



import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case						:Any						=					logging.get_logger(__name__)





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
				__a							=    os.path.abspath(_UpperCAmelCase    )
				logger.info(f'Converting TensorFlow checkpoint from {tf_path}'    )
				# Load weights from TF model
				__a							=    tf.train.list_variables(_UpperCAmelCase    )
				__a							=    []
				__a							=    []
				__a							=    []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								__a							=    full_name.split('''/'''    )
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(f'Skipping non-model layer {full_name}'    )
												continue
								if "optimizer" in full_name:
												logger.info(f'Skipping optimization layer {full_name}'    )
												continue
								if name[0] == "model":
												# ignore initial 'model'
												__a							=    name[1:]
								# figure out how many levels deep the name is
								__a							=    0
								for _name in name:
												if _name.startswith('''layer_with_weights'''    ):
																depth += 1
												else:
																break
								layer_depth.append(_UpperCAmelCase    )
								# read data
								__a							=    tf.train.load_variable(_UpperCAmelCase       ,			_UpperCAmelCase    )
								names.append('''/'''.join(_UpperCAmelCase    )    )
								arrays.append(_UpperCAmelCase    )
				logger.info(f'Read a total of {len(_UpperCAmelCase    ):,} layers'    )

				# Sanity check
				if len(set(_UpperCAmelCase    )    ) != 1:
								raise ValueError(f'Found layer names with different depths (layer depth {list(set(_UpperCAmelCase    )    )})'    )
				__a							=    list(set(_UpperCAmelCase    )    )[0]
				if layer_depth != 1:
								raise ValueError(
								    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
								    ''' heads.'''    )

				# convert layers
				logger.info('''Converting weights...'''    )
				for full_name, array in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ):
								__a							=    full_name.split('''/'''    )
								__a							=    model
								__a							=    []
								for i, m_name in enumerate(_UpperCAmelCase    ):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith('''layer_with_weights'''    ):
																__a							=    int(m_name.split('''-'''    )[-1]    )
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(['''embeddings''', '''LayerNorm''']    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''embeddings'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''LayerNorm'''    )
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(['''encoder''', '''layer''', str(layer_num - 4    )]    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''encoder'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''layer'''    )
																				__a							=    pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(['''pooler''', '''dense''']    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''pooler'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''dense'''    )
												elif m_name == "embeddings":
																trace.append('''embeddings'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''embeddings'''    )
																if layer_num == 0:
																				trace.append('''word_embeddings'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''word_embeddings'''    )
																elif layer_num == 1:
																				trace.append('''position_embeddings'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''position_embeddings'''    )
																elif layer_num == 2:
																				trace.append('''token_type_embeddings'''    )
																				__a							=    getattr(_UpperCAmelCase       ,			'''token_type_embeddings'''    )
																else:
																				raise ValueError(f'Unknown embedding layer with name {full_name}'    )
																trace.append('''weight'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''weight'''    )
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(['''attention''', '''self''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''attention'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''self'''    )
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(['''attention''', '''output''', '''LayerNorm''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''attention'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''output'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''LayerNorm'''    )
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(['''attention''', '''output''', '''dense''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''attention'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''output'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''dense'''    )
												elif m_name == "_output_dense":
																# output dense
																trace.extend(['''output''', '''dense''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''output'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''dense'''    )
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(['''output''', '''LayerNorm''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''output'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''LayerNorm'''    )
												elif m_name == "_key_dense":
																# attention key
																trace.append('''key'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''key'''    )
												elif m_name == "_query_dense":
																# attention query
																trace.append('''query'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''query'''    )
												elif m_name == "_value_dense":
																# attention value
																trace.append('''value'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''value'''    )
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(['''intermediate''', '''dense''']    )
																__a							=    getattr(_UpperCAmelCase       ,			'''intermediate'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''dense'''    )
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append('''output'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''output'''    )
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append('''bias'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''bias'''    )
												elif m_name in ["kernel", "gamma"]:
																trace.append('''weight'''    )
																__a							=    getattr(_UpperCAmelCase       ,			'''weight'''    )
												else:
																logger.warning(f'Ignored {m_name}'    )
        # for certain layers reshape is necessary
								__a							=    '''.'''.join(_UpperCAmelCase    )
								if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''       ,			_UpperCAmelCase    ) or re.match(
								    R'''(\S+)\.attention\.output\.dense\.weight'''       ,			_UpperCAmelCase    ):
												__a							=    array.reshape(pointer.data.shape    )
								if "kernel" in full_name:
												__a							=    array.transpose()
								if pointer.shape == array.shape:
												__a							=    torch.from_numpy(_UpperCAmelCase    )
								else:
												raise ValueError(
												    f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
												    f' {array.shape}'    )
								logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'    )
				return model





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
				# Instantiate model
				logger.info(f'Loading model based on config from {config_path}...'    )
				__a							=    BertConfig.from_json_file(_UpperCAmelCase    )
				__a							=    BertModel(_UpperCAmelCase    )

				# Load weights from checkpoint
				logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'    )
				load_tfa_weights_in_bert(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    )

				# Save pytorch-model
				logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'    )
				torch.save(model.state_dict()       ,			_UpperCAmelCase    )


if __name__ == "__main__":
	__snake_case						:Dict						=					argparse.ArgumentParser()
	parser.add_argument(
	    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
	)
	parser.add_argument(
	    '''--bert_config_file''',
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''',
	    type=str,
	    required=True,
	    help='''Path to the output PyTorch model (must include filename).''',
	)
	__snake_case						:List[Any]						=					parser.parse_args()
	convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class    _A	(     __UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   str						 =						KandinskyVaaImgaImgPipeline
       UpperCamelCase__  :   Optional[Any]						 =						['''image_embeds''', '''negative_image_embeds''', '''image''']
       UpperCamelCase__  :   Dict						 =						[
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       UpperCamelCase__  :   Any						 =						[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase__  :   List[Any]						 =						False



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           return 32



       @property
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return self.time_input_dim



       @property
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return self.time_input_dim * 4



       @property
       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           return 100



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           torch.manual_seed(0)

           __a							=    {
               '''in_channels''': 4,
               # Out channels is double in channels because predicts mean and variance
               '''out_channels''': 8,
               '''addition_embed_type''': '''image''',
               '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
               '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
               '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
               '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
               '''layers_per_block''': 1,
               '''encoder_hid_dim''': self.text_embedder_hidden_size,
               '''encoder_hid_dim_type''': '''image_proj''',
               '''cross_attention_dim''': self.cross_attention_dim,
               '''attention_head_dim''': 4,
               '''resnet_time_scale_shift''': '''scale_shift''',
               '''class_embed_type''': None,
           }

           __a							=    UNetaDConditionModel(**__SCREAMING_SNAKE_CASE)
           return model



       @property
       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
               "in_channels": 3,
               "latent_channels": 4,
               "layers_per_block": 1,
               "norm_num_groups": 8,
               "norm_type": "spatial",
               "num_vq_embeddings": 12,
               "out_channels": 3,
               "up_block_types": [
                   "AttnUpDecoderBlock2D",
                   "UpDecoderBlock2D",
               ],
               "vq_embed_dim": 4,
           }



       @property
       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           torch.manual_seed(0)
           __a							=    VQModel(**self.dummy_movq_kwargs)
           return model



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.dummy_unet
           __a							=    self.dummy_movq

           __a							=    {
               '''num_train_timesteps''': 1_000,
               '''beta_schedule''': '''linear''',
               '''beta_start''': 0.0_00_85,
               '''beta_end''': 0.0_12,
               '''clip_sample''': False,
               '''set_alpha_to_one''': False,
               '''steps_offset''': 0,
               '''prediction_type''': '''epsilon''',
               '''thresholding''': False,
           }

           __a							=    DDIMScheduler(**__SCREAMING_SNAKE_CASE)

           __a							=    {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''movq''': movq,
           }

           return components



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    floats_tensor((1, self.text_embedder_hidden_size)					,		rng=random.Random(seed + 1)).to(
               __SCREAMING_SNAKE_CASE)
           # create init_image
           __a							=    floats_tensor((1, 3, 64, 64)					,		rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a							=    image.cpu().permute(0					,		2					,		3					,		1)[0]
           __a							=    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert('''RGB''').resize((256, 256))

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    {
               '''image''': init_image,
               '''image_embeds''': image_embeds,
               '''negative_image_embeds''': negative_image_embeds,
               '''generator''': generator,
               '''height''': 64,
               '''width''': 64,
               '''num_inference_steps''': 10,
               '''guidance_scale''': 7.0,
               '''strength''': 0.2,
               '''output_type''': '''np''',
           }
           return inputs





       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    '''cpu'''

           __a							=    self.get_dummy_components()

           __a							=    self.pipeline_class(**__SCREAMING_SNAKE_CASE)
           __a							=    pipe.to(__SCREAMING_SNAKE_CASE)

           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE))
           __a							=    output.images

           __a							=    pipe(
               **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[0]

           __a							=    image[0, -3:, -3:, -1]
           __a							=    image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           __a							=    np.array(
               [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63])
           assert (
               np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
           ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
           assert (
               np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
           ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'


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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/kandinskyv22/kandinskyv22_img2img_frog.npy''')

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
           __a							=    '''A red cartoon frog, 4k'''

           __a							=    KandinskyVaaPriorPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-prior'''					,		torch_dtype=torch.floataa)
           pipe_prior.to(__SCREAMING_SNAKE_CASE)

           __a							=    KandinskyVaaImgaImgPipeline.from_pretrained(
               '''kandinsky-community/kandinsky-2-2-decoder'''					,		torch_dtype=torch.floataa)
           __a							=    pipeline.to(__SCREAMING_SNAKE_CASE)

           pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    torch.Generator(device='''cpu''').manual_seed(0)
           __a	,       __a							=    pipe_prior(
               __SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=5					,		negative_prompt=''''''					,		).to_tuple()

           __a							=    pipeline(
               image=__SCREAMING_SNAKE_CASE					,		image_embeds=__SCREAMING_SNAKE_CASE					,		negative_image_embeds=__SCREAMING_SNAKE_CASE					,		generator=__SCREAMING_SNAKE_CASE					,		num_inference_steps=100					,		height=768					,		width=768					,		strength=0.2					,		output_type='''np'''					,		)

           __a							=    output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





