
'''simple docstring'''





import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     =       [
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

SCREAMING_SNAKE_CASE     =       [
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[int]				       =							torch.load(lowercase__   ,       map_location="cpu"		)
  return sd







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__=rename_keys_prefix		):
  UpperCAmelCase__      :					Optional[Any]				       =							OrderedDict()
  UpperCAmelCase__      :					Tuple				       =							torch.arange(config.max_position_embeddings		).expand((1, -1)		)
  # detector_d = OrderedDict()
  for key in d:
    if "detector" in key:
      # detector_d[key.replace('detector.','')] = d[key]
      continue
    UpperCAmelCase__      :					int				       =							key
    for name_pair in rename_keys_prefix:
      UpperCAmelCase__      :					List[Any]				       =							new_key.replace(name_pair[0]   ,       name_pair[1]		)
    UpperCAmelCase__      :					Union[str, Any]				       =							d[key]
    if key == "bert.cls.predictions.decoder.weight":
      # Old bert code didn't have `decoder.bias`, but was added separately
      UpperCAmelCase__      :					Optional[int]				       =							new_d["cls.predictions.bias"]
  return new_d







@torch.no_grad()
def snake_case_     (					lowercase__   ,       lowercase__		):
  assert (
      checkpoint_path.split("/"		)[-1] in ACCEPTABLE_CHECKPOINTS
  ), F"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

  # Get Config
  if "pre" in checkpoint_path:
    UpperCAmelCase__      :					Tuple				       =							"pretraining"
    if "vcr" in checkpoint_path:
      UpperCAmelCase__      :					List[str]				       =							{"visual_embedding_dim": 5_1_2}
    elif "vqa_advanced" in checkpoint_path:
      UpperCAmelCase__      :					Union[str, Any]				       =							{"visual_embedding_dim": 2_0_4_8}
    elif "vqa" in checkpoint_path:
      UpperCAmelCase__      :					Union[str, Any]				       =							{"visual_embedding_dim": 2_0_4_8}
    elif "nlvr" in checkpoint_path:
      UpperCAmelCase__      :					Any				       =							{"visual_embedding_dim": 1_0_2_4}
    else:
      raise NotImplementedError(F"""No implementation found for `{checkpoint_path}`."""		)
  else:
    if "vcr" in checkpoint_path:
      UpperCAmelCase__      :					Union[str, Any]				       =							{"visual_embedding_dim": 5_1_2}
      UpperCAmelCase__      :					int				       =							"multichoice"
    elif "vqa_advanced" in checkpoint_path:
      UpperCAmelCase__      :					Tuple				       =							{"visual_embedding_dim": 2_0_4_8}
      UpperCAmelCase__      :					List[str]				       =							"vqa_advanced"
    elif "vqa" in checkpoint_path:
      UpperCAmelCase__      :					Dict				       =							{"visual_embedding_dim": 2_0_4_8, "num_labels": 3_1_2_9}
      UpperCAmelCase__      :					int				       =							"vqa"
    elif "nlvr" in checkpoint_path:
      UpperCAmelCase__      :					Dict				       =							{
          "visual_embedding_dim": 1_0_2_4,
          "num_labels": 2,
      }
      UpperCAmelCase__      :					List[Any]				       =							"nlvr"

  UpperCAmelCase__      :					int				       =							VisualBertConfig(**lowercase__		)

  # Load State Dict
  UpperCAmelCase__      :					int				       =							load_state_dict(lowercase__		)

  UpperCAmelCase__      :					List[Any]				       =							get_new_dict(lowercase__   ,       lowercase__		)

  if model_type == "pretraining":
    UpperCAmelCase__      :					Dict				       =							VisualBertForPreTraining(lowercase__		)
  elif model_type == "vqa":
    UpperCAmelCase__      :					Union[str, Any]				       =							VisualBertForQuestionAnswering(lowercase__		)
  elif model_type == "nlvr":
    UpperCAmelCase__      :					str				       =							VisualBertForVisualReasoning(lowercase__		)
  elif model_type == "multichoice":
    UpperCAmelCase__      :					Optional[int]				       =							VisualBertForMultipleChoice(lowercase__		)

  model.load_state_dict(lowercase__		)
  # Save Checkpoints
  Path(lowercase__		).mkdir(exist_ok=lowercase__		)
  model.save_pretrained(lowercase__		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
      parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
      SCREAMING_SNAKE_CASE     =       parser.parse_args()
      convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''





import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Tuple				=							PriorTransformer
 lowercase_					:    Dict				=							"hidden_states"







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							4
   UpperCAmelCase__      :					List[Any]				       =							8
   UpperCAmelCase__      :					Union[str, Any]				       =							7

   UpperCAmelCase__      :					Tuple				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							floats_tensor((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  int			,				snake_case__		:  List[str]=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					str				       =							4
   UpperCAmelCase__      :					int				       =							8
   UpperCAmelCase__      :					Optional[int]				       =							7

   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 @property
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   return (4, 8)







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   return (4, 8)







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							{
       "num_attention_heads": 2,
       "attention_head_dim": 4,
       "num_layers": 2,
       "embedding_dim": 8,
       "num_embeddings": 7,
       "additional_embeddings": 4,
   }
   UpperCAmelCase__      :					Optional[Any]				       =							self.dummy_input
   return init_dict, inputs_dict







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							PriorTransformer.from_pretrained(
       "hf-internal-testing/prior-dummy"			,				output_loading_info=snake_case__ )
   self.assertIsNotNone(snake_case__ )
   self.assertEqual(len(loading_info["missing_keys"] )			,				0 )

   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							model(**self.dummy_input )[0]

   assert hidden_states is not None, "Make sure output is not None"







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					int				       =							self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__      :					Tuple				       =							self.model_class(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							inspect.signature(model.forward )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__      :					int				       =							[*signature.parameters.keys()]

   UpperCAmelCase__      :					Optional[Any]				       =							["hidden_states", "timestep"]
   self.assertListEqual(arg_names[:2]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy" )
   UpperCAmelCase__      :					List[Any]				       =							model.to(snake_case__ )

   if hasattr(snake_case__			,				"set_default_attn_processor" ):
     model.set_default_attn_processor()

   UpperCAmelCase__      :					int				       =							self.get_dummy_seed_input()

   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )[0]

   UpperCAmelCase__      :					Any				       =							output[0, :5].flatten().cpu()
   print(snake_case__ )

   # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
   # the expected output slices are not the same for CPU and GPU.
   UpperCAmelCase__      :					Optional[int]				       =							torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239] )
   self.assertTrue(torch_all_close(snake_case__			,				snake_case__			,				rtol=1e-2 ) )






@slow
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  Optional[int]=1			,				snake_case__		:  Any=7_68			,				snake_case__		:  List[Any]=77			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   torch.manual_seed(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							batch_size
   UpperCAmelCase__      :					Any				       =							embedding_dim
   UpperCAmelCase__      :					Any				       =							num_embeddings

   UpperCAmelCase__      :					str				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )

   UpperCAmelCase__      :					Optional[Any]				       =							torch.randn((batch_size, embedding_dim) ).to(snake_case__ )
   UpperCAmelCase__      :					Any				       =							torch.randn((batch_size, num_embeddings, embedding_dim) ).to(snake_case__ )

   return {
       "hidden_states": hidden_states,
       "timestep": 2,
       "proj_embedding": proj_embedding,
       "encoder_hidden_states": encoder_hidden_states,
   }







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ] )
 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  List[str]			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"			,				subfolder="prior" )
   model.to(snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							self.get_dummy_seed_input(seed=snake_case__ )

   with torch.no_grad():
     UpperCAmelCase__      :					Any				       =							model(**snake_case__ )[0]

   assert list(sample.shape ) == [1, 7_68]

   UpperCAmelCase__      :					List[str]				       =							sample[0, :8].flatten().cpu()
   print(snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							torch.tensor(snake_case__ )

   assert torch_all_close(snake_case__			,				snake_case__			,				atol=1e-3 )



