

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Tuple					=					os.path.join(args.tf_model_dir   ,	'''parameters.json'''						)
      __lowerCAmelCase					:						Tuple					=					json.loads(open(lowercase__						).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'''						):
            __lowerCAmelCase					:						Tuple					=					args.output + '''.pt'''
      __lowerCAmelCase					:						int					=					OrderedDict()
      with tf.device('''/CPU:0'''						):
            __lowerCAmelCase					:						Optional[int]					=					tf.train.load_checkpoint(args.tf_model_dir						)
            __lowerCAmelCase					:						List[str]					=					reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  __lowerCAmelCase					:						str					=					reader.get_tensor(lowercase__						).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'''						):
                              __lowerCAmelCase					:						List[Any]					=					int(key_name[9]						)
                        elif key_name.startswith('''pasts/out'''						):
                              __lowerCAmelCase					:						List[Any]					=					8
                        __lowerCAmelCase					:						List[str]					=					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        __lowerCAmelCase					:						Any					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                        __lowerCAmelCase					:						Union[str, Any]					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/moe'''						):
                        __lowerCAmelCase					:						Union[str, Any]					=					int(key_name[9:].split('''/'''						)[0]						)
                        if key_name.endswith('''/switch_gating/kernel'''						):
                              __lowerCAmelCase					:						Optional[Any]					=					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                              __lowerCAmelCase					:						Dict					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              __lowerCAmelCase					:						int					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/softmlp/kernel'''						):
                              __lowerCAmelCase					:						Union[str, Any]					=					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                              __lowerCAmelCase					:						int					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              __lowerCAmelCase					:						Any					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/wo/kernel'''						) or key_name.endswith('''/wi/kernel'''						):
                              __lowerCAmelCase					:						Union[str, Any]					=					key_name[-9:-7]
                              for i in range(1_6						):
                                    __lowerCAmelCase					:						Dict					=					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                    __lowerCAmelCase					:						List[Any]					=					(
                                        vnp[i].transpose([1, 0]						).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    __lowerCAmelCase					:						Optional[Any]					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/mlp'''						):
                        __lowerCAmelCase					:						List[Any]					=					int(key_name[9:].split('''/'''						)[0]						)
                        if key_name.endswith('''/p1/kernel'''						):
                              __lowerCAmelCase					:						Tuple					=					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                              __lowerCAmelCase					:						List[Any]					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              __lowerCAmelCase					:						Union[str, Any]					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/p1/bias'''						):
                              __lowerCAmelCase					:						Union[str, Any]					=					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                              __lowerCAmelCase					:						Dict					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						List[str]					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/p2/kernel'''						):
                              __lowerCAmelCase					:						str					=					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                              __lowerCAmelCase					:						Optional[Any]					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                              __lowerCAmelCase					:						Tuple					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/p2/bias'''						):
                              __lowerCAmelCase					:						int					=					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                              __lowerCAmelCase					:						Union[str, Any]					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						str					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/ln'''						):
                        __lowerCAmelCase					:						Union[str, Any]					=					int(key_name[8:].split('''/'''						)[0]						)
                        if key_name.endswith('''/b'''						):
                              __lowerCAmelCase					:						List[Any]					=					'''model.blocks.%d.feed_forward.norm.bias''' % player
                              __lowerCAmelCase					:						Optional[Any]					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						List[str]					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/g'''						):
                              __lowerCAmelCase					:						Union[str, Any]					=					'''model.blocks.%d.feed_forward.norm.weight''' % player
                              __lowerCAmelCase					:						Union[str, Any]					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						Dict					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/att'''						):
                        __lowerCAmelCase					:						List[Any]					=					int(key_name[9:].split('''/'''						)[0]						)
                        if key_name.endswith('''/qkv/kernel'''						):
                              __lowerCAmelCase					:						Optional[int]					=					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              __lowerCAmelCase					:						Tuple					=					state[:, 0, :, :]
                              __lowerCAmelCase					:						Optional[int]					=					state[:, 1, :, :]
                              __lowerCAmelCase					:						List[str]					=					state[:, 2, :, :]
                              __lowerCAmelCase					:						Optional[int]					=					(
                                  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
                              __lowerCAmelCase					:						Dict					=					(
                                  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
                              __lowerCAmelCase					:						Optional[int]					=					(
                                  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
                              __lowerCAmelCase					:						Union[str, Any]					=					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                              __lowerCAmelCase					:						List[str]					=					torch.tensor(lowercase__						)
                              __lowerCAmelCase					:						Dict					=					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                              __lowerCAmelCase					:						List[Any]					=					torch.tensor(lowercase__						)
                              __lowerCAmelCase					:						Any					=					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                              __lowerCAmelCase					:						str					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/o/kernel'''						):
                              __lowerCAmelCase					:						List[str]					=					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                              __lowerCAmelCase					:						Optional[int]					=					(
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]						).transpose([1, 0]						).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              __lowerCAmelCase					:						Optional[int]					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/an'''						):
                        __lowerCAmelCase					:						List[str]					=					int(key_name[8:].split('''/'''						)[0]						)
                        if key_name.endswith('''/b'''						):
                              __lowerCAmelCase					:						Dict					=					'''model.blocks.%d.self_attn.norm.bias''' % player
                              __lowerCAmelCase					:						str					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						str					=					torch.tensor(lowercase__						)
                        elif key_name.endswith('''/g'''						):
                              __lowerCAmelCase					:						Optional[int]					=					'''model.blocks.%d.self_attn.norm.weight''' % player
                              __lowerCAmelCase					:						int					=					vnp.copy()  # same because it is one dimensional
                              __lowerCAmelCase					:						List[str]					=					torch.tensor(lowercase__						)
                  elif (
                      key_name.startswith('''model/wte'''						)
                      or key_name.startswith('''model/wpe'''						)
                      or key_name.startswith('''model/ete'''						)
                  ):
                        __lowerCAmelCase					:						int					=					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                            key_name[-3:]
                        ]
                        __lowerCAmelCase					:						Dict					=					'''model.%s.weight''' % nlayer
                        __lowerCAmelCase					:						List[str]					=					vnp.copy()  # same in embedded
                        __lowerCAmelCase					:						Optional[int]					=					torch.tensor(lowercase__						)
                        if key_name.startswith('''model/wte'''						):
                              __lowerCAmelCase					:						Union[str, Any]					=					'''lm_head.weight'''
                              __lowerCAmelCase					:						Dict					=					vnp.copy()  # same in embedded
                              __lowerCAmelCase					:						List[str]					=					torch.tensor(lowercase__						)
                  elif key_name.startswith('''model/wob'''						):
                        __lowerCAmelCase					:						Dict					=					'''final_logits_bias'''
                        __lowerCAmelCase					:						Tuple					=					vnp.copy()  # same in embedded
                        __lowerCAmelCase					:						str					=					state.reshape((1, -1)						)
                        __lowerCAmelCase					:						Tuple					=					torch.tensor(lowercase__						)
                  elif key_name == "model/dense/kernel":
                        __lowerCAmelCase					:						Optional[Any]					=					'''model.last_project.weight'''
                        __lowerCAmelCase					:						Any					=					vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                        __lowerCAmelCase					:						Optional[int]					=					torch.tensor(lowercase__						)
                  elif key_name == "model/dense_1/bias":
                        __lowerCAmelCase					:						Dict					=					'''model.last_project.bias'''
                        __lowerCAmelCase					:						int					=					vnp.copy()  # same because it is one dimensional
                        __lowerCAmelCase					:						Optional[int]					=					torch.tensor(lowercase__						)
      torch.save(lowercase__   ,	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)





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class   __lowercase      (unittest.TestCase ):
 @property
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					UNetaDModel(
           block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')      ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')      ,							)
       return model





 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.dummy_uncond_unet
       __lowerCAmelCase					:						Any					=					PNDMScheduler()

       __lowerCAmelCase					:						Dict					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''					).images

       __lowerCAmelCase					:						Optional[Any]					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						List[Any]					=					pndm(generator=A_      ,							num_inference_steps=20      ,							output_type='''numpy'''      ,							return_dict=A_					)[0]

       __lowerCAmelCase					:						Tuple					=					image[0, -3:, -3:, -1]
       __lowerCAmelCase					:						Optional[Any]					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						int					=					np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

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







@slow
@require_torch
class   __lowercase      (unittest.TestCase ):
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					'''google/ddpm-cifar10-32'''

       __lowerCAmelCase					:						Union[str, Any]					=					UNetaDModel.from_pretrained(A_					)
       __lowerCAmelCase					:						int					=					PNDMScheduler()

       __lowerCAmelCase					:						Any					=					PNDMPipeline(unet=A_      ,							scheduler=A_					)
       pndm.to(A_					)
       pndm.set_progress_bar_config(disable=A_					)
       __lowerCAmelCase					:						Tuple					=					torch.manual_seed(0					)
       __lowerCAmelCase					:						Any					=					pndm(generator=A_      ,							output_type='''numpy'''					).images

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

       assert image.shape == (1, 32, 32, 3)
       __lowerCAmelCase					:						List[Any]					=					np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]					)

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


