





import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)

os.makedirs("""hub/hopper-medium-v2/value_function""", exist_ok=True)


def     lowerCamelCase_	(      lowerCAmelCase:	Union[str, Any]				)->		Union[str, Any]:
		if hor == 1_28:
				_snake_case   :				Tuple														=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
				_snake_case   :				List[str]														=	(32, 1_28, 2_56)
				_snake_case   :				List[Any]														=	('UpResnetBlock1D', 'UpResnetBlock1D')

		elif hor == 32:
				_snake_case   :				Optional[Any]														=	('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
				_snake_case   :				List[str]														=	(32, 64, 1_28, 2_56)
				_snake_case   :				Tuple														=	('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
		_snake_case   :				Optional[int]														=	torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""				)
		_snake_case   :				str														=	model.state_dict()
		_snake_case   :				List[Any]														=	{
		    'down_block_types': down_block_types,
		    'block_out_channels': block_out_channels,
		    'up_block_types': up_block_types,
		    'layers_per_block': 1,
		    'use_timestep_embedding': True,
		    'out_block_type': 'OutConv1DBlock',
		    'norm_num_groups': 8,
		    'downsample_each_block': False,
		    'in_channels': 14,
		    'out_channels': 14,
		    'extra_in_channels': 0,
		    'time_embedding_type': 'positional',
		    'flip_sin_to_cos': False,
		    'freq_shift': 1,
		    'sample_size': 6_55_36,
		    'mid_block_type': 'MidResTemporalBlock1D',
		    'act_fn': 'mish',
		}
		_snake_case   :				Optional[int]														=	UNetaDModel(**__lowerCAmelCase				)
		print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
		print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)
		_snake_case   :				Union[str, Any]														=	dict(zip(model.state_dict().keys()    ,							hf_value_function.state_dict().keys()				)				)
		for k, v in mapping.items():
				_snake_case   :				List[Any]														=	state_dict.pop(__lowerCAmelCase				)
		hf_value_function.load_state_dict(__lowerCAmelCase				)

		torch.save(hf_value_function.state_dict()    ,							F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""				)
		with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""    ,							'w'				) as f:
				json.dump(__lowerCAmelCase    ,							__lowerCAmelCase				)


def     lowerCamelCase_	(      )->		Optional[int]:
		_snake_case   :				Tuple														=	{
		    'in_channels': 14,
		    'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
		    'up_block_types': (),
		    'out_block_type': 'ValueFunction',
		    'mid_block_type': 'ValueFunctionMidBlock1D',
		    'block_out_channels': (32, 64, 1_28, 2_56),
		    'layers_per_block': 1,
		    'downsample_each_block': True,
		    'sample_size': 6_55_36,
		    'out_channels': 14,
		    'extra_in_channels': 0,
		    'time_embedding_type': 'positional',
		    'use_timestep_embedding': True,
		    'flip_sin_to_cos': False,
		    'freq_shift': 1,
		    'norm_num_groups': 8,
		    'act_fn': 'mish',
		}

		_snake_case   :				Optional[int]														=	torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'				)
		_snake_case   :				Union[str, Any]														=	model
		_snake_case   :				str														=	UNetaDModel(**__lowerCAmelCase				)
		print(F"""length of state dict: {len(state_dict.keys()				)}"""				)
		print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()				)}"""				)

		_snake_case   :				List[str]														=	dict(zip(state_dict.keys()    ,							hf_value_function.state_dict().keys()				)				)
		for k, v in mapping.items():
				_snake_case   :				List[Any]														=	state_dict.pop(__lowerCAmelCase				)

		hf_value_function.load_state_dict(__lowerCAmelCase				)

		torch.save(hf_value_function.state_dict()    ,							'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'				)
		with open('hub/hopper-medium-v2/value_function/config.json'    ,							'w'				) as f:
				json.dump(__lowerCAmelCase    ,							__lowerCAmelCase				)


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()











import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
      import tensorflow as tf

if is_keras_nlp_available():
      from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase_						   =    ["""gpt2"""]
lowerCAmelCase_						   =    """gpt2"""

if is_tf_available():



      class        _lowerCAmelCase    (			tf.Module							):


        '''simple docstring'''







        def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Dict							):


          '''simple docstring'''





          super().__init__()
          _snake_case   :				Optional[int]														=	tokenizer
          _snake_case   :				Union[str, Any]														=	AutoConfig.from_pretrained(UpperCamelCase							)
          _snake_case   :				int														=	TFGPTaLMHeadModel.from_config(UpperCamelCase							)







        @tf.function(input_signature=(tf.TensorSpec((None,)      ,				tf.string      ,				name='text'							),)							)
        def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					Optional[int]							):


          '''simple docstring'''





          _snake_case   :				Dict														=	self.tokenizer(UpperCamelCase							)
          _snake_case   :				Union[str, Any]														=	tokenized['input_ids'].to_tensor()

          _snake_case   :				Any														=	tf.cast(input_ids_dense > 0      ,				tf.intaa							)
          # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

          _snake_case   :				Tuple														=	self.model(input_ids=UpperCamelCase      ,				attention_mask=UpperCamelCase							)['logits']

          return outputs



@require_tf
@require_keras_nlp
class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    super().setUp()

    _snake_case   :				Optional[int]														=	[GPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
    _snake_case   :				Tuple														=	[TFGPTaTokenizer.from_pretrained(UpperCamelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
    assert len(self.tokenizers							) == len(self.tf_tokenizers							)

    _snake_case   :				Any														=	[
        'This is a straightforward English test sentence.',
        'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
        'Now we\'re going to add some Chinese: 一 二 三 一二三',
        'And some much more rare Chinese: 齉 堃 齉堃',
        'Je vais aussi écrire en français pour tester les accents',
        'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
    ]
    _snake_case   :				Tuple														=	list(zip(self.test_sentences      ,				self.test_sentences[::-1]							)							)







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    for tokenizer, tf_tokenizer in zip(self.tokenizers      ,				self.tf_tokenizers							):
      for test_inputs in self.test_sentences:
        _snake_case   :				Optional[int]														=	tokenizer([test_inputs]      ,				return_tensors='tf'							)
        _snake_case   :				Tuple														=	tf_tokenizer([test_inputs]							)

        for key in python_outputs.keys():
          # convert them to numpy to avoid messing with ragged tensors
          _snake_case   :				Dict														=	python_outputs[key].numpy()
          _snake_case   :				Optional[Any]														=	tf_outputs[key].numpy()

          self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
          self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase      ,				tf.intaa							) == tf_outputs_values							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				str														=	tf.function(UpperCamelCase							)
      for test_inputs in self.test_sentences:
        _snake_case   :				int														=	tf.constant(UpperCamelCase							)
        _snake_case   :				Tuple														=	compiled_tokenizer(UpperCamelCase							)
        _snake_case   :				int														=	tf_tokenizer(UpperCamelCase							)

        for key in eager_outputs.keys():
          self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Union[str, Any]														=	ModelToSave(tokenizer=UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Tuple														=	model.serving(UpperCamelCase							)  # Build model with some sample inputs
      with TemporaryDirectory() as tempdir:
        _snake_case   :				str														=	Path(UpperCamelCase							) / 'saved.model'
        tf.saved_model.save(UpperCamelCase      ,				UpperCamelCase      ,				signatures={'serving_default': model.serving}							)
        _snake_case   :				Optional[int]														=	tf.saved_model.load(UpperCamelCase							)
      _snake_case   :				List[str]														=	loaded_model.signatures['serving_default'](UpperCamelCase							)['output_0']
      # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
      self.assertTrue(tf.reduce_all(out == loaded_output							)							)







  @slow
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      _snake_case   :				Optional[Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
      _snake_case   :				Any														=	tf_tokenizer(UpperCamelCase							)  # Build model with some sample inputs

      _snake_case   :				Optional[Any]														=	tf_tokenizer.get_config()
      _snake_case   :				Tuple														=	TFGPTaTokenizer.from_config(UpperCamelCase							)
      _snake_case   :				Optional[Any]														=	model_from_config(UpperCamelCase							)

      for key in from_config_output.keys():
        self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)







  @slow
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    for tf_tokenizer in self.tf_tokenizers:
      # for the test to run
      _snake_case   :				Union[str, Any]														=	12_31_23

      for max_length in [3, 5, 10_24]:
        _snake_case   :				Union[str, Any]														=	tf.convert_to_tensor([self.test_sentences[0]]							)
        _snake_case   :				List[str]														=	tf_tokenizer(UpperCamelCase      ,				max_length=UpperCamelCase							)

        _snake_case   :				int														=	out['input_ids'].numpy().shape[1]

        assert out_length == max_length




