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

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowercase__ 	=     (
    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
)

lowercase__ 	=     (
    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
)

lowercase__ 	=     [
    '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:
				a__:					str      							=     k.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
		return k







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->BigBirdPegasusForConditionalGeneration:
		a__:					List[str]      							=     BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE       )
		a__:					Tuple      							=     BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
		a__:					int      							=     torch_model.state_dict()
		a__:					List[Any]      							=     {}

		# separating decoder weights
		a__:					List[Any]      							=     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'       )}
		a__:					str      							=     {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'       ):
				a__:					str      							=     [k.endswith(_SCREAMING_SNAKE_CASE       ) for ending in KEYS_TO_IGNORE]
				if any(_SCREAMING_SNAKE_CASE       ):
						continue
				a__:					List[str]      							=     DECODER_PATTERNS
				a__:					Tuple      							=     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']       ):
						a__:					str      							=     v.T
				a__:					List[Any]      							=     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'       ):
				a__:					int      							=     [k.endswith(_SCREAMING_SNAKE_CASE       ) for ending in KEYS_TO_IGNORE]
				if any(_SCREAMING_SNAKE_CASE       ):
						continue
				a__:					Any      							=     REMAINING_PATTERNS
				a__:					int      							=     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']       ):
						a__:					Union[str, Any]      							=     v.T
				a__:					Tuple      							=     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}'

		a__:					List[Any]      							=     mapping['model.embed_positions.weight']
		a__:					Any      							=     mapping.pop('model.embed_positions.weight'       )
		a__:					int      							=     torch_model.load_state_dict(_SCREAMING_SNAKE_CASE		,					strict=_SCREAMING_SNAKE_CASE       )
		a__:					Tuple      							=     [
		    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:
		a__:					Dict      							=     tf.train.list_variables(_SCREAMING_SNAKE_CASE       )
		a__:					Dict      							=     {}
		a__:					Dict      							=     ['global_step']
		for name, shape in tqdm(_SCREAMING_SNAKE_CASE		,					desc='converting tf checkpoint to dict'       ):
				a__:					List[Any]      							=     any(pat in name for pat in ignore_name       )
				if skip_key:
						continue
				a__:					Any      							=     tf.train.load_variable(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
				a__:					str      							=     array
		return tf_weights







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
		a__:					Any      							=     get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE       )
		a__:					Tuple      							=     convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
		torch_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	lowercase__ 	=     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.')
	lowercase__ 	=     parser.parse_args()
	lowercase__ 	=     {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     UNetaDModel(
            block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     CLIPTextConfig(
            bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



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





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


