


"""simple docstring"""



import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  from transformers.models.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )







class _lowerCAmelCase    (				unittest.TestCase     ):







    def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=13						,     UpperCamelCase__=7						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=99						,     UpperCamelCase__=32						,     UpperCamelCase__=5						,     UpperCamelCase__=4						,     UpperCamelCase__=37						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=512						,     UpperCamelCase__=16						,     UpperCamelCase__=2						,     UpperCamelCase__=0.02						,     UpperCamelCase__=4						,     )     ->		List[str]:

        '''simple docstring'''




        snake_case   :				List[str]				 =   parent
        snake_case   :				Optional[int]				 =   batch_size
        snake_case   :				List[str]				 =   seq_length
        snake_case   :				List[Any]				 =   is_training
        snake_case   :				int				 =   use_attention_mask
        snake_case   :				Optional[Any]				 =   use_token_type_ids
        snake_case   :				Optional[int]				 =   use_labels
        snake_case   :				str				 =   vocab_size
        snake_case   :				List[Any]				 =   hidden_size
        snake_case   :				Optional[int]				 =   num_hidden_layers
        snake_case   :				Optional[Any]				 =   num_attention_heads
        snake_case   :				List[str]				 =   intermediate_size
        snake_case   :				Any				 =   hidden_act
        snake_case   :				Dict				 =   hidden_dropout_prob
        snake_case   :				List[Any]				 =   attention_probs_dropout_prob
        snake_case   :				List[str]				 =   max_position_embeddings
        snake_case   :				Any				 =   type_vocab_size
        snake_case   :				str				 =   type_sequence_label_size
        snake_case   :				Tuple				 =   initializer_range
        snake_case   :				Optional[int]				 =   num_choices







    def 			lowerCamelCase		(     self						)     ->		Any:

        '''simple docstring'''




        snake_case   :				Any				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

        snake_case   :				Optional[int]				 =   None
        if self.use_attention_mask:
            snake_case   :				Tuple				 =   random_attention_mask([self.batch_size, self.seq_length]						)

        snake_case   :				int				 =   None
        if self.use_token_type_ids:
            snake_case   :				int				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

        snake_case   :				str				 =   RobertaConfig(
            vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=UpperCamelCase__						,     initializer_range=self.initializer_range						,     )

        return config, input_ids, token_type_ids, attention_mask







    def 			lowerCamelCase		(     self						)     ->		int:

        '''simple docstring'''




        snake_case   :				Tuple				 =   self.prepare_config_and_inputs()
        snake_case   :				Any				 =   config_and_inputs
        snake_case   :				Optional[Any]				 =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict







    def 			lowerCamelCase		(     self						)     ->		Dict:

        '''simple docstring'''




        snake_case   :				List[Any]				 =   self.prepare_config_and_inputs()
        snake_case   :				Tuple				 =   config_and_inputs

        snake_case   :				Union[str, Any]				 =   True
        snake_case   :				Optional[Any]				 =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        snake_case   :				Dict				 =   ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2						)

        return (
            config,
            input_ids,
            token_type_ids,
            encoder_hidden_states,
            encoder_attention_mask,
        )







@require_flax
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
    __UpperCAmelCase			:	str							  =  True

    __UpperCAmelCase			:	Tuple							  =  (
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

        '''simple docstring'''




        snake_case   :				int				 =   FlaxRobertaModelTester(self						)







    @slow
    def 			lowerCamelCase		(     self						)     ->		int:

        '''simple docstring'''




        for model_class_name in self.all_model_classes:
            snake_case   :				Optional[Any]				 =   model_class_name.from_pretrained("roberta-base"						,     from_pt=UpperCamelCase__						)
            snake_case   :				str				 =   model(np.ones((1, 1)						)						)
            self.assertIsNotNone(UpperCamelCase__						)









"""simple docstring"""



import importlib

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







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





    if conf_path is None:
        snake_case   :				Optional[Any]				 =   "./model_checkpoints/vqgan_only.yaml"
    snake_case   :				Union[str, Any]				 =   load_config(lowercase						,							display=lowercase			)
    snake_case   :				List[Any]				 =   VQModel(**config.model.params			)
    if ckpt_path is None:
        snake_case   :				Optional[int]				 =   "./model_checkpoints/vqgan_only.pt"
    snake_case   :				Union[str, Any]				 =   torch.load(lowercase						,							map_location=lowercase			)
    if ".ckpt" in ckpt_path:
        snake_case   :				Union[str, Any]				 =   sd["state_dict"]
    model.load_state_dict(lowercase						,							strict=lowercase			)
    model.to(lowercase			)
    del sd
    return model







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				List[Any]				 =   model.encode(lowercase			)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
    snake_case   :				Union[str, Any]				 =   model.decode(lowercase			)
    return xrec







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """simple docstring"""





    if "target" not in config:
        raise KeyError("Expected key `target` to instantiate."			)
    return get_obj_from_str(config["target"]			)(**config.get("params"						,							{}			)			)







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





    if ckpt:
        snake_case   :				Dict				 =   torch.load(lowercase						,							map_location="cpu"			)
        snake_case   :				Any				 =   pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'			)
    else:
        snake_case   :				Any				 =   {"state_dict": None}
        snake_case   :				List[str]				 =   None
    snake_case   :				Dict				 =   load_model_from_config(config.model						,							pl_sd["state_dict"]						,							gpu=lowercase						,							eval_mode=lowercase			)["model"]
    return model, global_step





