.z_pres_temperature ) # applying sigmoid to render the Concrete sample z_pres = tf.nn.sigmoid(z_pres_pre_sigmoid) # during test time, rounding the Concrete sample # to obtain the corresponding Bernoulli sample if not self.train: z_pres = tf.round(z_pres) # computing and collecting underlying Bernoulli # probability from inferred log-odds solely for # analysis purposes (not used in the model) z_pres_prob = tf.nn.sigmoid(z_pres_log_odds) z_pres_probs_ta = z_pres_probs_ta.write(z_pres_probs_ta.size(), z_pres_prob) with tf.variable_scope("loss/z_pres_kl"): # z_pres KL-divergence: # previous value of stop_sum is used # to account for KL of first z_pres after # stop_sum becomes >= 1.0 z_pres_kl = concrete_binary_kl_mc_sample( z_pres_pre_sigmoid, self.z_pres_prior_log_odds, self.z_pres_temperature, z_pres_log_odds, self.z_pres_temperature ) # adding z_pres KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), z_pres_kl, tf.zeros_like(running_loss) ) # populating z_pres KL's TensorArray with a new value z_pres_kls_ta = z_pres_kls_ta.write(z_pres_kls_ta.size(), z_pres_kl) # updating stop sum by adding (1 - z_pres) to it: # for small z_pres values stop_sum becomes greater # or equal to self.stopping_threshold and attention # counting of the corresponding batch item stops stopping_sum += (1.0 - z_pres) # updating inferred number of digits per batch item running_digits += tf.cast(tf.less(stopping_sum, self.stopping_threshold), tf.int32) with tf.variable_scope("canvas"): # continuous relaxation: # adding reconstructed window scaled # by z_pres to the running canvas running_recon += tf.where( tf.less(stopping_sum, self.stopping_threshold), tf.expand_dims(z_pres, 1) * tf.reshape( window_recon, [-1, self.canvas_size * self.canvas_size] ), tf.zeros_like(running_recon) ) with tf.variable_scope("loss/scale_kl"): # scale KL-divergence scale_kl = 0.5 * tf.reduce_sum( self.scale_prior_log_variance - scale_log_variance - 1.0 + scale_variance / self.scale_prior_variance + tf.square(scale_mean - self.scale_prior_mean) / self.scale_prior_variance, 1 ) # adding scale KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), scale_kl, tf.zeros_like(running_loss) ) # populating scale KL's TensorArray with a new value scale_kls_ta = scale_kls_ta.write(scale_kls_ta.size(), scale_kl) with tf.variable_scope("loss/shift_kl"): # shift KL-divergence shift_kl = 0.5 * tf.reduce_sum( self.shift_prior_log_variance - shift_log_variance - 1.0 + shift_variance / self.shift_prior_variance + tf.square(shift_mean - self.shift_prior_mean) / self.shift_prior_variance, 1 ) # adding shift KL scaled by z_pres to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), shift_kl, tf.zeros_like(running_loss) ) # populating shift KL's TensorArray with a new value shift_kls_ta = shift_kls_ta.write(shift_kls_ta.size(), shift_kl) with tf.variable_scope("loss/VAE_kl"): # VAE KL-divergence vae_kl = 0.5 * tf.reduce_sum( self.vae_prior_log_variance - vae_log_variance - 1.0 + tf.exp(vae_log_variance) / self.vae_prior_variance + tf.square(vae_mean - self.vae_prior_mean) / self.vae_prior_variance, 1 ) # adding VAE KL scaled by (1-z_pres) to the loss # for those batch items that are not yet finished running_loss += tf.where( tf.less(stopping_sum, self.stopping_threshold), vae_kl, tf.zeros_like(running_loss) ) # populating VAE KL's TensorArray with a new value vae_kls_ta = vae_kls_ta.write(vae_kls_ta.size(), vae_kl) # explicating the shape of "batch-sized" # tensors for TensorFlow graph compiler stopping_sum.set_shape([None]) running_digits.set_shape([None]) running_loss.set_shape([None]) return step + 1, stopping_sum, next_state, \ running_recon, running_loss, running_digits, \ scales_ta, shifts_ta, z_pres_probs_ta, \ z_pres_kls_ta, scale_kls_ta, shift_kls_ta, vae_kls_ta, \ st_backward_ta, windows_ta, latents_ta if self.cnn: with tf.variable_scope("cnn") as cnn_scope: cnn_input = tf.reshape(self.input_images, [-1, 50, 50, 1], name="cnn_input") conv1 = tf.layers.conv2d( inputs=cnn_input, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv1" ) pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2, name="pool1") conv2 = tf.layers.conv2d( inputs=pool1, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv2" ) pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2, name="pool2") conv3 = tf.layers.conv2d( inputs=pool2, filters=self.cnn_filters, kernel_size=[5, 5], strides=(1, 1), padding="same", activation=tf.nn.relu, reuse=cnn_scope.reuse, name="conv3" ) self.rnn_input = tf.reshape(conv3, [-1, 12 * 12 * self.cnn_filters], name="cnn_output") else: self.rnn_input = self.input_images with tf.variable_scope("rnn") as rnn_scope: # creating RNN cells and initial state cell = rnn.BasicLSTMCell(self.rnn_units, reuse=rnn_scope.reuse) rnn_init_state = cell.zero_state( self.batch_size, self.input_images.dtype ) # RNN while_loop with variable number of steps for each batch item _, _, _, reconstruction, loss, self.rec_num_digits, scales, shifts, \ z_pres_probs, z_pres_kls, scale_kls, shift_kls, vae_kls, \ st_backward, windows, latents = tf.while_loop( cond, body, [ tf.constant(0), # RNN time step, initially zero tf.zeros([self.batch_size]), # running sum of z_pres samples rnn_init_state, # initial RNN state tf.zeros_like(self.input_images), # reconstruction canvas, initially empty tf.zeros([self.batch_size]), # running value of the loss function tf.zeros([self.batch_size], dtype=tf.int32), # running inferred number of digits tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # inferred scales tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # inferred shifts tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # z_pres probabilities tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # z_pres KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # scale KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # shift KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # VAE KL-divergence tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # backward ST matrices tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True), # individual recon. windows tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True) # latents of individual digits ] ) # transposing contents of TensorArray's fetched from while_loop iterations self.rec_scales = tf.transpose(scales.stack(), (1, 0, 2), name="rec_scales") self.rec_shifts = tf.transpose(shifts.stack(), (1, 0, 2), name="rec_shifts") self.rec_st_back = tf.transpose(st_backward.stack(), (1, 0, 2, 3), name="rec_st_back") self.rec_windows = tf.transpose(windows.stack(), (1, 0, 2), name="rec_windows") self.rec_latents = tf.transpose(latents.stack(), (1, 0, 2), name="rec_windows") self.z_pres_probs = tf.transpose(z_pres_probs.stack(), name="z_pres_probs") self.z_pres_kls = tf.transpose(z_pres_kls.stack(), name="z_pres_kls") self.scale_kls = tf.transpose(scale_kls.stack(), name="scale_kls") self.shift_kls = tf.transpose(shift_kls.stack(), name="shift_kls") self.vae_kls = tf.transpose(vae_kls.stack(), name="vae_kls") with tf.variable_scope("loss/reconstruction"): # clipping the reconstructed canvas by [0.0, 1.0] self.reconstruction = tf.maximum(tf.minimum(reconstruction, 1.0), 0.0, name="clipped_rec") # reconstruction loss: cross-entropy between # original images and their reconstructions self.reconstruction_loss = -tf.reduce_sum( self.input_images * tf.log(self.reconstruction + 10e-10) + (1.0 - self.input_images) * tf.log(1.0 - self.reconstruction + 10e-10), 1, name="reconstruction_loss" ) # adding reconstruction loss loss += self.reconstruction_loss with tf.variable_scope("accuracy"): # accuracy of inferred number of digits accuracy = tf.cast( tf.equal(self.target_num_digits, self.rec_num_digits), tf.float32 ) var_scope = tf.get_variable_scope().name model_vars = [ v for v in tf.trainable_variables() if v.name.startswith(var_scope) ] with tf.variable_scope("summaries"): # averaging between batch items self.loss = tf.reduce_mean(loss, name="loss") self.accuracy = tf.reduce_mean(accuracy, name="accuracy") # post while-loop numeric summaries grouped by digit count self._summarize_by_digit_count(self.rec_num_digits, self.target_num_digits, "steps") self._summarize_by_digit_count(self.reconstruction_loss, self.target_num_digits, "rec_loss") self._summarize_by_digit_count(accuracy, self.target_num_digits, "digit_acc") self._summarize_by_digit_count(loss, self.target_num_digits, "total_loss") # step-level numeric summaries (from within while-loop) grouped by step and digit count self._summarize_by_step(self.rec_scales[:, :, 0], self.rec_num_digits, "scale") self._summarize_by_step(self.z_pres_probs, self.rec_num_digits, "z_pres_prob", all_steps=True) self._summarize_by_step(self.z_pres_kls, self.rec_num_digits, "z_pres_kl", one_more_step=True) self._summarize_by_step(self.scale_kls, self.rec_num_digits, "scale_kl") self._summarize_by_step(self.shift_kls, self.rec_num_digits, "shift_kl") self._summarize_by_step(self.vae_kls, self.rec_num_digits, "vae_kl") # image summary of the reconstructions self.img_summaries.append( tf.summary.image( "reconstruction", self._visualize_reconstructions( self.input_images[:self.num_summary_images], self.reconstruction[:self.num_summary_images], self.rec_st_back[:self.num_summary_images], self.rec_num_digits[:self.num_summary_images], zoom=2 ), max_outputs=self.num_summary_images ) ) # variable summaries for v in model_vars: self.var_summaries.append( tf.summary.histogram( v.name, v.value() ) ) if self.train: with tf.variable_scope("training"): # optimizer to minimize the loss function optimizer = tf.train.AdamOptimizer(self.learning_rate) grads, variables = zip(*optimizer.compute_gradients(self.loss)) if self.gradient_clipping_norm is not None: for i in range(len(grads)): if grads[i] is None: continue # summaries of the original gradient values self.grad_summaries.append(tf.summary.histogram( variables[i].name + "_grad_original", grads[i] )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_original_norm", tf.norm(grads[i]) )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_original_avg", tf.reduce_mean(grads[i]) )) # gradient clipping by global norm, if required grads = tf.clip_by_global_norm(grads, self.gradient_clipping_norm)[0] for i in range(len(grads)): if grads[i] is None: continue # summaries of the applied (maybe clipped) gradients self.grad_summaries.append(tf.summary.histogram( variables[i].name + "_grad_applied", grads[i] )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_applied_norm", tf.norm(grads[i]) )) self.grad_summaries.append(tf.summary.scalar( variables[i].name + "_grad_applied_avg", tf.reduce_mean(grads[i]) )) grads_and_vars = list(zip(grads, variables)) # training step operation self.training = optimizer.apply_gradients( grads_and_vars, global_step=self.global_step )