import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__UpperCamelCase	: List[Any]    		=  (
    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
)

__UpperCamelCase	: 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
)

__UpperCamelCase	: Optional[Any]    		=  [
    '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			(					_lowercase	,						_lowercase		):
						for tf_name, hf_name in patterns:
												SCREAMING_SNAKE_CASE							:					str           =							k.replace(_lowercase	,						_lowercase		)
						return k
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							BigBirdPegasusConfig(**_lowercase		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							BigBirdPegasusForConditionalGeneration(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch_model.state_dict()
						SCREAMING_SNAKE_CASE							:					Any           =							{}

						# separating decoder weights
						SCREAMING_SNAKE_CASE							:					str           =							{k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''		)}
						SCREAMING_SNAKE_CASE							:					Dict           =							{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'''		):
												SCREAMING_SNAKE_CASE							:					int           =							[k.endswith(_lowercase		) for ending in KEYS_TO_IGNORE]
												if any(_lowercase		):
																		continue
												SCREAMING_SNAKE_CASE							:					Tuple           =							DECODER_PATTERNS
												SCREAMING_SNAKE_CASE							:					Tuple           =							rename_state_dict_key(_lowercase	,						_lowercase		)
												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''']		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							v.T
												SCREAMING_SNAKE_CASE							:					int           =							torch.from_numpy(_lowercase		)
												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'''		):
												SCREAMING_SNAKE_CASE							:					str           =							[k.endswith(_lowercase		) for ending in KEYS_TO_IGNORE]
												if any(_lowercase		):
																		continue
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							REMAINING_PATTERNS
												SCREAMING_SNAKE_CASE							:					Dict           =							rename_state_dict_key(_lowercase	,						_lowercase		)
												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''']		):
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							v.T
												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.from_numpy(_lowercase		)
												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}"""

						SCREAMING_SNAKE_CASE							:					Dict           =							mapping['''model.embed_positions.weight''']
						SCREAMING_SNAKE_CASE							:					Dict           =							mapping.pop('''model.embed_positions.weight'''		)
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Any           =							torch_model.load_state_dict(_lowercase	,						strict=_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							[
						    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			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							tf.train.list_variables(_lowercase		)
						SCREAMING_SNAKE_CASE							:					Any           =							{}
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							['''global_step''']
						for name, shape in tqdm(_lowercase	,						desc='''converting tf checkpoint to dict'''		):
												SCREAMING_SNAKE_CASE							:					List[Any]           =							any(pat in name for pat in ignore_name		)
												if skip_key:
																		continue
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tf.train.load_variable(_lowercase	,						_lowercase		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							array
						return tf_weights
def    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							get_tf_weights_as_numpy(_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							convert_bigbird_pegasus(_lowercase	,						_lowercase		)
						torch_model.save_pretrained(_lowercase		)


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






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__UpperCamelCase	: str    		=  False





class       lowercase__		(     unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Dict           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							VersatileDiffusionPipeline.from_pretrained(UpperCamelCase__					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							generator.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe.dual_guided(
												    prompt='''first prompt'''					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=2					,	output_type='''numpy'''					,	).images

												assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					,	torch_dtype=torch.floataa		)
												pipe.to(UpperCamelCase__		)
												pipe.set_progress_bar_config(disable=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''cyberpunk 2077'''
												SCREAMING_SNAKE_CASE							:					Tuple           =							load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							pipe.dual_guided(
												    prompt=UpperCamelCase__					,	image=UpperCamelCase__					,	text_to_image_strength=0.75					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''					,	).images

												SCREAMING_SNAKE_CASE							:					Tuple           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					int           =							np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''A painting of a squirrel eating a burger '''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Any           =							pipe.text_to_image(
												    prompt=UpperCamelCase__					,	generator=UpperCamelCase__					,	guidance_scale=7.5					,	num_inference_steps=50					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					List[str]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Any           =							np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe.image_variation(UpperCamelCase__					,	generator=UpperCamelCase__					,	output_type='''numpy'''		).images

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]		)

												assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1





