



'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 	A_			(    UpperCAmelCase_	,       UpperCAmelCase_	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  StableDiffusionSAGPipeline
     UpperCAmelCase_						:     Tuple  		=  TEXT_TO_IMAGE_PARAMS
     UpperCAmelCase_						:     Tuple  		=  TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCAmelCase_						:     List[Any]  		=  TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase_						:     Optional[Any]  		=  TEXT_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase_						:     List[str]  		=  False


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Dict:
      torch.manual_seed(0	)
      UpperCAmelCase	:     Tuple						 =			UNetaDConditionModel(
          block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=4					,      out_channels=4					,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,      cross_attention_dim=32					,      )
      UpperCAmelCase	:     List[str]						 =			DDIMScheduler(
          beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule='scaled_linear'					,      clip_sample=__lowercase					,      set_alpha_to_one=__lowercase					,      )
      torch.manual_seed(0	)
      UpperCAmelCase	:     Dict						 =			AutoencoderKL(
          block_out_channels=[32, 64]					,      in_channels=3					,      out_channels=3					,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,      latent_channels=4					,      )
      torch.manual_seed(0	)
      UpperCAmelCase	:     Any						 =			CLIPTextConfig(
          bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1E-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1_000					,      )
      UpperCAmelCase	:     Tuple						 =			CLIPTextModel(__lowercase	)
      UpperCAmelCase	:     Optional[Any]						 =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

      UpperCAmelCase	:     int						 =			{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]=0	)						->     Union[str, Any]:
      if str(__lowercase	).startswith('mps'	):
       UpperCAmelCase	:     Union[str, Any]						 =			torch.manual_seed(__lowercase	)
      else:
       UpperCAmelCase	:     Optional[Any]						 =			torch.Generator(device=__lowercase	).manual_seed(__lowercase	)
      UpperCAmelCase	:     Any						 =			{
          '''prompt''': '''.''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 1.0,
          '''sag_scale''': 1.0,
          '''output_type''': '''numpy''',
      }
      return inputs




     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[int]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3	)




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






     '''simple docstring'''



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


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Tuple:
      UpperCAmelCase	:     str						 =			StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'	)
      UpperCAmelCase	:     Tuple						 =			sag_pipe.to(__lowercase	)
      sag_pipe.set_progress_bar_config(disable=__lowercase	)

      UpperCAmelCase	:     Optional[Any]						 =			'''.'''
      UpperCAmelCase	:     Union[str, Any]						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     List[str]						 =			sag_pipe(
          [prompt]					,      generator=__lowercase					,      guidance_scale=7.5					,      sag_scale=1.0					,      num_inference_steps=20					,      output_type='np'	)

      UpperCAmelCase	:     List[Any]						 =			output.images

      UpperCAmelCase	:     Optional[int]						 =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      UpperCAmelCase	:     Any						 =			np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]	)

      assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      UpperCAmelCase	:     Optional[int]						 =			StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'	)
      UpperCAmelCase	:     List[str]						 =			sag_pipe.to(__lowercase	)
      sag_pipe.set_progress_bar_config(disable=__lowercase	)

      UpperCAmelCase	:     Dict						 =			'''.'''
      UpperCAmelCase	:     List[Any]						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     Union[str, Any]						 =			sag_pipe(
          [prompt]					,      generator=__lowercase					,      guidance_scale=7.5					,      sag_scale=1.0					,      num_inference_steps=20					,      output_type='np'	)

      UpperCAmelCase	:     List[str]						 =			output.images

      UpperCAmelCase	:     Union[str, Any]						 =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      UpperCAmelCase	:     Optional[int]						 =			np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]	)

      assert np.abs(image_slice.flatten() - expected_slice	).max() < 5E-2




     def        UpperCAmelCase_	(		self			:					Any	)						->     Dict:
      UpperCAmelCase	:     Dict						 =			StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'	)
      UpperCAmelCase	:     Optional[Any]						 =			sag_pipe.to(__lowercase	)
      sag_pipe.set_progress_bar_config(disable=__lowercase	)

      UpperCAmelCase	:     Union[str, Any]						 =			'''.'''
      UpperCAmelCase	:     int						 =			torch.manual_seed(0	)
      UpperCAmelCase	:     Any						 =			sag_pipe(
          [prompt]					,      width=768					,      height=512					,      generator=__lowercase					,      guidance_scale=7.5					,      sag_scale=1.0					,      num_inference_steps=20					,      output_type='np'					,      )

      UpperCAmelCase	:     Optional[int]						 =			output.images

      assert image.shape == (1, 512, 768, 3)







'''simple docstring'''

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
lowercase__         =      {
    "/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 				UpperCamelCase(					UpperCAmelCase_     ):
 # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
 # the original model
 UpperCAmelCase	:     List[Any]						 =			list(s_dict.keys()     )
 for key in keys:
  UpperCAmelCase	:     Union[str, Any]						 =			R'.*/layers_(\d+)'
  UpperCAmelCase	:     List[str]						 =			key
  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'layers_(\d+)'  ,	R'block/\1/layer'  ,	UpperCAmelCase_     )

  UpperCAmelCase	:     str						 =			R'(encoder|decoder)\/'

  if re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Tuple						 =			re.match(UpperCAmelCase_  ,	UpperCAmelCase_     ).groups()
   if groups[0] == "encoder":
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/mlp/'  ,	R'/1/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     List[Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/1/layer_norm/'  ,	UpperCAmelCase_     )

   elif groups[0] == "decoder":
    UpperCAmelCase	:     Tuple						 =			re.sub(R'/mlp/'  ,	R'/2/mlp/'  ,	UpperCAmelCase_     )
    UpperCAmelCase	:     Union[str, Any]						 =			re.sub(R'/pre_mlp_layer_norm/'  ,	R'/2/layer_norm/'  ,	UpperCAmelCase_     )

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

  print(F"""{key} -> {new_key}"""     )
  UpperCAmelCase	:     List[Any]						 =			s_dict.pop(UpperCAmelCase_     )

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCAmelCase	:     Optional[int]						 =			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:
  UpperCAmelCase	:     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:
   UpperCAmelCase	:     List[str]						 =			s_dict[key].shape[0]
   UpperCAmelCase	:     List[Any]						 =			s_dict[key]
   for idx in range(UpperCAmelCase_     ):
    UpperCAmelCase	:     Union[str, Any]						 =			expert_weihts[idx]
    print(F"""{key} -> {key.replace("expert/"  ,	"nested fstring"     )}"""     )

   s_dict.pop(UpperCAmelCase_     )

 return s_dict


lowercase__         =      {
    "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 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(UpperCAmelCase_  ,	'r'     ) as f:
  UpperCAmelCase	:     Union[str, Any]						 =			f.read()

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

 UpperCAmelCase	:     str						 =			re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,	UpperCAmelCase_     )[0]
 UpperCAmelCase	:     Union[str, Any]						 =			str(activation[1]     )

 UpperCAmelCase	:     Optional[int]						 =			num_experts
 UpperCAmelCase	:     List[str]						 =			SwitchTransformersConfig(**UpperCAmelCase_     )
 return config




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_="./"  ,	UpperCAmelCase_=8     ):
 # Initialise PyTorch model

 print(F"""Loading flax weights from : {flax_checkpoint_path}"""     )
 UpperCAmelCase	:     List[Any]						 =			checkpoints.load_tax_checkpoint(UpperCAmelCase_     )

 if gin_file is not None:
  UpperCAmelCase	:     List[Any]						 =			convert_gin_to_config(UpperCAmelCase_  ,	UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     str						 =			SwitchTransformersConfig.from_pretrained(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			SwitchTransformersForConditionalGeneration(UpperCAmelCase_     )

 UpperCAmelCase	:     str						 =			flax_params['target']
 UpperCAmelCase	:     Union[str, Any]						 =			flatten_dict(UpperCAmelCase_  ,	sep='/'     )
 UpperCAmelCase	:     Tuple						 =			rename_keys(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[int]						 =			unflatten_dict(UpperCAmelCase_  ,	sep='/'     )

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

 print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
 pt_model.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      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")
 lowercase__         =      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,
 )




