'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step






'''simple docstring'''

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
 from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
 from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
 from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


A_     :						Optional[Any]    							=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

A_     :						Tuple    							='''main'''
# Default branch name
A_     :						Dict    							='''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
A_     :						int    							='''aaaaaaa'''
# This commit does not exist, so we should 404.

A_     :						List[str]    							='''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
A_     :						List[str]    							='''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''



@contextlib.contextmanager
def snake_case_      (					)			->					Tuple:
   print('''Welcome!''')
   yield
   print('''Bye!''')



@contextlib.contextmanager
def snake_case_      (					)			->					str:
   print('''Bonjour!''')
   yield
   print('''Au revoir!''')


class     __UpperCAmelCase       ( unittest.TestCase		):





  def 						UpperCAmelCase_	(	self ):
     # If the spec is missing, importlib would not be able to import the module dynamically.
     assert transformers.__spec__ is not None
     assert importlib.util.find_spec('''transformers''' ) is not None






class     __UpperCAmelCase       ( unittest.TestCase		):





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([] ):
        print('''Transformers are awesome!''' )
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()    ,     '''Transformers are awesome!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Welcome!\nTransformers are awesome!\nBye!\n''' )





  @unittest.mock.patch('''sys.stdout'''    ,     new_callable=io.StringIO )
  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     with ContextManagers([context_fr(), context_en()] ):
        print('''Transformers are awesome!''' )
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()    ,     '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )





  @require_torch
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )





  @require_tf
  def 						UpperCAmelCase_	(	self ):
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels''', '''next_sentence_label'''] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''start_positions''', '''end_positions'''] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     ['''labels'''] )







  @require_flax
  def 						UpperCAmelCase_	(	self ):
     # Flax models don't have labels
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )
     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )


     class     __UpperCAmelCase       ( __a		):
       pass






     self.assertEqual(find_labels(_lowerCamelCase )    ,     [] )





