




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    os.path.join(args.tf_model_dir ,				'''parameters.json'''						)
       A__       =    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'''						):
              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(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'''						):
                                   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(lowercase_						)
                     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(lowercase_						)
                            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(lowercase_						)
                            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(lowercase_						)
                     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(lowercase_						)
                            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(lowercase_						)
                            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(lowercase_						)
                            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(lowercase_						)
                     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(lowercase_						)
                            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(lowercase_						)
                     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(lowercase_						)
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   A__       =    torch.tensor(lowercase_						)
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   A__       =    torch.tensor(lowercase_						)
                            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(lowercase_						)
                     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(lowercase_						)
                            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(lowercase_						)
                     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(lowercase_						)
                            if key_name.startswith('''model/wte'''						):
                                   A__       =    '''lm_head.weight'''
                                   A__       =    vnp.copy()  # same in embedded
                                   A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/wob'''						):
                            A__       =    '''final_logits_bias'''
                            A__       =    vnp.copy()  # same in embedded
                            A__       =    state.reshape((1, -1)						)
                            A__       =    torch.tensor(lowercase_						)
                     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(lowercase_						)
                     elif key_name == "model/dense_1/bias":
                            A__       =    '''model.last_project.bias'''
                            A__       =    vnp.copy()  # same because it is one dimensional
                            A__       =    torch.tensor(lowercase_						)
       torch.save(lowercase_ ,				args.output						)


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






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


