





'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						os.path.join(args.tf_model_dir	,		'parameters.json'	)
  lowercase__			    =						json.loads(open(_SCREAMING_SNAKE_CASE	).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'	):
    lowercase__			    =						args.output + '.pt'
  lowercase__			    =						OrderedDict()
  with tf.device('/CPU:0'	):
    lowercase__			    =						tf.train.load_checkpoint(args.tf_model_dir	)
    lowercase__			    =						reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      lowercase__			    =						reader.get_tensor(_SCREAMING_SNAKE_CASE	).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'	):
          lowercase__			    =						int(key_name[9]	)
        elif key_name.startswith('pasts/out'	):
          lowercase__			    =						8
        lowercase__			    =						'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/moe'	):
        lowercase__			    =						int(key_name[9:].split('/'	)[0]	)
        if key_name.endswith('/switch_gating/kernel'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
          lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/softmlp/kernel'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
          lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/wo/kernel'	) or key_name.endswith('/wi/kernel'	):
          lowercase__			    =						key_name[-9:-7]
          for i in range(16	):
            lowercase__			    =						'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
            lowercase__			    =						(
                vnp[i].transpose([1, 0]	).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/mlp'	):
        lowercase__			    =						int(key_name[9:].split('/'	)[0]	)
        if key_name.endswith('/p1/kernel'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.mlp.wi.weight' % player
          lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/p1/bias'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.mlp.wi.bias' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/p2/kernel'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.mlp.wo.weight' % player
          lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/p2/bias'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.mlp.wo.bias' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/ln'	):
        lowercase__			    =						int(key_name[8:].split('/'	)[0]	)
        if key_name.endswith('/b'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.norm.bias' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/g'	):
          lowercase__			    =						'model.blocks.%d.feed_forward.norm.weight' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/att'	):
        lowercase__			    =						int(key_name[9:].split('/'	)[0]	)
        if key_name.endswith('/qkv/kernel'	):
          lowercase__			    =						vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          lowercase__			    =						state[:, 0, :, :]
          lowercase__			    =						state[:, 1, :, :]
          lowercase__			    =						state[:, 2, :, :]
          lowercase__			    =						(
              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
          lowercase__			    =						(
              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
          lowercase__			    =						(
              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
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/o/kernel'	):
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
          lowercase__			    =						(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/an'	):
        lowercase__			    =						int(key_name[8:].split('/'	)[0]	)
        if key_name.endswith('/b'	):
          lowercase__			    =						'model.blocks.%d.self_attn.norm.bias' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/g'	):
          lowercase__			    =						'model.blocks.%d.self_attn.norm.weight' % player
          lowercase__			    =						vnp.copy()  # same because it is one dimensional
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif (
          key_name.startswith('model/wte'	)
          or key_name.startswith('model/wpe'	)
          or key_name.startswith('model/ete'	)
      ):
        lowercase__			    =						{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
            key_name[-3:]
        ]
        lowercase__			    =						'model.%s.weight' % nlayer
        lowercase__			    =						vnp.copy()  # same in embedded
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        if key_name.startswith('model/wte'	):
          lowercase__			    =						'lm_head.weight'
          lowercase__			    =						vnp.copy()  # same in embedded
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/wob'	):
        lowercase__			    =						'final_logits_bias'
        lowercase__			    =						vnp.copy()  # same in embedded
        lowercase__			    =						state.reshape((1, -1)	)
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name == "model/dense/kernel":
        lowercase__			    =						'model.last_project.weight'
        lowercase__			    =						vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name == "model/dense_1/bias":
        lowercase__			    =						'model.last_project.bias'
        lowercase__			    =						vnp.copy()  # same because it is one dimensional
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
  torch.save(_SCREAMING_SNAKE_CASE	,		args.output	)


if __name__ == "__main__":
      lowercase_					     =	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""")
      lowercase_					     =	parser.parse_args()
      convert_tf_gptsan_to_pt(args)









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase_					     =	False
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):
   pass






@nightly
@require_torch_gpu
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )

     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(a )
       lowercase__			    =						VersatileDiffusionPipeline.from_pretrained(a    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						generator.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt='first prompt'    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=2    ,  output_type='numpy'    ,  ).images

     assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     lowercase__			    =						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,  torch_dtype=torch.floataa )
     pipe.to(a )
     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						'cyberpunk 2077'
     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.dual_guided(
         prompt=a    ,  image=a    ,  text_to_image_strength=0.75    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy'    ,  ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )

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

     lowercase__			    =						'A painting of a squirrel eating a burger '
     lowercase__			    =						torch.manual_seed(0 )
     lowercase__			    =						pipe.text_to_image(
         prompt=a    ,  generator=a    ,  guidance_scale=7.5    ,  num_inference_steps=50    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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

     lowercase__			    =						pipe.image_variation(a    ,  generator=a    ,  output_type='numpy' ).images

     lowercase__			    =						image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     lowercase__			    =						np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )

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


