
"""simple docstring"""


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint






def    lowerCAmelCase_   (		snake_case_		:			Optional[int]			)   ->int:
			# encoder.embeddings are double copied in original FLAVA
			return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()			)






def    lowerCAmelCase_   (		snake_case_		:			List[Any]							,    snake_case_		:			Tuple			)   ->List[Any]:
			lowerCamelCase__	:     Optional[int]              ={}

			for key, value in state_dict.items():
						if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
									continue

						lowerCamelCase__	:     Dict              =key.replace('heads.cmd.mim_head.cls.predictions'							,    'mmm_image_head'			)
						lowerCamelCase__	:     Optional[int]              =key.replace('heads.cmd.mlm_head.cls.predictions'							,    'mmm_text_head'			)
						lowerCamelCase__	:     List[Any]              =key.replace('heads.cmd.itm_head.cls'							,    'itm_head'			)
						lowerCamelCase__	:     Any              =key.replace('heads.cmd.itm_head.pooler'							,    'itm_head.pooler'			)
						lowerCamelCase__	:     int              =key.replace('heads.cmd.clip_head.logit_scale'							,    'flava.logit_scale'			)
						lowerCamelCase__	:     Tuple              =key.replace('heads.fairseq_mlm.cls.predictions'							,    'mlm_head'			)
						lowerCamelCase__	:     Optional[int]              =key.replace('heads.imagenet.mim_head.cls.predictions'							,    'mim_head'			)
						lowerCamelCase__	:     str              =key.replace('mm_text_projection'							,    'flava.text_to_mm_projection'			)
						lowerCamelCase__	:     Any              =key.replace('mm_image_projection'							,    'flava.image_to_mm_projection'			)
						lowerCamelCase__	:     Tuple              =key.replace('image_encoder.module'							,    'flava.image_model'			)
						lowerCamelCase__	:     List[Any]              =key.replace('text_encoder.module'							,    'flava.text_model'			)
						lowerCamelCase__	:     str              =key.replace('mm_encoder.module.encoder.cls_token'							,    'flava.multimodal_model.cls_token'			)
						lowerCamelCase__	:     Dict              =key.replace('mm_encoder.module'							,    'flava.multimodal_model'			)
						lowerCamelCase__	:     Dict              =key.replace('text_projection'							,    'flava.text_projection'			)
						lowerCamelCase__	:     Optional[int]              =key.replace('image_projection'							,    'flava.image_projection'			)

						lowerCamelCase__	:     Union[str, Any]              =value.float()

			for key, value in codebook_state_dict.items():
						lowerCamelCase__	:     Any              =value

			return upgrade






@torch.no_grad()
def    lowerCAmelCase_   (		snake_case_		:			Any							,    snake_case_		:			Any							,    snake_case_		:			str							,    snake_case_		:			Any=None			)   ->Any:
			if config_path is not None:
						lowerCamelCase__	:     Dict              =FlavaConfig.from_pretrained(_lowerCamelCase			)
			else:
						lowerCamelCase__	:     Union[str, Any]              =FlavaConfig()

			lowerCamelCase__	:     Tuple              =FlavaForPreTraining(_lowerCamelCase			).eval()

			lowerCamelCase__	:     int              =convert_dalle_checkpoint(_lowerCamelCase							,    _lowerCamelCase							,    save_checkpoint=_lowerCamelCase			)

			if os.path.exists(_lowerCamelCase			):
						lowerCamelCase__	:     Optional[Any]              =torch.load(_lowerCamelCase							,    map_location='cpu'			)
			else:
						lowerCamelCase__	:     Dict              =torch.hub.load_state_dict_from_url(_lowerCamelCase							,    map_location='cpu'			)

			lowerCamelCase__	:     Union[str, Any]              =upgrade_state_dict(_lowerCamelCase							,    _lowerCamelCase			)
			hf_model.load_state_dict(_lowerCamelCase			)
			lowerCamelCase__	:     Dict              =hf_model.state_dict()
			lowerCamelCase__	:     str              =count_parameters(_lowerCamelCase			)
			lowerCamelCase__	:     Dict              =count_parameters(_lowerCamelCase			) + count_parameters(_lowerCamelCase			)

			assert torch.allclose(_lowerCamelCase							,    _lowerCamelCase							,    atol=1E-3			)

			hf_model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
							lowerCAmelCase								=						argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
							parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase								=						parser.parse_args()

							convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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    UpperCAmelCase	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				KandinskyVaaPipeline
 __lowercase   		=				[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __lowercase   		=				["""image_embeds""", """negative_image_embeds"""]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :int							)->							List[Any]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Union[str, Any]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     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(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     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      UpperCAmelCase_							(				self      :Dict							)->							Union[str, Any]:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :Dict							)->							Optional[Any]:
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			DDIMScheduler(
         num_train_timesteps=10_00						,       beta_schedule="linear"						,       beta_start=0.0_0_0_8_5						,       beta_end=0.0_1_2						,       clip_sample=lowercase_						,       set_alpha_to_one=lowercase_						,       steps_offset=1						,       prediction_type="epsilon"						,       thresholding=lowercase_						,       )

     A__				=			{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }
     return components
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :int						,       lowercase_      :Union[str, Any]=0							)->							Dict:
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__				=			floats_tensor((1, self.text_embedder_hidden_size)						,       rng=random.Random(seed + 1							)							).to(
         lowercase_							)
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)
     A__				=			{
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "guidance_scale": 4.0,
         "num_inference_steps": 2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

     A__				=			self.pipeline_class(**lowercase_							)
     A__				=			pipe.to(lowercase_							)

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			pipe(**self.get_dummy_inputs(lowercase_							)							)
     A__				=			output.images

     A__				=			pipe(
         **self.get_dummy_inputs(lowercase_							)						,       return_dict=lowercase_						,       )[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_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]							)

     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    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :Any							)->							List[Any]:
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"							)

     A__				=			KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"						,       torch_dtype=torch.floataa							)
     pipe_prior.to(lowercase_							)

     A__				=			KandinskyVaaPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder"						,       torch_dtype=torch.floataa							)
     A__				=			pipeline.to(lowercase_							)
     pipeline.set_progress_bar_config(disable=lowercase_							)

     A__				=			"red cat, 4k photo"

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__,	A__				=			pipe_prior(
         lowercase_						,       generator=lowercase_						,       num_inference_steps=5						,       negative_prompt=""						,       ).to_tuple()

     A__				=			torch.Generator(device="cuda"							).manual_seed(0							)
     A__				=			pipeline(
         image_embeds=lowercase_						,       negative_image_embeds=lowercase_						,       generator=lowercase_						,       num_inference_steps=1_00						,       output_type="np"						,       )

     A__				=			output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowercase_						,       lowercase_							)



