


"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__SCREAMING_SNAKE_CASE							:    Any		=    [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

__SCREAMING_SNAKE_CASE							:    Tuple		=    (
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

__SCREAMING_SNAKE_CASE							:    List[str]		=    (
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

__SCREAMING_SNAKE_CASE							:    Optional[int]		=    [
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							for tf_name, hf_name in patterns:
														snake_case_           =		k.replace(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							return k







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				BigBirdPegasusForConditionalGeneration:
							snake_case_           =		BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
							snake_case_           =		BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		torch_model.state_dict()
							snake_case_           =		{}

							# separating decoder weights
							snake_case_           =		{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""				)}
							snake_case_           =		{k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""				)}

							for k, v in tqdm(decoder_weights.items()				, """tf -> hf conversion"""				):
														snake_case_           =		[k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
														if any(_SCREAMING_SNAKE_CASE				):
																					continue
														snake_case_           =		DECODER_PATTERNS
														snake_case_           =		rename_state_dict_key(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														if new_k not in state_dict:
																					raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""				)
														if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]				):
																					snake_case_           =		v.T
														snake_case_           =		torch.from_numpy(_SCREAMING_SNAKE_CASE				)
														assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

							for k, v in tqdm(remaining_weights.items()				, """tf -> hf conversion"""				):
														snake_case_           =		[k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
														if any(_SCREAMING_SNAKE_CASE				):
																					continue
														snake_case_           =		REMAINING_PATTERNS
														snake_case_           =		rename_state_dict_key(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
																					raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""				)
														if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]				):
																					snake_case_           =		v.T
														snake_case_           =		torch.from_numpy(_SCREAMING_SNAKE_CASE				)
														if k != "pegasus/embeddings/position_embeddings":
																					assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

							snake_case_           =		mapping["""model.embed_positions.weight"""]
							snake_case_           =		mapping.pop("""model.embed_positions.weight"""				)
							snake_case_       ,						snake_case_           =		torch_model.load_state_dict(_SCREAMING_SNAKE_CASE				, strict=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		[
							    k
							    for k in missing
							    if k
							    not in [
							        """final_logits_bias""",
							        """model.encoder.embed_tokens.weight""",
							        """model.decoder.embed_tokens.weight""",
							        """lm_head.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 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Dict:
							snake_case_           =		tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		{}
							snake_case_           =		["""global_step"""]
							for name, shape in tqdm(_SCREAMING_SNAKE_CASE				, desc="""converting tf checkpoint to dict"""				):
														snake_case_           =		any(pat in name for pat in ignore_name				)
														if skip_key:
																					continue
														snake_case_           =		tf.train.load_variable(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
														snake_case_           =		array
							return tf_weights







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							snake_case_           =		get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    List[str]		=    argparse.ArgumentParser()
				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.')
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    parser.parse_args()
				__SCREAMING_SNAKE_CASE							:    Dict		=    {}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



							@property
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														return (3, 32, 32)



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

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



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



