



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 			_snake_case     :




				def 							A__ (  self   :  List[Any]  ):
							torch.manual_seed(0  )
							lowercase__   					=			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			UNetaDConditionModel(
							    sample_size=32,   layers_per_block=1,   block_out_channels=[32, 64],   down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ],   mid_block_type="UNetMidBlock2DSimpleCrossAttn",   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],   in_channels=3,   out_channels=6,   cross_attention_dim=32,   encoder_hid_dim=32,   attention_head_dim=8,   addition_embed_type="text",   addition_embed_type_num_heads=2,   cross_attention_norm="group_norm",   resnet_time_scale_shift="scale_shift",   act_fn="gelu",   )
							unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   thresholding=__lowercase,   dynamic_thresholding_ratio=0.95,   sample_max_value=1.0,   prediction_type="epsilon",   variance_type="learned_range",   )

							torch.manual_seed(0  )
							lowercase__   					=			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




				def 							A__ (  self   :  List[str]  ):
							torch.manual_seed(0  )
							lowercase__   					=			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			UNetaDConditionModel(
							    sample_size=32,   layers_per_block=[1, 2],   block_out_channels=[32, 64],   down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ],   mid_block_type="UNetMidBlock2DSimpleCrossAttn",   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],   in_channels=6,   out_channels=6,   cross_attention_dim=32,   encoder_hid_dim=32,   attention_head_dim=8,   addition_embed_type="text",   addition_embed_type_num_heads=2,   cross_attention_norm="group_norm",   resnet_time_scale_shift="scale_shift",   act_fn="gelu",   class_embed_type="timestep",   mid_block_scale_factor=1.414,   time_embedding_act_fn="gelu",   time_embedding_dim=32,   )
							unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   thresholding=__lowercase,   dynamic_thresholding_ratio=0.95,   sample_max_value=1.0,   prediction_type="epsilon",   variance_type="learned_range",   )

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   )

							torch.manual_seed(0  )
							lowercase__   					=			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




				def 							A__ (  self   :  str  ):
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			self.pipeline_class(**__lowercase  )
							pipe.to(__lowercase  )
							pipe.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )

							lowercase__   					=			inputs["prompt"]
							lowercase__   					=			inputs["generator"]
							lowercase__   					=			inputs["num_inference_steps"]
							lowercase__   					=			inputs["output_type"]

							if "image" in inputs:
										lowercase__   					=			inputs["image"]
							else:
										lowercase__   					=			None

							if "mask_image" in inputs:
										lowercase__   					=			inputs["mask_image"]
							else:
										lowercase__   					=			None

							if "original_image" in inputs:
										lowercase__   					=			inputs["original_image"]
							else:
										lowercase__   					=			None

							lowercase__					,    lowercase__   					=			pipe.encode_prompt(__lowercase  )

							# inputs with prompt converted to embeddings
							lowercase__   					=			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										lowercase__   					=			image

							if mask_image is not None:
										lowercase__   					=			mask_image

							if original_image is not None:
										lowercase__   					=			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
										setattr(__lowercase,   __lowercase,   __lowercase  )

							lowercase__   					=			pipe(**__lowercase  )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase  )
										lowercase__   					=			self.pipeline_class.from_pretrained(__lowercase  )
										pipe_loaded.to(__lowercase  )
										pipe_loaded.set_progress_bar_config(disable=__lowercase  )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(__lowercase,   __lowercase  ) is None,   F'''`{optional_component}` did not stay set to None after loading.''',   )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )

							lowercase__   					=			inputs["generator"]
							lowercase__   					=			inputs["num_inference_steps"]
							lowercase__   					=			inputs["output_type"]

							# inputs with prompt converted to embeddings
							lowercase__   					=			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										lowercase__   					=			image

							if mask_image is not None:
										lowercase__   					=			mask_image

							if original_image is not None:
										lowercase__   					=			original_image

							lowercase__   					=			pipe_loaded(**__lowercase  )[0]

							lowercase__   					=			np.abs(to_np(__lowercase  ) - to_np(__lowercase  )  ).max()
							self.assertLess(__lowercase,   1e-4  )




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			self.pipeline_class(**__lowercase  )
							pipe.to(__lowercase  )
							pipe.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )
							lowercase__   					=			pipe(**__lowercase  )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase  )
										lowercase__   					=			self.pipeline_class.from_pretrained(__lowercase  )
										pipe_loaded.to(__lowercase  )
										pipe_loaded.set_progress_bar_config(disable=__lowercase  )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )
							lowercase__   					=			pipe_loaded(**__lowercase  )[0]

							lowercase__   					=			np.abs(to_np(__lowercase  ) - to_np(__lowercase  )  ).max()
							self.assertLess(__lowercase,   1e-4  )









import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowercase_						=       logging.get_logger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			r"\w+[.]\d+"
			lowercase__   					=			re.findall(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			for pat in pats:
						lowercase__   					=			key.replace(SCREAMING_SNAKE_CASE_  ,					"_".join(pat.split("."					)					)					)
			return key



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
			if (
			    any("norm" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						lowercase__   					=			pt_tensor.transpose(2  ,					3  ,					1  ,					0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight":
						lowercase__   					=			pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			lowercase__   					=			pt_tuple_key[:-1] + ("weight",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			lowercase__   					=			pt_tuple_key[:-1] + ("bias",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			lowercase__   					=			flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE_					)					)

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowercase__   					=			rename_key(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			tuple(renamed_pt_key.split("."					)					)

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)




