


import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  str       =		AutoencoderKL
      snake_case__       :  Any       =		'''sample'''
      snake_case__       :  Optional[Any]       =		1e-2
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             a_      :	List[str]							=						4
             a_      :	List[Any]							=						3
             a_      :	Optional[int]							=						(3_2, 3_2)

             a_      :	Dict							=						floats_tensor((batch_size, num_channels) + sizes  ).to(SCREAMING_SNAKE_CASE__  )

             return {"sample": image}
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Dict:
             return (3, 3_2, 3_2)
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Any:
             return (3, 3_2, 3_2)
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[int]:
             a_      :	Any							=						{
                 'block_out_channels': [3_2, 6_4],
                 'in_channels': 3,
                 'out_channels': 3,
                 'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
                 'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
                 'latent_channels': 4,
             }
             a_      :	Optional[Any]							=						self.dummy_input
             return init_dict, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             pass
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[Any]:
             pass
      @unittest.skipIf(torch_device == 'mps'  ,							'Gradient checkpointing skipped on MPS'  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[Any]:
             # enable deterministic behavior for gradient checkpointing
             a_							,	a_      :	Optional[int]							=						self.prepare_init_args_and_inputs_for_common()
             a_      :	Dict							=						self.model_class(**SCREAMING_SNAKE_CASE__  )
             model.to(SCREAMING_SNAKE_CASE__  )

             assert not model.is_gradient_checkpointing and model.training

             a_      :	Optional[int]							=						model(**SCREAMING_SNAKE_CASE__  ).sample
             # run the backwards pass on the model. For backwards pass, for simplicity purpose,
             # we won't calculate the loss and rather backprop on out.sum()
             model.zero_grad()

             a_      :	int							=						torch.randn_like(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						(out - labels).mean()
             loss.backward()

             # re-instantiate the model now enabling gradient checkpointing
             a_      :	List[str]							=						self.model_class(**SCREAMING_SNAKE_CASE__  )
             # clone model
             model_a.load_state_dict(model.state_dict()  )
             model_a.to(SCREAMING_SNAKE_CASE__  )
             model_a.enable_gradient_checkpointing()

             assert model_a.is_gradient_checkpointing and model_a.training

             a_      :	List[str]							=						model_a(**SCREAMING_SNAKE_CASE__  ).sample
             # run the backwards pass on the model. For backwards pass, for simplicity purpose,
             # we won't calculate the loss and rather backprop on out.sum()
             model_a.zero_grad()
             a_      :	Optional[Any]							=						(out_a - labels).mean()
             loss_a.backward()

             # compare the output and parameters gradients
             self.assertTrue((loss - loss_a).abs() < 1E-5  )
             a_      :	List[Any]							=						dict(model.named_parameters()  )
             a_      :	Dict							=						dict(model_a.named_parameters()  )
             for name, param in named_params.items():
                    self.assertTrue(torch_all_close(param.grad.data  ,							named_params_a[name].grad.data  ,							atol=5E-5  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             a_							,	a_      :	int							=						AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'  ,							output_loading_info=SCREAMING_SNAKE_CASE__  )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )
             self.assertEqual(len(loading_info['missing_keys']  )  ,							0  )

             model.to(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						model(**self.dummy_input  )

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

      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             a_      :	Any							=						AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'  )
             a_      :	str							=						model.to(SCREAMING_SNAKE_CASE__  )
             model.eval()

             if torch_device == "mps":
                    a_      :	Tuple							=						torch.manual_seed(0  )
             else:
                    a_      :	Any							=						torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(0  )

             a_      :	List[Any]							=						torch.randn(
                 1  ,							model.config.in_channels  ,							model.config.sample_size  ,							model.config.sample_size  ,							generator=torch.manual_seed(0  )  ,							)
             a_      :	Any							=						image.to(SCREAMING_SNAKE_CASE__  )
             with torch.no_grad():
                    a_      :	Tuple							=						model(SCREAMING_SNAKE_CASE__  ,							sample_posterior=SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ).sample

             a_      :	Tuple							=						output[0, -1, -3:, -3:].flatten().cpu()

             # 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.
             if torch_device == "mps":
                    a_      :	Optional[Any]							=						torch.tensor(
                        [
                            -4.0_078E-01,
                            -3.8_323E-04,
                            -1.2_681E-01,
                            -1.1_462E-01,
                            2.0_095E-01,
                            1.0_893E-01,
                            -8.8_247E-02,
                            -3.0_361E-01,
                            -9.8_644E-03,
                        ]  )
             elif torch_device == "cpu":
                    a_      :	List[Any]							=						torch.tensor(
                        [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]  )
             else:
                    a_      :	Tuple							=						torch.tensor(
                        [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]  )

             self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							rtol=1E-2  )  )
@slow
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Dict:
             return F"""gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE__  ) for s in shape]  )}.npy"""
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0  ,							SCREAMING_SNAKE_CASE__       : Tuple=(4, 3, 5_1_2, 5_1_2)  ,							SCREAMING_SNAKE_CASE__       : List[str]=False  )	->    str:
             a_      :	int							=						torch.floataa if fpaa else torch.floataa
             a_      :	str							=						torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )  )  ).to(SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  )
             return image
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Any="CompVis/stable-diffusion-v1-4"  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=False  )	->    List[Any]:
             a_      :	Optional[int]							=						'fp16' if fpaa else None
             a_      :	Any							=						torch.floataa if fpaa else torch.floataa

             a_      :	Dict							=						AutoencoderKL.from_pretrained(
                 SCREAMING_SNAKE_CASE__  ,							subfolder='vae'  ,							torch_dtype=SCREAMING_SNAKE_CASE__  ,							revision=SCREAMING_SNAKE_CASE__  ,							)
             model.to(SCREAMING_SNAKE_CASE__  ).eval()

             return model
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : str=0  )	->    Union[str, Any]:
             if torch_device == "mps":
                    return torch.manual_seed(SCREAMING_SNAKE_CASE__  )
             return torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )
      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [4_7, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]  )
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  )	->    Dict:
             a_      :	Any							=						self.get_sd_vae_model()
             a_      :	Tuple							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						self.get_generator(SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							sample_posterior=SCREAMING_SNAKE_CASE__  ).sample

             assert sample.shape == image.shape

             a_      :	List[Any]							=						sample[-1, -2:, -2:, :2].flatten().float().cpu()
             a_      :	List[str]							=						torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice  )

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=3E-3  )
      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
              [4_7, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
              # fmt: on
          ]  )
      @require_torch_gpu
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : List[str]  )	->    str:
             a_      :	Tuple							=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  ,							fpaa=SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						self.get_generator(SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							generator=SCREAMING_SNAKE_CASE__  ,							sample_posterior=SCREAMING_SNAKE_CASE__  ).sample

             assert sample.shape == image.shape

             a_      :	List[Any]							=						sample[-1, -2:, :2, -2:].flatten().float().cpu()
             a_      :	Optional[Any]							=						torch.tensor(SCREAMING_SNAKE_CASE__  )

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-2  )
      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [4_7, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : int  )	->    int:
             a_      :	List[Any]							=						self.get_sd_vae_model()
             a_      :	List[str]							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	Any							=						model(SCREAMING_SNAKE_CASE__  ).sample

             assert sample.shape == image.shape

             a_      :	str							=						sample[-1, -2:, -2:, :2].flatten().float().cpu()
             a_      :	str							=						torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice  )

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=3E-3  )
      @parameterized.expand(
          [
              # fmt: off
              [1_3, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
              [3_7, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
              # fmt: on
          ]  )
      @require_torch_gpu
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : List[str]  )	->    Any:
             a_      :	Optional[Any]							=						self.get_sd_vae_model()
             a_      :	Any							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  ,							shape=(3, 4, 6_4, 6_4)  )

             with torch.no_grad():
                    a_      :	int							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

             a_      :	Dict							=						sample[-1, -2:, :2, -2:].flatten().cpu()
             a_      :	Optional[int]							=						torch.tensor(SCREAMING_SNAKE_CASE__  )

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-3  )
      @parameterized.expand(
          [
              # fmt: off
              [2_7, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
              [1_6, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
              # fmt: on
          ]  )
      @require_torch_gpu
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    List[str]:
             a_      :	Union[str, Any]							=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__  )
             a_      :	Union[str, Any]							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  ,							shape=(3, 4, 6_4, 6_4)  ,							fpaa=SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	List[Any]							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

             a_      :	Tuple							=						sample[-1, -2:, :2, -2:].flatten().float().cpu()
             a_      :	Optional[Any]							=						torch.tensor(SCREAMING_SNAKE_CASE__  )

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=5E-3  )
      @parameterized.expand([(1_3,), (1_6,), (2_7,)]  )
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()  ,							reason='xformers is not required when using PyTorch 2.0.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    List[Any]:
             a_      :	Dict							=						self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  ,							shape=(3, 4, 6_4, 6_4)  ,							fpaa=SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	Any							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             model.enable_xformers_memory_efficient_attention()
             with torch.no_grad():
                    a_      :	Any							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-1  )
      @parameterized.expand([(1_3,), (1_6,), (3_7,)]  )
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()  ,							reason='xformers is not required when using PyTorch 2.0.'  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    str:
             a_      :	Tuple							=						self.get_sd_vae_model()
             a_      :	int							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  ,							shape=(3, 4, 6_4, 6_4)  )

             with torch.no_grad():
                    a_      :	List[Any]							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             model.enable_xformers_memory_efficient_attention()
             with torch.no_grad():
                    a_      :	int							=						model.decode(SCREAMING_SNAKE_CASE__  ).sample

             assert list(sample.shape  ) == [3, 3, 5_1_2, 5_1_2]

             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-2  )

      @parameterized.expand(
          [
              # fmt: off
              [3_3, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
              [4_7, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
              # fmt: on
          ]  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    List[Any]:
             a_      :	List[str]							=						self.get_sd_vae_model()
             a_      :	Dict							=						self.get_sd_image(SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[int]							=						self.get_generator(SCREAMING_SNAKE_CASE__  )

             with torch.no_grad():
                    a_      :	str							=						model.encode(SCREAMING_SNAKE_CASE__  ).latent_dist
                    a_      :	str							=						dist.sample(generator=SCREAMING_SNAKE_CASE__  )

             assert list(sample.shape  ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

             a_      :	Tuple							=						sample[0, -1, -3:, -3:].flatten().cpu()
             a_      :	Optional[int]							=						torch.tensor(SCREAMING_SNAKE_CASE__  )

             a_      :	List[Any]							=						3E-3 if torch_device != 'mps' else 1E-2
             assert torch_all_close(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=SCREAMING_SNAKE_CASE__  )






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


UpperCAmelCase_   :				Optional[int]						      = [
    # 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  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

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

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

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


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = 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.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


