
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class        A_   (			__lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     int							 =       IFInpaintingSuperResolutionPipeline
	_UpperCamelCase     :     str							 =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
	_UpperCamelCase     :     int							 =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}   )
	_UpperCamelCase     :     str							 =       PipelineTesterMixin.required_optional_params - {"""latents"""}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return self._get_superresolution_dummy_components()





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=0       ):
		if str(snake_case       ).startswith('mps'       ):
			lowercase       =    torch.manual_seed(snake_case       )
		else:
			lowercase       =    torch.Generator(device=snake_case       ).manual_seed(snake_case       )

		lowercase       =    floats_tensor((1, 3, 16, 16)		,    rng=random.Random(snake_case       )       ).to(snake_case       )
		lowercase       =    floats_tensor((1, 3, 32, 32)		,    rng=random.Random(snake_case       )       ).to(snake_case       )
		lowercase       =    floats_tensor((1, 3, 32, 32)		,    rng=random.Random(snake_case       )       ).to(snake_case       )

		lowercase       =    {
		    'prompt': 'A painting of a squirrel eating a burger',
		    'image': image,
		    'original_image': original_image,
		    'mask_image': mask_image,
		    'generator': generator,
		    'num_inference_steps': 2,
		    'output_type': 'numpy',
		}

		return inputs





	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()		,    reason='XFormers attention is only available with CUDA and `xformers` installed'		,    )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_save_load_optional_components()





	@unittest.skipIf(torch_device != 'cuda'		,    reason='float16 requires CUDA'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
		super().test_save_load_floataa(expected_max_diff=1E-1       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_save_load_local()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_inference_batch_single_identical(
		    expected_max_diff=1E-2		,    )



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							)

