






"""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     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		int									=PriorTransformer
       a    :		Tuple									='''hidden_states'''





       @property
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    4
         UpperCamelCase_: str   				=    8
         UpperCamelCase_: Optional[Any]   				=    7

         UpperCamelCase_: List[str]   				=    floats_tensor((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: Any   				=    floats_tensor((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(_lowerCamelCase							)

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





       def       _a   ( self  ,      _lowerCamelCase=0							):
         torch.manual_seed(_lowerCamelCase							)
         UpperCamelCase_: Optional[Any]   				=    4
         UpperCamelCase_: Dict   				=    8
         UpperCamelCase_: Optional[int]   				=    7

         UpperCamelCase_: str   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_lowerCamelCase							)

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





       @property
       def       _a   ( self							):
         return (4, 8)





       @property
       def       _a   ( self							):
         return (4, 8)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    {
             'num_attention_heads': 2,
             'attention_head_dim': 4,
             'num_layers': 2,
             'embedding_dim': 8,
             'num_embeddings': 7,
             'additional_embeddings': 4,
         }
         UpperCamelCase_: Tuple   				=    self.dummy_input
         return init_dict, inputs_dict





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    PriorTransformer.from_pretrained(
             'hf-internal-testing/prior-dummy'  ,      output_loading_info=_lowerCamelCase							)
         self.assertIsNotNone(_lowerCamelCase							)
         self.assertEqual(len(loading_info['missing_keys']							)  ,      0							)

         model.to(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    model(**self.dummy_input							)[0]

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





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.prepare_init_args_and_inputs_for_common()

         UpperCamelCase_: Dict   				=    self.model_class(**_lowerCamelCase							)
         UpperCamelCase_: str   				=    inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         UpperCamelCase_: Tuple   				=    [*signature.parameters.keys()]

         UpperCamelCase_: int   				=    ['hidden_states', 'timestep']
         self.assertListEqual(arg_names[:2]  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'							)
         UpperCamelCase_: Tuple   				=    model.to(_lowerCamelCase							)

         if hasattr(_lowerCamelCase  ,      'set_default_attn_processor'							):
           model.set_default_attn_processor()

         UpperCamelCase_: Optional[Any]   				=    self.get_dummy_seed_input()

         with torch.no_grad():
           UpperCamelCase_: Dict   				=    model(**_lowerCamelCase							)[0]

         UpperCamelCase_: Optional[Any]   				=    output[0, :5].flatten().cpu()
         print(_lowerCamelCase							)

         # 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([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]							)
         self.assertTrue(torch_all_close(_lowerCamelCase  ,      _lowerCamelCase  ,      rtol=1e-2							)							)







@slow
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self  ,      _lowerCamelCase=1  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=7_7  ,      _lowerCamelCase=0							):
         torch.manual_seed(_lowerCamelCase							)
         UpperCamelCase_: int   				=    batch_size
         UpperCamelCase_: Any   				=    embedding_dim
         UpperCamelCase_: str   				=    num_embeddings

         UpperCamelCase_: Optional[Any]   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)

         UpperCamelCase_: str   				=    torch.randn((batch_size, embedding_dim)							).to(_lowerCamelCase							)
         UpperCamelCase_: str   				=    torch.randn((batch_size, num_embeddings, embedding_dim)							).to(_lowerCamelCase							)

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





       def       _a   ( self							):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
               [3_7, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
               # fmt: on
           ]							)
       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'  ,      subfolder='prior'							)
         model.to(_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    self.get_dummy_seed_input(seed=_lowerCamelCase							)

         with torch.no_grad():
           UpperCamelCase_: Tuple   				=    model(**_lowerCamelCase							)[0]

         assert list(sample.shape							) == [1, 7_6_8]

         UpperCamelCase_: Dict   				=    sample[0, :8].flatten().cpu()
         print(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    torch.tensor(_lowerCamelCase							)

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







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A_		:		Tuple		     =			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]

def   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    if new_k not in sd:
      raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''  )

    if "dense" in k or "proj" in new_k:
      UpperCamelCase_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   dtype=sd[new_k].dtype  )
    assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  UpperCamelCase_: Tuple   				=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
  UpperCamelCase_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
  ]
  assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], F'''no matches found for the following tf keys {extra}'''
  return torch_model

def   snake_case (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

  # convert model
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    torch_model.state_dict()
  sd.pop('model.decoder.embed_positions.weight'  )
  sd.pop('model.encoder.embed_positions.weight'  )
  torch.save(UpperCAmelCase__     ,   Path(UpperCAmelCase__  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   A_		:		Optional[Any]		     =			parser.parse_args()
   if args.save_dir is None:
      A_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)