
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def 							UpperCAmelCase_(  a__					):



    """simple docstring"""



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


if __name__ == "__main__":
      a__							:	List[Any]  						=			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''')
      a__							:	str  						=			parser.parse_args()
      convert_tf_gptsan_to_pt(args)

'''simple docstring'''




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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase   					=  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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_uncond_unet
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    num_inference_steps=20       ,    output_type="""numpy"""       ,    return_dict=__lowerCAmelCase)[0]

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]
					lowerCAmelCase   					=  image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """google/ddpm-cifar10-32"""

					lowerCAmelCase   					=  UNetaDModel.from_pretrained(__lowerCAmelCase)
					lowerCAmelCase   					=  PNDMScheduler()

					lowerCAmelCase   					=  PNDMPipeline(unet=__lowerCAmelCase       ,    scheduler=__lowerCAmelCase)
					pndm.to(__lowerCAmelCase)
					pndm.set_progress_bar_config(disable=__lowerCAmelCase)
					lowerCAmelCase   					=  torch.manual_seed(0)
					lowerCAmelCase   					=  pndm(generator=__lowerCAmelCase       ,    output_type="""numpy""").images

					lowerCAmelCase   					=  image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCAmelCase   					=  np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])

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




