




"""simple docstring"""




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
   import tensorflow as tf

   from transformers import TFBlipTextModel
   from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class       a__	:







     def __init__(     self  ,		A  ,		A=12  ,		A=7  ,		A=True  ,		A=True  ,		A=True  ,		A=99  ,		A=32  ,		A=32  ,		A=2  ,		A=4  ,		A=37  ,		A=0.1  ,		A=0.1  ,		A=512  ,		A=0.0_2  ,		A=0  ,		A=None  ,		)  ->   Tuple:



          '''simple docstring'''



          a     =				parent
          a     =				batch_size
          a     =				seq_length
          a     =				is_training
          a     =				use_input_mask
          a     =				use_labels
          a     =				vocab_size
          a     =				hidden_size
          a     =				projection_dim
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				intermediate_size
          a     =				dropout
          a     =				attention_dropout
          a     =				max_position_embeddings
          a     =				initializer_range
          a     =				scope
          a     =				bos_token_id







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size			)

          a     =				None
          if self.use_input_mask:
               a     =				random_attention_mask([self.batch_size, self.seq_length]			)

          if input_mask is not None:
               a     =				input_mask.numpy()
               a  ,				a     =				input_mask.shape
               a     =				np.random.randint(1  ,		seq_length - 1  ,		size=(batch_size,)			)
               for batch_idx, start_index in enumerate(__SCREAMING_SNAKE_CASE			):
                    a     =				1
                    a     =				0

          a     =				self.get_config()

          return config, input_ids, tf.convert_to_tensor(__SCREAMING_SNAKE_CASE			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          return BlipTextConfig(
              vocab_size=self.vocab_size  ,		hidden_size=self.hidden_size  ,		projection_dim=self.projection_dim  ,		num_hidden_layers=self.num_hidden_layers  ,		num_attention_heads=self.num_attention_heads  ,		intermediate_size=self.intermediate_size  ,		dropout=self.dropout  ,		attention_dropout=self.attention_dropout  ,		max_position_embeddings=self.max_position_embeddings  ,		initializer_range=self.initializer_range  ,		bos_token_id=self.bos_token_id  ,		)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				TFBlipTextModel(config=__SCREAMING_SNAKE_CASE			)
          a     =				model(__SCREAMING_SNAKE_CASE  ,		attention_mask=__SCREAMING_SNAKE_CASE  ,		training=__SCREAMING_SNAKE_CASE			)
          a     =				model(__SCREAMING_SNAKE_CASE  ,		training=__SCREAMING_SNAKE_CASE			)
          self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)			)
          self.parent.assertEqual(result.pooler_output.shape  ,		(self.batch_size, self.hidden_size)			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          a  ,				a  ,				a     =				config_and_inputs
          a     =				{"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict







@require_tf
class       a__	( lowerCAmelCase_       , unittest.TestCase				):
     a		:      Optional[int]		 =     (TFBlipTextModel,) if is_tf_available() else ()
     a		:      int		 =     False
     a		:      List[Any]		 =     False
     a		:      List[Any]		 =     False







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				BlipTextModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=__SCREAMING_SNAKE_CASE  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     @unittest.skip(reason="Blip does not use inputs_embeds"			)
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          pass







     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          pass







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a     =				TFBlipTextModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)







     def       lowerCAmelCase_    (     self  ,		A=True			)  ->   Optional[int]:



          '''simple docstring'''



          super().test_pt_tf_model_equivalence(allow_missing_keys=__SCREAMING_SNAKE_CASE			)







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     if k.startswith("encoder"):
          a     =				k.replace(".attn"		, ".self_attn")
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "final_layer_norm")
     elif k.startswith("decoder"):
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "encoder_attn_layer_norm")
          a     =				k.replace("norm3"		, "final_layer_norm")
     return k

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          a     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          a     =				rename_state_dict_key(__UpperCamelCase)
          if new_k not in valid_keys:
               failures.append([k, new_k])
          else:
               a     =				v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__UpperCamelCase)
     m.model.load_state_dict(__UpperCamelCase		, strict=__UpperCamelCase)
     m.half()
     m.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
   lowercase__				:      int	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
   parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
   parser.add_argument(
       "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

