





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase_        =							(
    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
)

lowerCAmelCase_        =							(
    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
)

lowerCAmelCase_        =							[
    '''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 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Optional[int]:






				"""simple docstring"""


				for tf_name, hf_name in patterns:
								snake_case_		:       Optional[int]           =    k.replace(_UpperCamelCase					,							_UpperCamelCase					)
				return k






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		BigBirdPegasusForConditionalGeneration:






				"""simple docstring"""


				snake_case_		:       Tuple           =    BigBirdPegasusConfig(**_UpperCamelCase					)
				snake_case_		:       List[str]           =    BigBirdPegasusForConditionalGeneration(_UpperCamelCase					)
				snake_case_		:       Tuple           =    torch_model.state_dict()
				snake_case_		:       Tuple           =    {}

				# separating decoder weights
				snake_case_		:       Dict           =    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''					)}
				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'''					):
								snake_case_		:       Optional[int]           =    [k.endswith(_UpperCamelCase					) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase					):
												continue
								snake_case_		:       str           =    DECODER_PATTERNS
								snake_case_		:       Optional[int]           =    rename_state_dict_key(_UpperCamelCase					,							_UpperCamelCase					)
								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_		:       List[str]           =    v.T
								snake_case_		:       Dict           =    torch.from_numpy(_UpperCamelCase					)
								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_		:       Union[str, Any]           =    [k.endswith(_UpperCamelCase					) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase					):
												continue
								snake_case_		:       Optional[Any]           =    REMAINING_PATTERNS
								snake_case_		:       int           =    rename_state_dict_key(_UpperCamelCase					,							_UpperCamelCase					)
								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_		:       List[str]           =    v.T
								snake_case_		:       Dict           =    torch.from_numpy(_UpperCamelCase					)
								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_		:       Dict           =    mapping['''model.embed_positions.weight''']
				snake_case_		:       Tuple           =    mapping.pop('''model.embed_positions.weight'''					)
				snake_case_     ,				snake_case_		:       Union[str, Any]           =    torch_model.load_state_dict(_UpperCamelCase					,							strict=_UpperCamelCase					)
				snake_case_		:       Union[str, Any]           =    [
				    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 	lowerCamelCase_		(							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


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






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    get_tf_weights_as_numpy(_UpperCamelCase					)
				snake_case_		:       Optional[Any]           =    convert_bigbird_pegasus(_UpperCamelCase					,							_UpperCamelCase					)
				torch_model.save_pretrained(_UpperCamelCase					)


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






'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Union[str, Any]        =						StableDiffusionXLImgaImgPipeline
       A						:				Any        =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       A						:				str        =						PipelineTesterMixin.required_optional_params - {'''latents'''}
       A						:				List[str]        =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       A						:				Dict        =						IMAGE_TO_IMAGE_IMAGE_PARAMS
       A						:				int        =						IMAGE_TO_IMAGE_IMAGE_PARAMS







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						UNetaDConditionModel(
                 block_out_channels=(32, 64),							layers_per_block=2,							sample_size=32,							in_channels=4,							out_channels=4,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),							attention_head_dim=(2, 4),							use_linear_projection=A,							addition_embed_type='text_time',							addition_time_embed_dim=8,							transformer_layers_per_block=(1, 2),							projection_class_embeddings_input_dim=80,							cross_attention_dim=64,							)
             SCREAMING_SNAKE_CASE   :      str    					=						EulerDiscreteScheduler(
                 beta_start=0.0_00_85,							beta_end=0.0_12,							steps_offset=1,							beta_schedule='scaled_linear',							timestep_spacing='leading',							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						AutoencoderKL(
                 block_out_channels=[32, 64],							in_channels=3,							out_channels=3,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],							latent_channels=4,							sample_size=128,							)
             torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						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=1_000,							hidden_act='gelu',							projection_dim=32,							)
             SCREAMING_SNAKE_CASE   :      int    					=						CLIPTextModel(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip',							local_files_only=A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						CLIPTextModelWithProjection(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip',							local_files_only=A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						{
                 'unet': unet,
                 'scheduler': scheduler,
                 'vae': vae,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'text_encoder_2': text_encoder_a,
                 'tokenizer_2': tokenizer_a,
                 # "safety_checker": None,
                 # "feature_extractor": None,
             }
             return components







       def        UpperCamelCase_							(      self,							A,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						floats_tensor((1, 3, 32, 32),							rng=random.Random(A  )  ).to(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						image / 2 + 0.5
             if str(A  ).startswith('mps'  ):
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.manual_seed(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{
                 'prompt': 'A painting of a squirrel eating a burger',
                 'image': image,
                 'generator': generator,
                 'num_inference_steps': 2,
                 'guidance_scale': 5.0,
                 'output_type': 'numpy',
                 'strength': 0.75,
             }
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'cpu'  # ensure determinism for the device-dependent torch.Generator
             SCREAMING_SNAKE_CASE   :      str    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						StableDiffusionXLImgaImgPipeline(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						sd_pipe.to(A  )
             sd_pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Any    					=						sd_pipe(**A  ).images
             SCREAMING_SNAKE_CASE   :      Dict    					=						image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]  )

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().test_attention_slicing_forward_pass(expected_max_diff=3E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().test_inference_batch_single_identical(expected_max_diff=3E-3  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_dummy_components()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						StableDiffusionXLImgaImgPipeline(**A  )
             SCREAMING_SNAKE_CASE   :      str    					=						sd_pipe.to(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sd_pipe.to(A  )
             sd_pipe.set_progress_bar_config(disable=A  )

             # forward without prompt embeds
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						3 * ['this is a negative prompt']
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						negative_prompt
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						3 * [inputs['prompt']]

             SCREAMING_SNAKE_CASE   :      int    					=						sd_pipe(**A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						output.images[0, -3:, -3:, -1]

             # forward with prompt embeds
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_dummy_inputs(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						3 * ['this is a negative prompt']
             SCREAMING_SNAKE_CASE   :      int    					=						3 * [inputs.pop('prompt'  )]

             (
                 (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    (
                 SCREAMING_SNAKE_CASE
             )					,    
             )   :      Optional[Any]    					=						sd_pipe.encode_prompt(A,							negative_prompt=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						sd_pipe(
                 **A,							prompt_embeds=A,							negative_prompt_embeds=A,							pooled_prompt_embeds=A,							negative_pooled_prompt_embeds=A,							)
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						output.images[0, -3:, -3:, -1]

             # make sure that it's equal
             assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()  ).max() < 1E-4






@slow
@require_torch_gpu
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A,							A="cpu",							A=torch.floataa,							A=0  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						torch.Generator(device=A  ).manual_seed(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.random.RandomState(A  ).standard_normal((1, 4, 64, 64)  )
             SCREAMING_SNAKE_CASE   :      str    					=						torch.from_numpy(A  ).to(device=A,							dtype=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{
                 'prompt': 'a photograph of an astronaut riding a horse',
                 'latents': latents,
                 'generator': generator,
                 'num_inference_steps': 3,
                 'guidance_scale': 7.5,
                 'output_type': 'numpy',
             }
             return inputs







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_inputs(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						pipe(**A  ).images
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)
             SCREAMING_SNAKE_CASE   :      Dict    					=						np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]  )
             assert np.abs(image_slice - expected_slice  ).max() < 7E-3


