
'''simple docstring'''
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_											=						[
    # 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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for pegasus_name, hf_name in PATTERNS:
        snake_case_ :    Optional[Any]     			=						k.replace(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return k

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						DEFAULTS.copy()
    cfg_kwargs.update(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						PegasusConfig(**__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Any     			=						PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Dict     			=						torch_model.model.state_dict()
    snake_case_ :    Union[str, Any]     			=						{}
    for k, v in tf_weights.items():
        snake_case_ :    List[str]     			=						rename_state_dict_key(__SCREAMING_SNAKE_CASE       )
        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:
            snake_case_ :    Dict     			=						v.T
        snake_case_ :    Optional[Any]     			=						torch.tensor(__SCREAMING_SNAKE_CASE,		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
    snake_case_ :    Optional[int]     			=						torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]       )
    snake_case_ :    Union[str, Any]     			=						mapping["shared.weight"]
    snake_case_ :    Optional[int]     			=						mapping["shared.weight"]
    snake_case_ :    Optional[int]     			=						{k: torch.zeros_like(__SCREAMING_SNAKE_CASE       ) for k, v in sd.items() if k.endswith("bias"       ) and k not in mapping}
    mapping.update(**__SCREAMING_SNAKE_CASE       )
    snake_case_			, snake_case_ :    Optional[int]     			=						torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE,		strict=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						[
        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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE="./ckpt/aeslc/model.ckpt-32000"       ):
    """simple docstring"""






    snake_case_ :    Any     			=						tf.train.list_variables(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						{}
    snake_case_ :    Optional[int]     			=						["Adafactor", "global_step"]
    for name, shape in tqdm(__SCREAMING_SNAKE_CASE,		desc="converting tf checkpoint to dict"       ):
        snake_case_ :    Optional[int]     			=						any(pat in name for pat in ignore_name       )
        if skip_key:
            continue
        snake_case_ :    Dict     			=						tf.train.load_variable(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Optional[Any]     			=						array
    return tf_weights

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						Path(__SCREAMING_SNAKE_CASE       ).parent.name
    snake_case_ :    Optional[Any]     			=						task_specific_params[f'summarization_{dataset}']["max_position_embeddings"]
    snake_case_ :    int     			=						PegasusTokenizer.from_pretrained("sshleifer/pegasus",		model_max_length=__SCREAMING_SNAKE_CASE       )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(__SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
  a_											=						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_											=						parser.parse_args()
  if args.save_dir is None:
    a_											=						Path(args.tf_ckpt_path).parent.name
    a_											=						os.path.join("pegasus", dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''
import gc
import math
import unittest

import torch

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

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


a_											=						logging.get_logger(__name__)

enable_full_determinism()






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						4
     snake_case_ :    int     			=						3
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    str     			=						{
         "block_out_channels": (32, 64),
         "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
         "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
         "attention_head_dim": 3,
         "out_channels": 3,
         "in_channels": 3,
         "layers_per_block": 2,
         "sample_size": 32,
     }
     snake_case_ :    List[Any]     			=						self.dummy_input
     return init_dict, inputs_dict






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    Dict     			=						4
     snake_case_ :    Dict     			=						(32, 32)

     snake_case_ :    Dict     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10]).to(lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 @property
 def        snake_case__		(    self):
     return (4, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						{
         "sample_size": 32,
         "in_channels": 4,
         "out_channels": 4,
         "layers_per_block": 2,
         "block_out_channels": (32, 64),
         "attention_head_dim": 32,
         "down_block_types": ("DownBlock2D", "DownBlock2D"),
         "up_block_types": ("UpBlock2D", "UpBlock2D"),
     }
     snake_case_ :    Any     			=						self.dummy_input
     return init_dict, inputs_dict





 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Tuple     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)

     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    int     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[Any]     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model.to(lowercase_)
     snake_case_ :    str     			=						model(**self.dummy_input).sample

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





 @unittest.skipIf(torch_device != "cuda" ,       "This test is supposed to run on GPU")
 def        snake_case__		(    self):
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     snake_case_			, snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_)
     model_accelerate.to(lowercase_)
     model_accelerate.eval()

     snake_case_ :    Optional[int]     			=						torch.randn(
         1 ,       model_accelerate.config.in_channels ,       model_accelerate.config.sample_size ,       model_accelerate.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    Any     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     snake_case_ :    Optional[int]     			=						model_accelerate(lowercase_ ,       lowercase_)["sample"]

     # two models don't need to stay in the device at the same time
     del model_accelerate
     torch.cuda.empty_cache()
     gc.collect()

     snake_case_			, snake_case_ :    Union[str, Any]     			=						UNetaDModel.from_pretrained(
         "fusing/unet-ldm-dummy-update" ,       output_loading_info=lowercase_ ,       low_cpu_mem_usage=lowercase_)
     model_normal_load.to(lowercase_)
     model_normal_load.eval()
     snake_case_ :    Optional[Any]     			=						model_normal_load(lowercase_ ,       lowercase_)["sample"]

     assert torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3)





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
     model.eval()
     model.to(lowercase_)

     snake_case_ :    List[str]     			=						torch.randn(
         1 ,       model.config.in_channels ,       model.config.sample_size ,       model.config.sample_size ,       generator=torch.manual_seed(0) ,       )
     snake_case_ :    List[str]     			=						noise.to(lowercase_)
     snake_case_ :    str     			=						torch.tensor([10] * noise.shape[0]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Tuple     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    Optional[int]     			=						output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     snake_case_ :    Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-3))






class    UpperCAmelCase_				(	snake_case__    ,						snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						UNetaDModel
 UpperCAmelCase_		    =						"""sample"""





 @property
 def        snake_case__		(    self ,       lowercase_=(32, 32)):
     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3

     snake_case_ :    str     			=						floats_tensor((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    Dict     			=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa ,       device=lowercase_)

     return {"sample": noise, "timestep": time_step}





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 @property
 def        snake_case__		(    self):
     return (3, 32, 32)





 def        snake_case__		(    self):
     snake_case_ :    List[str]     			=						{
         "block_out_channels": [32, 64, 64, 64],
         "in_channels": 3,
         "layers_per_block": 1,
         "out_channels": 3,
         "time_embedding_type": "fourier",
         "norm_eps": 1E-6,
         "mid_block_scale_factor": math.sqrt(2.0),
         "norm_num_groups": None,
         "down_block_types": [
             "SkipDownBlock2D",
             "AttnSkipDownBlock2D",
             "SkipDownBlock2D",
             "SkipDownBlock2D",
         ],
         "up_block_types": [
             "SkipUpBlock2D",
             "SkipUpBlock2D",
             "AttnSkipUpBlock2D",
             "SkipUpBlock2D",
         ],
     }
     snake_case_ :    Optional[Any]     			=						self.dummy_input
     return init_dict, inputs_dict





 @slow
 def        snake_case__		(    self):
     snake_case_			, snake_case_ :    Optional[int]     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ,       output_loading_info=lowercase_)
     self.assertIsNotNone(lowercase_)
     self.assertEqual(len(loading_info["missing_keys"]) ,       0)

     model.to(lowercase_)
     snake_case_ :    Dict     			=						self.dummy_input
     snake_case_ :    Tuple     			=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowercase_)
     snake_case_ :    Tuple     			=						noise
     snake_case_ :    Tuple     			=						model(**lowercase_)

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





 @slow
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
     model.to(lowercase_)

     snake_case_ :    List[Any]     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    Dict     			=						(2_56, 2_56)

     snake_case_ :    Tuple     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    int     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[str]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
     model.to(lowercase_)

     snake_case_ :    Dict     			=						4
     snake_case_ :    str     			=						3
     snake_case_ :    List[Any]     			=						(32, 32)

     snake_case_ :    int     			=						torch.ones((batch_size, num_channels) + sizes).to(lowercase_)
     snake_case_ :    List[Any]     			=						torch.tensor(batch_size * [1E-4]).to(lowercase_)

     with torch.no_grad():
         snake_case_ :    Optional[Any]     			=						model(lowercase_ ,       lowercase_).sample

     snake_case_ :    List[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     snake_case_ :    Union[str, Any]     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256])
     # fmt: on

     self.assertTrue(torch_all_close(lowercase_ ,       lowercase_ ,       rtol=1E-2))





 def        snake_case__		(    self):
     # not required for this model
     pass




