import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class 		a__						(					A__    ,		A__    ,		A__    ,		unittest.TestCase					):
					A	       =							StableUnCLIPImgaImgPipeline
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					A	       =							frozenset(
					    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A	       =							frozenset([]					)

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    32
							SCREAMING_SNAKE_CASE_  :		Dict				      =    embedder_hidden_size

							# image encoding components

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    CLIPImageProcessor(crop_size=32,size=32    )

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=_A,projection_dim=_A,num_hidden_layers=5,num_attention_heads=4,image_size=32,intermediate_size=37,patch_size=1,)    )

							# regular denoising components

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    StableUnCLIPImageNormalizer(embedding_dim=_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    DDPMScheduler(beta_schedule="squaredcos_cap_v2"    )

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

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0,eos_token_id=2,hidden_size=_A,projection_dim=32,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,)    )

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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=_A,layers_per_block=1,upcast_attention=_A,use_linear_projection=_A,)

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    DDIMScheduler(
							    beta_schedule="scaled_linear",beta_start=0.00085,beta_end=0.012,prediction_type="v_prediction",set_alpha_to_one=_A,steps_offset=1,)

							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    AutoencoderKL()

							SCREAMING_SNAKE_CASE_  :		str				      =    {
							    # image encoding components
							    "feature_extractor": feature_extractor,
							    "image_encoder": image_encoder.eval(),
							    # image noising components
							    "image_normalizer": image_normalizer.eval(),
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder.eval(),
							    "unet": unet.eval(),
							    "scheduler": scheduler,
							    "vae": vae.eval(),
							}

							return components

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       Optional[int]=0,_A      :       Union[str, Any]=True    ):







							"""simple docstring"""






							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		int				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		int				      =    torch.Generator(device=_A    ).manual_seed(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    floats_tensor((1, 3, 32, 32),rng=random.Random(_A    )    ).to(_A    )

							if pil_image:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    input_image * 0.5 + 0.5
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    input_image.clamp(0,1    )
									SCREAMING_SNAKE_CASE_  :		int				      =    input_image.cpu().permute(0,2,3,1    ).float().numpy()
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    DiffusionPipeline.numpy_to_pil(_A    )[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}

					@skip_mps
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "cpu"  # ensure determinism for the device-dependent torch.Generator
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    StableUnCLIPImgaImgPipeline(**_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    sd_pipe.to(_A    )
							sd_pipe.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_dummy_inputs(_A    )
							inputs.update({"image_embeds": None}    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    sd_pipe(**_A    ).images
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-3

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch_device in ["cpu", "mps"]

							self._test_attention_slicing_forward_pass(test_max_difference=_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_A    )






					@unittest.skipIf(
					    torch_device != "cuda" or not is_xformers_available(),reason="XFormers attention is only available with CUDA and `xformers` installed",)
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=_A    )







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

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    StableUnCLIPImgaImgPipeline.from_pretrained(
							    "fusing/stable-unclip-2-1-l-img2img",torch_dtype=torch.floataa    )
							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )
							# 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()

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.Generator(device="cpu"    ).manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(_A,"anime turle",generator=_A,output_type="np"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    output.images[0]

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

							assert_mean_pixel_difference(_A,_A    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    StableUnCLIPImgaImgPipeline.from_pretrained(
							    "fusing/stable-unclip-2-1-h-img2img",torch_dtype=torch.floataa    )
							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )
							# 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()

							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.Generator(device="cpu"    ).manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(_A,"anime turle",generator=_A,output_type="np"    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    output.images[0]

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

							assert_mean_pixel_difference(_A,_A    )






					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"    )

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							SCREAMING_SNAKE_CASE_  :		int				      =    StableUnCLIPImgaImgPipeline.from_pretrained(
							    "fusing/stable-unclip-2-1-h-img2img",torch_dtype=torch.floataa    )
							SCREAMING_SNAKE_CASE_  :		int				      =    pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(
							    _A,"anime turtle",num_inference_steps=2,output_type="np",)

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCamelCase					:	Any       =  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    list(s_dict.keys()	)
		for key in keys:
				SCREAMING_SNAKE_CASE_  :		int				      =    R".*/layers_(\d+)"
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    key
				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"layers_(\d+)"   , R"block/\1/layer"   , lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    R"(encoder|decoder)\/"

				if re.match(lowerCAmelCase   , lowerCAmelCase	):
						SCREAMING_SNAKE_CASE_  :		str				      =    re.match(lowerCAmelCase   , lowerCAmelCase	).groups()
						if groups[0] == "encoder":
								SCREAMING_SNAKE_CASE_  :		Any				      =    re.sub(R"/mlp/"   , R"/1/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/1/layer_norm/"   , lowerCAmelCase	)

						elif groups[0] == "decoder":
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.sub(R"/mlp/"   , R"/2/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    re.sub(R"/pre_mlp_layer_norm/"   , R"/2/layer_norm/"   , lowerCAmelCase	)

        # 2. Convert other classic mappings
				for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
						if old_key in new_key:
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    new_key.replace(lowerCAmelCase   , lowerCAmelCase	)

				print(f'{key} -> {new_key}'	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict.pop(lowerCAmelCase	)

		if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_SNAKE_CASE_  :		str				      =    s_dict[
				    "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
				].T
		if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    s_dict[
				    "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
				].T

		# 3. Take extra care of the EXPERTS layer
		for key in list(s_dict.keys()	):
				if "expert" in key:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    s_dict[key].shape[0]
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict[key]
						for idx in range(lowerCAmelCase	):
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    expert_weihts[idx]
								print(f'{key} -> {key.replace("expert/"   , "nested fstring"	)}'	)

						s_dict.pop(lowerCAmelCase	)

		return s_dict


__lowerCamelCase					:	List[Any]       =  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}




def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		import regex as re

		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.read()

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.findall(R"(.*) = ([0-9.]*)"   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    {}
		for param, value in regex_match:
				if param in GIN_TO_CONFIG_MAPPING and value != "":
						SCREAMING_SNAKE_CASE_  :		int				      =    float(lowerCAmelCase	) if "." in value else int(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    re.findall(R"(.*activations) = \(\'(.*)\',\)"   , lowerCAmelCase	)[0]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    str(activation[1]	)

		SCREAMING_SNAKE_CASE_  :		str				      =    num_experts
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SwitchTransformersConfig(**lowerCAmelCase	)
		return config




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Optional[Any]="./"   , lowerCAmelCase							:   Dict=8	):
		"""simple docstring"""







		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		SCREAMING_SNAKE_CASE_  :		int				      =    checkpoints.load_tax_checkpoint(lowerCAmelCase	)

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    convert_gin_to_config(lowerCAmelCase   , lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		str				      =    SwitchTransformersForConditionalGeneration(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    flax_params["target"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    flatten_dict(lowerCAmelCase   , sep="/"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    rename_keys(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    unflatten_dict(lowerCAmelCase   , sep="/"	)

		# Load the flax params in the PT model
		load_flax_weights_in_pytorch_model(lowerCAmelCase   , lowerCAmelCase	)

		print(f'Save PyTorch model to {pytorch_dump_path}'	)
		pt_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[str]       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--switch_t5x_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
		        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
		    ),
		)
		parser.add_argument(
		    '''--gin_file''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
		)
		parser.add_argument(
		    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
		)
		parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
		__lowerCamelCase					:	Any       =  parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)
