

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  lowercase_   							=  '0.12'  # assumed parallelism: 8


if is_torch_available():
  import torch
def 	a (     A__      :  Any ,  A__      :  Any ,  A__      :  List[str]=None	)       -> Optional[int]:





      """simple docstring"""



      if rng is None:
            _lowercase      			=random.Random()

      _lowercase      			=1
      for dim in shape:
            total_dims *= dim

      _lowercase      			=[]
      for _ in range(A__	):
            values.append(rng.randint(0 ,  vocab_size - 1	)	)

      _lowercase      			=np.array(A__ ,  dtype=jnp.intaa	).reshape(A__	)

      return output
def 	a (     A__      :  Optional[int] ,  A__      :  Tuple=None	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=ids_tensor(A__ ,  vocab_size=2 ,  rng=A__	)
      # make sure that at least one token is attended to for each batch
      _lowercase      			=1
      return attn_mask





@require_flax
class 					__lowerCAmelCase	:
  _a		     =    None
  _a		     =    ()




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        _lowercase      			=2
        _lowercase      			=inputs['input_ids'].shape[-1] // 2
        _lowercase      			=inputs['input_ids'][:max_batch_size, :sequence_length]

        _lowercase      			=jnp.ones_like(lowerCAmelCase   )
        _lowercase      			=attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        _lowercase      			=input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
              # hack to allow generate for models such as GPT2 as is done in `generate()`
              _lowercase      			=config.eos_token_id
        return config, input_ids, attention_mask, max_length




  @is_pt_flax_cross_test
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=False
        _lowercase      			=max_length
        _lowercase      			=0

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model_class.__name__[4:]  # Skip the "Flax" at the beginning
              _lowercase      			=getattr(lowerCAmelCase				,  lowerCAmelCase   )
              _lowercase      			=pt_model_class(lowerCAmelCase   ).eval()
              _lowercase      			=load_flax_weights_in_pytorch_model(lowerCAmelCase				,  flax_model.params   )

              _lowercase      			=flax_model.generate(lowerCAmelCase   ).sequences
              _lowercase      			=pt_model.generate(torch.tensor(lowerCAmelCase				,  dtype=torch.long   )   )

              if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                    _lowercase      			=flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

              self.assertListEqual(pt_generation_outputs.numpy().tolist()				,  flax_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					Any:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=False
        _lowercase      			=max_length

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=True
        _lowercase      			=max_length

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=False
        _lowercase      			=max_length
        _lowercase      			=2

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=False
        _lowercase      			=max_length
        _lowercase      			=2
        _lowercase      			=2

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[0]				,  input_ids.shape[0] * config.num_return_sequences   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=True
        _lowercase      			=max_length
        _lowercase      			=0.8
        _lowercase      			=10
        _lowercase      			=0.3
        _lowercase      			=1
        _lowercase      			=8
        _lowercase      			=9

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=max_length
        _lowercase      			=1
        _lowercase      			=8
        _lowercase      			=9

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()
        _lowercase      			=max_length
        _lowercase      			=2
        _lowercase      			=1
        _lowercase      			=8
        _lowercase      			=9

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()

        # pad attention mask on the left
        _lowercase      			=attention_mask.at[(0, 0)].set(0   )

        _lowercase      			=False
        _lowercase      			=max_length

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					List[Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()

        # pad attention mask on the left
        _lowercase      			=attention_mask.at[(0, 0)].set(0   )

        _lowercase      			=True
        _lowercase      			=max_length

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=self._get_input_ids_and_config()

        # pad attention mask on the left
        _lowercase      			=attention_mask.at[(0, 0)].set(0   )

        _lowercase      			=2
        _lowercase      			=max_length

        for model_class in self.all_generative_model_classes:
              _lowercase      			=model_class(lowerCAmelCase   )

              _lowercase      			=model.generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences
              self.assertEqual(generation_outputs.shape[-1]				,  lowerCAmelCase   )

              _lowercase      			=jit(model.generate   )
              _lowercase      			=jit_generate(lowerCAmelCase				,  attention_mask=lowerCAmelCase   ).sequences

              self.assertListEqual(generation_outputs.tolist()				,  jit_generation_outputs.tolist()   )





@require_flax
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=AutoTokenizer.from_pretrained('hf-internal-testing/tiny-bert'   )
        _lowercase      			=FlaxAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-bert-flax-only'   )

        _lowercase      			='Hello world'
        _lowercase      			=tokenizer(lowerCAmelCase				,  return_tensors='np'   ).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(lowerCAmelCase				,  'do_samples'   ):
              model.generate(lowerCAmelCase				,  do_samples=lowerCAmelCase   )

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(lowerCAmelCase				,  'foo'   ):
              _lowercase      			={'foo': 'bar'}
              model.generate(lowerCAmelCase				,  **lowerCAmelCase   )





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()

  parser.add_argument(
      '--repo_path',
      default=None,
      type=str,
      required=True,
      help='The config json file corresponding to the architecture.',
  )

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      'image_size': 'sample_size',
      'num_res_blocks': 'layers_per_block',
      'block_channels': 'block_out_channels',
      'down_blocks': 'down_block_types',
      'up_blocks': 'up_block_types',
      'downscale_freq_shift': 'freq_shift',
      'resnet_num_groups': 'norm_num_groups',
      'resnet_act_fn': 'act_fn',
      'resnet_eps': 'norm_eps',
      'num_head_channels': 'attention_head_dim',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' if has_file(args.repo_path, 'config.json') else 'unet'

  with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
    lowercase_   							=  reader.read()
    lowercase_   							=  json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, 'config.json'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowercase_   							=  dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowercase_   							=  config[key]
        del config[key]

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

  if do_only_weights:
    lowercase_   							=  torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

    lowercase_   							=  {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
        continue
      lowercase_   							=  False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split('.')[0] == key:
          lowercase_   							=  param_value
          lowercase_   							=  True
      if not has_changed:
        lowercase_   							=  param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))


