

import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  __lowerCAmelCase     (					__lowerCamelCase			,			unittest.TestCase							):
  _a									=  DiTPipeline
  _a									=  CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  _a									=  PipelineTesterMixin.required_optional_params - {
      """latents""",
      """num_images_per_prompt""",
      """callback""",
      """callback_steps""",
  }
  _a									=  CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  _a									=  False




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        torch.manual_seed(0					)
        _lowercase	       =TransformeraDModel(
            sample_size=16							,   num_layers=2							,   patch_size=4							,   attention_head_dim=8							,   num_attention_heads=2							,   in_channels=4							,   out_channels=8							,   attention_bias=lowerCAmelCase							,   activation_fn='gelu-approximate'							,   num_embeds_ada_norm=1_000							,   norm_type='ada_norm_zero'							,   norm_elementwise_affine=lowerCAmelCase							,   )
        _lowercase	       =AutoencoderKL()
        _lowercase	       =DDIMScheduler()
        _lowercase	       ={'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
        return components




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=0					)  -> Any:
        '''simple docstring'''






        if str(lowerCAmelCase					).startswith('mps'					):
              _lowercase	       =torch.manual_seed(lowerCAmelCase					)
        else:
              _lowercase	       =torch.Generator(device=lowerCAmelCase					).manual_seed(lowerCAmelCase					)
        _lowercase	       ={
            'class_labels': [1],
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs




  def   A__     (				self					)  -> str:
        '''simple docstring'''






        _lowercase	       ='cpu'

        _lowercase	       =self.get_dummy_components()
        _lowercase	       =self.pipeline_class(**lowerCAmelCase					)
        pipe.to(lowerCAmelCase					)
        pipe.set_progress_bar_config(disable=lowerCAmelCase					)

        _lowercase	       =self.get_dummy_inputs(lowerCAmelCase					)
        _lowercase	       =pipe(**lowerCAmelCase					).images
        _lowercase	       =image[0, -3:, -3:, -1]

        self.assertEqual(image.shape							,   (1, 16, 16, 3)					)
        _lowercase	       =np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]					)
        _lowercase	       =np.abs(image_slice.flatten() - expected_slice					).max()
        self.assertLessEqual(lowerCAmelCase							,   1e-3					)




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        self._test_inference_batch_single_identical(relax_max_difference=lowerCAmelCase							,   expected_max_diff=1e-3					)




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()							,   reason='XFormers attention is only available with CUDA and `xformers` installed'							,   )
  def   A__     (				self					)  -> Union[str, Any]:
        '''simple docstring'''






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)





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




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






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




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =torch.manual_seed(0					)

        _lowercase	       =DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'					)
        pipe.to('cuda'					)

        _lowercase	       =['vase', 'umbrella', 'white shark', 'white wolf']
        _lowercase	       =pipe.get_label_ids(lowerCAmelCase					)

        _lowercase	       =pipe(lowerCAmelCase							,   generator=lowerCAmelCase							,   num_inference_steps=40							,   output_type='np'					).images

        for word, image in zip(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =load_numpy(
                  F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''					)
              assert np.abs((expected_image - image).max()					) < 1e-2




  def   A__     (				self					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'					)
        _lowercase	       =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
        pipe.to('cuda'					)

        _lowercase	       =['vase', 'umbrella']
        _lowercase	       =pipe.get_label_ids(lowerCAmelCase					)

        _lowercase	       =torch.manual_seed(0					)
        _lowercase	       =pipe(lowerCAmelCase							,   generator=lowerCAmelCase							,   num_inference_steps=25							,   output_type='np'					).images

        for word, image in zip(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =load_numpy(
                  'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                  F'''/dit/{word}_512.npy'''					)

              assert np.abs((expected_image - image).max()					) < 1e-1




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


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__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 							UpperCAmelCase_		(							__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 							UpperCAmelCase_		(							__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							)

