
from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_A     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			try:
						with open(__lowerCAmelCase					,							"rb"       ) as flax_state_f:
									lowerCAmelCase_      			=			from_bytes(__lowerCAmelCase					,							flax_state_f.read()       )
			except UnpicklingError as e:
						try:
									with open(__lowerCAmelCase       ) as f:
												if f.read().startswith("version"       ):
															raise OSError(
															    "You seem to have cloned a repository without having git-lfs installed. Please"
															    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
															    " folder you cloned."       )
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """       )

			return load_flax_weights_in_pytorch_model(__lowerCAmelCase					,							__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
						    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
						    " instructions."       )
						raise

			# check if we have bf16 weights
			lowerCAmelCase_      			=			flatten_dict(jax.tree_util.tree_map(lambda __lowerCAmelCase       : x.dtype == jnp.bfloataa					,							__lowerCAmelCase       )       ).values()
			if any(__lowerCAmelCase       ):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
						    "before loading those in PyTorch model."       )
						lowerCAmelCase_      			=			jax.tree_util.tree_map(
						    lambda __lowerCAmelCase       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params					,							__lowerCAmelCase       )

			lowerCAmelCase_      			=			""

			lowerCAmelCase_      			=			flatten_dict(__lowerCAmelCase					,							sep="."       )
			lowerCAmelCase_      			=			pt_model.state_dict()

			# keep track of unexpected & missing keys
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			set(pt_model_dict.keys()       )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						lowerCAmelCase_      			=			flax_key_tuple.split("."       )

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									lowerCAmelCase_      			=			flax_key_tuple_array[:-1] + ["weight"]
									lowerCAmelCase_      			=			jnp.transpose(__lowerCAmelCase					,							(3, 2, 0, 1)       )
						elif flax_key_tuple_array[-1] == "kernel":
									lowerCAmelCase_      			=			flax_key_tuple_array[:-1] + ["weight"]
									lowerCAmelCase_      			=			flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									lowerCAmelCase_      			=			flax_key_tuple_array[:-1] + ["weight"]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(__lowerCAmelCase       ):
												lowerCAmelCase_      			=			(
												    flax_key_tuple_string.replace("_0"					,							".0"       )
												    .replace("_1"					,							".1"       )
												    .replace("_2"					,							".2"       )
												    .replace("_3"					,							".3"       )
												    .replace("_4"					,							".4"       )
												    .replace("_5"					,							".5"       )
												    .replace("_6"					,							".6"       )
												    .replace("_7"					,							".7"       )
												    .replace("_8"					,							".8"       )
												    .replace("_9"					,							".9"       )
												)

						lowerCAmelCase_      			=			".".join(__lowerCAmelCase       )

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
												    F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""       )
									else:
												# add weight to pytorch dict
												lowerCAmelCase_      			=			np.asarray(__lowerCAmelCase       ) if not isinstance(__lowerCAmelCase					,							np.ndarray       ) else flax_tensor
												lowerCAmelCase_      			=			torch.from_numpy(__lowerCAmelCase       )
												# remove from missing keys
												missing_keys.remove(__lowerCAmelCase       )
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(__lowerCAmelCase       )

			pt_model.load_state_dict(__lowerCAmelCase       )

			# re-transform missing_keys to list
			lowerCAmelCase_      			=			list(__lowerCAmelCase       )

			if len(__lowerCAmelCase       ) > 0:
						logger.warning(
						    "Some weights of the Flax model were not used when initializing the PyTorch model"
						    F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
						    F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
						    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
						    F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
						    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
						    " FlaxBertForSequenceClassification model)."       )
			if len(__lowerCAmelCase       ) > 0:
						logger.warning(
						    F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
						    F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
						    " use it for predictions and inference."       )

			return pt_model





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableUnCLIPPipeline
				_lowercase    	=TEXT_TO_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowercase    	=False




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			32
							lowerCAmelCase_      			=			embedder_hidden_size

							# prior components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=_UpperCamelCase       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			PriorTransformer(
							    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=_UpperCamelCase       ,       num_layers=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDPMScheduler(
							    variance_type="fixed_small_log"       ,       prediction_type="sample"       ,       num_train_timesteps=1_000       ,       clip_sample=_UpperCamelCase       ,       clip_sample_range=5.0       ,       beta_schedule="squaredcos_cap_v2"       ,       )

							# regular denoising components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
							lowerCAmelCase_      			=			DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type="projection"       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=_UpperCamelCase       ,       layers_per_block=1       ,       upcast_attention=_UpperCamelCase       ,       use_linear_projection=_UpperCamelCase       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDIMScheduler(
							    beta_schedule="scaled_linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       prediction_type="v_prediction"       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			AutoencoderKL()

							lowerCAmelCase_      			=			{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Tuple:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )





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




				def   __a		(      self  )       ->   List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"  )

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_      			=			pipe("anime turle"       ,       generator=_UpperCamelCase       ,       output_type="np"  )

							lowerCAmelCase_      			=			output.images[0]

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

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			pipe(
							    "anime turtle"       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type="np"       ,       )

							lowerCAmelCase_      			=			torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9



