



'''simple docstring'''





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard






@slow
@require_flax
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	List[Any]   )					->					int:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()




  def 						_lowercase     ( self      :	List[str]   )					->					List[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = FlaxControlNetModel.from_pretrained(
        'lllyasviel/sd-controlnet-canny'			,	from_pt=_a			,	dtype=jnp.bfloataa   )
    __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = FlaxStableDiffusionControlNetPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'			,	controlnet=_a			,	from_pt=_a			,	dtype=jnp.bfloataa   )
    __lowerCamelCase       :       Dict           = controlnet_params

    __lowerCamelCase       :       str           = 'bird'
    __lowerCamelCase       :       Optional[Any]           = jax.device_count()
    __lowerCamelCase       :       Any           = pipe.prepare_text_inputs([prompts] * num_samples   )

    __lowerCamelCase       :       Tuple           = load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'   )
    __lowerCamelCase       :       Any           = pipe.prepare_image_inputs([canny_image] * num_samples   )

    __lowerCamelCase       :       List[str]           = jax.random.PRNGKey(0   )
    __lowerCamelCase       :       List[Any]           = jax.random.split(_a			,	jax.device_count()   )

    __lowerCamelCase       :       Any           = replicate(_a   )
    __lowerCamelCase       :       str           = shard(_a   )
    __lowerCamelCase       :       Any           = shard(_a   )

    __lowerCamelCase       :       Optional[Any]           = pipe(
        prompt_ids=_a			,	image=_a			,	params=_a			,	prng_seed=_a			,	num_inference_steps=50			,	jit=_a			,	).images
    assert images.shape == (jax.device_count(), 1, 768, 512, 3)

    __lowerCamelCase       :       List[Any]           = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __lowerCamelCase       :       Union[str, Any]           = images[0, 253:256, 253:256, -1]

    __lowerCamelCase       :       Tuple           = jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __lowerCamelCase       :       Optional[int]           = jnp.array(
        [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]   )
    print(f'output_slice: {output_slice}'   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2




  def 						_lowercase     ( self      :	List[Any]   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       Tuple           = FlaxControlNetModel.from_pretrained(
        'lllyasviel/sd-controlnet-openpose'			,	from_pt=_a			,	dtype=jnp.bfloataa   )
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[int]           = FlaxStableDiffusionControlNetPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'			,	controlnet=_a			,	from_pt=_a			,	dtype=jnp.bfloataa   )
    __lowerCamelCase       :       str           = controlnet_params

    __lowerCamelCase       :       List[Any]           = 'Chef in the kitchen'
    __lowerCamelCase       :       Optional[Any]           = jax.device_count()
    __lowerCamelCase       :       int           = pipe.prepare_text_inputs([prompts] * num_samples   )

    __lowerCamelCase       :       Optional[int]           = load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'   )
    __lowerCamelCase       :       int           = pipe.prepare_image_inputs([pose_image] * num_samples   )

    __lowerCamelCase       :       Optional[Any]           = jax.random.PRNGKey(0   )
    __lowerCamelCase       :       List[str]           = jax.random.split(_a			,	jax.device_count()   )

    __lowerCamelCase       :       int           = replicate(_a   )
    __lowerCamelCase       :       Optional[int]           = shard(_a   )
    __lowerCamelCase       :       Any           = shard(_a   )

    __lowerCamelCase       :       Dict           = pipe(
        prompt_ids=_a			,	image=_a			,	params=_a			,	prng_seed=_a			,	num_inference_steps=50			,	jit=_a			,	).images
    assert images.shape == (jax.device_count(), 1, 768, 512, 3)

    __lowerCamelCase       :       Optional[int]           = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __lowerCamelCase       :       str           = images[0, 253:256, 253:256, -1]

    __lowerCamelCase       :       Optional[int]           = jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __lowerCamelCase       :       Dict           = jnp.array(
        [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]   )
    print(f'output_slice: {output_slice}'   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1e-2











'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






