






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 						_a    (      a :Union[str, Any] )							->			List[str]:
 a		  =      os.path.join(args.tf_model_dir	,							'''parameters.json''' )
 a		  =      json.loads(open(a ).read() )
 if not params:
  raise ValueError(
      F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
 if not args.output.endswith('''.pt''' ):
  a		  =      args.output + '''.pt'''
 a		  =      OrderedDict()
 with tf.device('''/CPU:0''' ):
  a		  =      tf.train.load_checkpoint(args.tf_model_dir )
  a		  =      reader.get_variable_to_shape_map()
  for key_name in shapes.keys():
   a		  =      reader.get_tensor(a ).astype(np.floataa )
   if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
    continue
   if key_name.startswith('''pasts/''' ):
    if key_name.startswith('''pasts/mlp''' ):
     a		  =      int(key_name[9] )
    elif key_name.startswith('''pasts/out''' ):
     a		  =      8
    a		  =      '''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
    a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
    a		  =      torch.tensor(a )
   elif key_name.startswith('''model/moe''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/switch_gating/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/softmlp/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
     a		  =      key_name[-9:-7]
     for i in range(16 ):
      a		  =      '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
      a		  =      (
          vnp[i].transpose([1, 0] ).copy()
      )  # In Mesh-Tensorflow, it is one array, so it is divided
      a		  =      torch.tensor(a )
   elif key_name.startswith('''model/mlp''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/p1/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p1/bias''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p2/kernel''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
     a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/p2/bias''' ):
     a		  =      '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/ln''' ):
    a		  =      int(key_name[8:].split('''/''' )[0] )
    if key_name.endswith('''/b''' ):
     a		  =      '''model.blocks.%d.feed_forward.norm.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/g''' ):
     a		  =      '''model.blocks.%d.feed_forward.norm.weight''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/att''' ):
    a		  =      int(key_name[9:].split('''/''' )[0] )
    if key_name.endswith('''/qkv/kernel''' ):
     a		  =      vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
     a		  =      state[:, 0, :, :]
     a		  =      state[:, 1, :, :]
     a		  =      state[:, 2, :, :]
     a		  =      (
         state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      (
         state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      (
         state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
         .transpose([1, 0] )
         .copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
     a		  =      torch.tensor(a )
     a		  =      '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
     a		  =      torch.tensor(a )
     a		  =      '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/o/kernel''' ):
     a		  =      '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
     a		  =      (
         vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
     )  # Mesh-Tensorflow is a diagonal matrix
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/an''' ):
    a		  =      int(key_name[8:].split('''/''' )[0] )
    if key_name.endswith('''/b''' ):
     a		  =      '''model.blocks.%d.self_attn.norm.bias''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
    elif key_name.endswith('''/g''' ):
     a		  =      '''model.blocks.%d.self_attn.norm.weight''' % player
     a		  =      vnp.copy()  # same because it is one dimensional
     a		  =      torch.tensor(a )
   elif (
       key_name.startswith('''model/wte''' )
       or key_name.startswith('''model/wpe''' )
       or key_name.startswith('''model/ete''' )
   ):
    a		  =      {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
        key_name[-3:]
    ]
    a		  =      '''model.%s.weight''' % nlayer
    a		  =      vnp.copy()  # same in embedded
    a		  =      torch.tensor(a )
    if key_name.startswith('''model/wte''' ):
     a		  =      '''lm_head.weight'''
     a		  =      vnp.copy()  # same in embedded
     a		  =      torch.tensor(a )
   elif key_name.startswith('''model/wob''' ):
    a		  =      '''final_logits_bias'''
    a		  =      vnp.copy()  # same in embedded
    a		  =      state.reshape((1, -1) )
    a		  =      torch.tensor(a )
   elif key_name == "model/dense/kernel":
    a		  =      '''model.last_project.weight'''
    a		  =      vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
    a		  =      torch.tensor(a )
   elif key_name == "model/dense_1/bias":
    a		  =      '''model.last_project.bias'''
    a		  =      vnp.copy()  # same because it is one dimensional
    a		  =      torch.tensor(a )
 torch.save(a	,							args.output )


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser(
         description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
     parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
     parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
     UpperCAmelCase__				   =		parser.parse_args()
     convert_tf_gptsan_to_pt(args)





'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDConditionModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=9					,							out_channels=4					,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,							cross_attention_dim=32					,							attention_head_dim=(2, 4)					,							use_linear_projection=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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					,							sample_size=128					,							)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      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					,							hidden_act="""gelu"""					,							projection_dim=512					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": init_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 5E-1


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






