


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


snake_case							:	Optional[int]					      =				logging.get_logger(__name__)
logging.set_verbosity_info()






def 							__lowercase       (			__lowerCAmelCase							:    str ,   __lowerCAmelCase							:    str       ):
							if "xprophetnet" in prophetnet_checkpoint_path:
														a__					=					XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase       )
														a__				, a__					=					XLMProphetNetForConditionalGeneration.from_pretrained(
														    __lowerCAmelCase ,   output_loading_info=__lowerCAmelCase       )
							else:
														a__					=					ProphetNetForConditionalGenerationOld.from_pretrained(__lowerCAmelCase       )
														a__				, a__					=					ProphetNetForConditionalGeneration.from_pretrained(
														    __lowerCAmelCase ,   output_loading_info=__lowerCAmelCase       )

							a__					=					['key_proj', 'value_proj', 'query_proj']

							a__					=					{
							    '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"]:
														a__					=					key.split('.'       )

														if attributes[0] == "lm_head":
																					a__					=					prophet
																					a__					=					prophet_old
														else:
																					a__					=					prophet.prophetnet
																					a__					=					prophet_old.model

														a__					=					False
														for attribute in attributes:
																					if attribute in mapping:
																												a__					=					mapping[attribute]
																												if not hasattr(__lowerCAmelCase ,   __lowerCAmelCase       ) and len(__lowerCAmelCase       ) > 0:
																																			a__					=					attribute
																					elif hasattr(__lowerCAmelCase ,   __lowerCAmelCase       ):
																												a__					=					attribute

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												a__					=					old_model.weight
																												logger.info(F'{attribute} is initialized.'       )
																												a__					=					True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												a__					=					old_model.bias
																												logger.info(F'{attribute} is initialized'       )
																												a__					=					True
																												break
																					elif attribute in special_keys and hasattr(__lowerCAmelCase ,   'in_proj_weight'       ):
																												a__					=					old_model.in_proj_weight.shape[0] // 3
																												a__					=					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":
																																			a__					=					nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																																			a__					=					nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																												elif attribute == "key_proj":
																																			a__					=					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																																			a__					=					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																												elif attribute == "value_proj":
																																			a__					=					nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																																			a__					=					nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																												a__					=					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] == 5_1_2, "We want 512 position_embeddings."
																												a__					=					nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]       )
																												a__					=					True
																												break

																					if attribute.isdigit():
																												a__					=					model[int(__lowerCAmelCase       )]
																												a__					=					old_model[int(__lowerCAmelCase       )]
																					else:
																												a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

																												if old_attribute == "":
																																			a__					=					old_model
																												else:
																																			if not hasattr(__lowerCAmelCase ,   __lowerCAmelCase       ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}'       )
																																			a__					=					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__":
						snake_case							:	int					      =				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.'''
						)
						snake_case							:	Tuple					      =				parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








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 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-canny'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

												a__					=					'bird'
												a__					=					jax.device_count()
												a__					=					pipe.prepare_text_inputs([prompts] * num_samples				)

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					pipe(
												    prompt_ids=__snake_case      ,image=__snake_case      ,params=__snake_case      ,prng_seed=__snake_case      ,num_inference_steps=50      ,jit=__snake_case      ,).images
												assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2



					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-openpose'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

												a__					=					'Chef in the kitchen'
												a__					=					jax.device_count()
												a__					=					pipe.prepare_text_inputs([prompts] * num_samples				)

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

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					pipe(
												    prompt_ids=__snake_case      ,image=__snake_case      ,params=__snake_case      ,prng_seed=__snake_case      ,num_inference_steps=50      ,jit=__snake_case      ,).images
												assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




