

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


A_ :				Dict						 =    logging.get_logger(__name__)
logging.set_verbosity_info()

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

	A__							:					Any       =	["""key_proj""", """value_proj""", """query_proj"""]

	A__							:					Optional[int]       =	{
	    """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__							:					Optional[int]       =	key.split("""."""      )

		if attributes[0] == "lm_head":
			A__							:					int       =	prophet
			A__							:					Tuple       =	prophet_old
		else:
			A__							:					Optional[int]       =	prophet.prophetnet
			A__							:					Optional[int]       =	prophet_old.model

		A__							:					List[str]       =	False
		for attribute in attributes:
			if attribute in mapping:
				A__							:					str       =	mapping[attribute]
				if not hasattr(lowercase_					,   lowercase_      ) and len(lowercase_      ) > 0:
					A__							:					Union[str, Any]       =	attribute
			elif hasattr(lowercase_					,   lowercase_      ):
				A__							:					List[Any]       =	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				A__							:					Union[str, Any]       =	old_model.weight
				logger.info(f"""{attribute} is initialized."""      )
				A__							:					List[str]       =	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				A__							:					Tuple       =	old_model.bias
				logger.info(f"""{attribute} is initialized"""      )
				A__							:					Optional[Any]       =	True
				break
			elif attribute in special_keys and hasattr(lowercase_					,   """in_proj_weight"""      ):
				A__							:					Union[str, Any]       =	old_model.in_proj_weight.shape[0] // 3
				A__							:					Dict       =	getattr(lowercase_					,   lowercase_      )
				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__							:					Tuple       =	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
					A__							:					Optional[Any]       =	nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

				elif attribute == "key_proj":
					A__							:					int       =	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
					A__							:					List[Any]       =	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
				elif attribute == "value_proj":
					A__							:					Union[str, Any]       =	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
					A__							:					Dict       =	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
				A__							:					Union[str, Any]       =	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."
				A__							:					Union[str, Any]       =	nn.Parameter(old_model.embed_positions.weight[:512, :]      )
				A__							:					str       =	True
				break

			if attribute.isdigit():
				A__							:					Optional[Any]       =	model[int(lowercase_      )]
				A__							:					Optional[int]       =	old_model[int(lowercase_      )]
			else:
				A__							:					List[Any]       =	getattr(lowercase_					,   lowercase_      )

				if old_attribute == "":
					A__							:					Any       =	old_model
				else:
					if not hasattr(lowercase_					,   lowercase_      ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
					A__							:					str       =	getattr(lowercase_					,   lowercase_      )

		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(lowercase_      )


if __name__ == "__main__":
	A_ :				str						 =    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.'
	)
	A_ :				Union[str, Any]						 =    parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= TextToVideoSDPipeline
					UpperCAmelCase__:     Any													= TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__:     Optional[Any]													= TEXT_TO_IMAGE_BATCH_PARAMS
					# No `output_type`.
					UpperCAmelCase__:     Optional[int]													= frozenset(
					    [
					        '''num_inference_steps''',
					        '''generator''',
					        '''latents''',
					        '''return_dict''',
					        '''callback''',
					        '''callback_steps''',
					    ]       )






					def 						__A    (				self    ):
						torch.manual_seed(0    )
						A__							:					Optional[int]       =	UNetaDConditionModel(
						    block_out_channels=(32, 64, 64, 64)					, layers_per_block=2					, sample_size=32					, in_channels=4					, out_channels=4					, down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")					, up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")					, cross_attention_dim=32					, attention_head_dim=4					, )
						A__							:					Optional[int]       =	DDIMScheduler(
						    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule="""scaled_linear"""					, clip_sample=A__					, set_alpha_to_one=A__					, )
						torch.manual_seed(0    )
						A__							:					Union[str, Any]       =	AutoencoderKL(
						    block_out_channels=[32, 64]					, in_channels=3					, out_channels=3					, down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					, up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					, latent_channels=4					, sample_size=128					, )
						torch.manual_seed(0    )
						A__							:					Union[str, Any]       =	CLIPTextConfig(
						    bos_token_id=0					, eos_token_id=2					, hidden_size=32					, intermediate_size=37					, layer_norm_eps=1e-05					, num_attention_heads=4					, num_hidden_layers=5					, pad_token_id=1					, vocab_size=1000					, hidden_act="""gelu"""					, projection_dim=512					, )
						A__							:					Union[str, Any]       =	CLIPTextModel(A__    )
						A__							:					Tuple       =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

						A__							:					Dict       =	{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						}
						return components






					def 						__A    (				self					, A__					, A__=0    ):
						if str(A__    ).startswith("""mps"""    ):
							A__							:					Tuple       =	torch.manual_seed(A__    )
						else:
							A__							:					List[str]       =	torch.Generator(device=A__    ).manual_seed(A__    )
						A__							:					List[str]       =	{
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 6.0,
						    """output_type""": """pt""",
						}
						return inputs






					def 						__A    (				self    ):
						A__							:					List[str]       =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__							:					Union[str, Any]       =	self.get_dummy_components()
						A__							:					Union[str, Any]       =	TextToVideoSDPipeline(**A__    )
						A__							:					int       =	sd_pipe.to(A__    )
						sd_pipe.set_progress_bar_config(disable=A__    )

						A__							:					int       =	self.get_dummy_inputs(A__    )
						A__							:					int       =	"""np"""
						A__							:					Any       =	sd_pipe(**A__    ).frames
						A__							:					Dict       =	frames[0][-3:, -3:, -1]

						assert frames[0].shape == (64, 64, 3)
						A__							:					Optional[Any]       =	np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]    )

						assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2






					def 						__A    (				self    ):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A__					, expected_max_diff=3e-3    )






					@unittest.skipIf(
					    torch_device != """cuda""" or not is_xformers_available()					, reason="""XFormers attention is only available with CUDA and `xformers` installed"""					, )
					def 						__A    (				self    ):
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A__					, expected_max_diff=1e-2    )






					@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						return super().test_progress_bar()



@slow
@skip_mps
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""    )

						A__							:					Tuple       =	TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""    )
						A__							:					Any       =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
						A__							:					int       =	pipe.to("""cuda"""    )

						A__							:					Optional[Any]       =	"""Spiderman is surfing"""
						A__							:					List[str]       =	torch.Generator(device="""cpu"""    ).manual_seed(0    )

						A__							:					Optional[Any]       =	pipe(A__					, generator=A__					, num_inference_steps=25					, output_type="""pt"""    ).frames
						A__							:					Dict       =	video_frames.cpu().numpy()

						assert np.abs(expected_video - video    ).mean() < 5e-2






					def 						__A    (				self    ):
						A__							:					List[Any]       =	load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""    )

						A__							:					Optional[int]       =	TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""    )
						A__							:					List[str]       =	pipe.to("""cuda"""    )

						A__							:					Dict       =	"""Spiderman is surfing"""
						A__							:					Union[str, Any]       =	torch.Generator(device="""cpu"""    ).manual_seed(0    )

						A__							:					Optional[int]       =	pipe(A__					, generator=A__					, num_inference_steps=2					, output_type="""pt"""    ).frames
						A__							:					Optional[int]       =	video_frames.cpu().numpy()

						assert np.abs(expected_video - video    ).mean() < 5e-2



