



'''simple docstring'''






import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()



class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    UnCLIPPipeline
 UpperCAmelCase		      =    TEXT_TO_IMAGE_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
     """guidance_scale""",
     """prompt_embeds""",
     """cross_attention_kwargs""",
 }
 UpperCAmelCase		      =    TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase		      =    [
     """generator""",
     """return_dict""",
     """prior_num_inference_steps""",
     """decoder_num_inference_steps""",
     """super_res_num_inference_steps""",
 ]
 UpperCAmelCase		      =    False







 @property
 def _snake_case					(				self				)						->  List[Any]:
        return 32







 @property
 def _snake_case					(				self				)						->  List[Any]:
        return 32







 @property
 def _snake_case					(				self				)						->  Any:
        return self.time_input_dim







 @property
 def _snake_case					(				self				)						->  List[str]:
        return self.time_input_dim * 4







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        return 100







 @property
 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Dict     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)
        return tokenizer







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		int     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=self.text_embedder_hidden_size    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        return CLIPTextModelWithProjection(a_				)







 @property
 def _snake_case					(				self				)						->  str:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Optional[int]     	=					{
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCAmelCase    :		Any     	=					PriorTransformer(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  Tuple:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		List[str]     	=					{
            """clip_embeddings_dim""": self.text_embedder_hidden_size,
            """time_embed_dim""": self.time_embed_dim,
            """cross_attention_dim""": self.cross_attention_dim,
        }

        _UpperCAmelCase    :		Optional[Any]     	=					UnCLIPTextProjModel(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Optional[int]     	=					{
            """sample_size""": 32,
            # RGB in channels
            """in_channels""": 3,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 6,
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": """identity""",
        }

        _UpperCAmelCase    :		Union[str, Any]     	=					UNetaDConditionModel(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  str:
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }







 @property
 def _snake_case					(				self				)						->  List[Any]:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Any     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 @property
 def _snake_case					(				self				)						->  str:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1				)

        _UpperCAmelCase    :		Optional[int]     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Tuple     	=					self.dummy_prior
        _UpperCAmelCase    :		str     	=					self.dummy_decoder
        _UpperCAmelCase    :		Optional[int]     	=					self.dummy_text_proj
        _UpperCAmelCase    :		Tuple     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		Any     	=					self.dummy_tokenizer
        _UpperCAmelCase    :		List[Any]     	=					self.dummy_super_res_first
        _UpperCAmelCase    :		Tuple     	=					self.dummy_super_res_last

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""sample"""    ,num_train_timesteps=1_000    ,clip_sample_range=5.0    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""learned_range"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		int     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		List[Any]     	=					{
            """prior""": prior,
            """decoder""": decoder,
            """text_proj""": text_proj,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """super_res_first""": super_res_first,
            """super_res_last""": super_res_last,
            """prior_scheduler""": prior_scheduler,
            """decoder_scheduler""": decoder_scheduler,
            """super_res_scheduler""": super_res_scheduler,
        }

        return components







 def _snake_case					(				self    ,a_    ,a_=0				)						->  List[Any]:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		int     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[str]     	=					torch.Generator(device=a_				).manual_seed(a_				)
        _UpperCAmelCase    :		Any     	=					{
            """prompt""": """horse""",
            """generator""": generator,
            """prior_num_inference_steps""": 2,
            """decoder_num_inference_steps""": 2,
            """super_res_num_inference_steps""": 2,
            """output_type""": """numpy""",
        }
        return inputs







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Optional[int]     	=					"""cpu"""

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		List[Any]     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.to(a_				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					pipe(**self.get_dummy_inputs(a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					output.images

        _UpperCAmelCase    :		int     	=					pipe(
            **self.get_dummy_inputs(a_				)    ,return_dict=a_    ,)[0]

        _UpperCAmelCase    :		List[Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[int]     	=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        _UpperCAmelCase    :		Any     	=					np.array(
            [
                0.9997,
                0.9988,
                0.0028,
                0.9997,
                0.9984,
                0.9965,
                0.0029,
                0.9986,
                0.0025,
            ]				)

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







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		Optional[Any]     	=					torch.device("""cpu"""				)



        class 				lowercase			:


         """simple docstring"""


         UpperCAmelCase		      =    1







        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		Tuple     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Any     	=					pipe.to(a_				)

        _UpperCAmelCase    :		int     	=					components["""prior"""]
        _UpperCAmelCase    :		Optional[Any]     	=					components["""decoder"""]
        _UpperCAmelCase    :		str     	=					components["""super_res_first"""]
        _UpperCAmelCase    :		Any     	=					components["""tokenizer"""]
        _UpperCAmelCase    :		Union[str, Any]     	=					components["""text_encoder"""]

        _UpperCAmelCase    :		Tuple     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Optional[Any]     	=					prior.dtype
        _UpperCAmelCase    :		Tuple     	=					1

        _UpperCAmelCase    :		str     	=					(batch_size, prior.config.embedding_dim)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)
        _UpperCAmelCase    :		List[str]     	=					(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _UpperCAmelCase    :		List[str]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        _UpperCAmelCase    :		List[Any]     	=					(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _UpperCAmelCase    :		List[Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					"""this is a prompt example"""

        _UpperCAmelCase    :		Optional[Any]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipe(
            [prompt]    ,generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		List[str]     	=					output.images

        _UpperCAmelCase    :		Dict     	=					tokenizer(
            a_    ,padding="""max_length"""    ,max_length=tokenizer.model_max_length    ,return_tensors="""pt"""    ,)
        _UpperCAmelCase    :		Optional[int]     	=					text_encoder(text_inputs.input_ids				)
        _UpperCAmelCase    :		int     	=					text_inputs.attention_mask

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		str     	=					pipe(
            generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,text_model_output=a_    ,text_attention_mask=a_    ,output_type="""np"""    ,)[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text				).max() < 1E-4







 @skip_mps
 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		str     	=					torch_device == """cpu"""

        self._test_attention_slicing_forward_pass(test_max_difference=a_    ,expected_max_diff=0.01				)







 @skip_mps
 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Any     	=					torch_device == """cpu"""
        _UpperCAmelCase    :		Any     	=					True
        _UpperCAmelCase    :		Any     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=a_    ,relax_max_difference=a_    ,additional_params_copy_to_batched_inputs=a_    ,)







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Optional[Any]     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        if torch_device == "mps":
               # TODO: MPS errors with larger batch sizes
               _UpperCAmelCase    :		List[Any]     	=					[2, 3]
               self._test_inference_batch_consistent(
                   batch_sizes=a_    ,additional_params_copy_to_batched_inputs=a_    ,)
        else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=a_				)







 @skip_mps
 def _snake_case					(				self				)						->  Any:
        return super().test_dict_tuple_outputs_equivalent()







 @skip_mps
 def _snake_case					(				self				)						->  List[Any]:
        return super().test_save_load_local()







 @skip_mps
 def _snake_case					(				self				)						->  Optional[Any]:
        return super().test_save_load_optional_components()







@nightly
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_cpu.npy"""				)

        _UpperCAmelCase    :		Tuple     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Any     	=					torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipeline(
            """horse"""    ,num_images_per_prompt=1    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image				).max() < 1E-1







@slow
@require_torch_gpu
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_fp16.npy"""				)

        _UpperCAmelCase    :		List[str]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipeline.to(a_				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device="""cpu"""				).manual_seed(0				)
        _UpperCAmelCase    :		int     	=					pipeline(
            """horse"""    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[Any]     	=					output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCAmelCase    :		Union[str, Any]     	=					pipe(
            """horse"""    ,num_images_per_prompt=1    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9








"""simple docstring"""






import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
		from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
		raise ImportError("You need to install the version of fairseq from the Mega repo!")

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    int	)->   str:
								super().__init__()
								lowerCamelCase__						:						Tuple						      =mega_args
								lowerCamelCase__						:						List[Any]						      =nn.Embedding(lowerCamelCase,    self.mega_args.encoder_embed_dim	)
								lowerCamelCase__						:						List[Any]						      =nn.ModuleList([MegaEncoderLayer(self.mega_args	) for _ in range(lowerCamelCase	)]	)
								lowerCamelCase__						:						List[Any]						      =depth




					def 						snake_case			(			self				:    List[Any],    lowerCamelCase				:    int,    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   Union[str, Any]:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
											lowerCamelCase__						:						Any						      =input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
											lowerCamelCase__						:						Union[str, Any]						      =1 - attention_mask

								# get token embeddings from IDs
								lowerCamelCase__						:						Any						      =self.embedding_layer(lowerCamelCase	)

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
											lowerCamelCase__						:						Union[str, Any]						      =encoder(lowerCamelCase,    lowerCamelCase	)

								# return according to the shape specified
								if batch_first:
											# (T, B, H) --> (B, T, H)
											return torch.transpose(lowerCamelCase,    0,    1	)
								else:
											return embeds





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    Tuple	)->   Dict:
								super().__init__()
								lowerCamelCase__						:						Dict						      =MegaLM(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =nn.Linear(mega_args.encoder_embed_dim,    lowerCamelCase	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(p=0.1	)




					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    Optional[int],    lowerCamelCase				:    List[str],    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=0	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.mega(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self.mlm_head(self.dropout(lowerCamelCase	)	)






def 	snake_case__  (						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			with open(os.path.join(__lowerCamelCase				,						'''model_args.pkl'''      )				,						'''rb'''      ) as f:
						lowerCamelCase__						:						Any						      =pkl.load(__lowerCamelCase      )

			# load the original encoder
			lowerCamelCase__						:						Tuple						      =OriginalMegaForMaskedLM(**__lowerCamelCase      ).eval()

			# load its weights
			print(
			    '''Original Mega encoder:'''				,						original_mlm.mega.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''encoder_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)
			print(
			    '''Original Mega MLM layer:'''				,						original_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# create a new config from the old one
			lowerCamelCase__						:						Union[str, Any]						      =MegaConfig(
			    num_hidden_layers=mega_original_args['''depth''']				,						vocab_size=mega_original_args['''vocab_size''']				,						hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim				,						shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim				,						intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim				,						ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim				,						dropout_prob=mega_original_args['''mega_args'''].dropout				,						attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout				,						hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout				,						activation=mega_original_args['''mega_args'''].activation_fn				,						attention_activation=mega_original_args['''mega_args'''].attention_activation_fn				,						bidirectional=mega_original_args['''mega_args'''].bidirectional				,						use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0				,						chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size				,						truncation=mega_original_args['''mega_args'''].truncation_length				,						normalization_type=mega_original_args['''mega_args'''].normalization_type				,						normalize_before_mega=__lowerCamelCase				,						norm_affine=__lowerCamelCase				,						use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout				,						relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias				,						max_positions=mega_original_args['''mega_args'''].max_source_positions				,						nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim				,						normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before				,						nffn_activation_dropout_prob=0.0				,						add_token_type_embeddings=__lowerCamelCase				,						add_lm_hidden_dense_layer=__lowerCamelCase				,						)

			lowerCamelCase__						:						Union[str, Any]						      =MegaForMaskedLM(__lowerCamelCase      ).eval()

			# the originl checkpoint just uses nn.Embedding for the word embeddings
			# we use a wrapper module for embeddings to add support for positional embeddings
			lowerCamelCase__						:						str						      =original_mlm.mega.embedding_layer.weight

			# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
			# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
			# also renaming previously confusing parameter names
			lowerCamelCase__						:						int						      =original_mlm.mega.encoders.state_dict()
			lowerCamelCase__						:						List[Any]						      ={}
			for module_name in original_state_dict.keys():
						lowerCamelCase__						:						Union[str, Any]						      =None
						# have to handle gamma, beta, and alpha differently due to their use
						# in multiple modules within the original repository;
						# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
						# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
						if "beta" in module_name:
									# EMA sub-layers were always called "move" in the original repo
									if "move.beta" in module_name:
												lowerCamelCase__						:						Any						      =module_name.replace('''move.beta'''				,						'''ema_gate.ema_expansion_matrix'''      )
									elif "mega_layer.beta" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''beta'''				,						'''qk_bias'''      )
									else:
												lowerCamelCase__						:						Dict						      =module_name.replace('''beta'''				,						'''b_param'''      )
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
						elif "gamma" in module_name:
									if "move.gamma" in module_name:
												lowerCamelCase__						:						List[str]						      =module_name.replace('''move.gamma'''				,						'''ema_gate.kernel_projection_matrix'''      )
									elif "mega_layer.gamma" in module_name:
												lowerCamelCase__						:						Tuple						      =module_name.replace('''gamma'''				,						'''qk_weight'''      )
									else:
												lowerCamelCase__						:						int						      =module_name.replace('''gamma'''				,						'''g_param'''      )
        # alpha is used in EMA and positional bias; renaming to improve readability
						elif "move.alpha" in module_name:
									lowerCamelCase__						:						int						      =module_name.replace('''move.alpha'''				,						'''ema_gate.decay_factor'''      )
						# delta is only used in EMA; renaming to improve readability
						elif "move.delta" in module_name:
									lowerCamelCase__						:						Union[str, Any]						      =module_name.replace('''move.delta'''				,						'''ema_gate.damping_factor'''      )
						# omega is only used in EMA; renaming to improve readability
						elif "omega" in module_name:
									lowerCamelCase__						:						Any						      =module_name.replace('''move.omega'''				,						'''ema_gate.residual_weight'''      )

						if new_module_name:
									lowerCamelCase__						:						Union[str, Any]						      =new_module_name

			if len(__lowerCamelCase      ) != 0:
						print(f'''Renaming these keys: {updated_keys.keys()}'''      )
			else:
						print('''No need to rename state dict entries'''      )
			for old, new in updated_keys.items():
						lowerCamelCase__						:						Optional[int]						      =original_state_dict.pop(__lowerCamelCase      )

			# now attempt to load the state dictionary with updated names
			# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
			print('''HF Mega encoder:'''				,						hf_mlm.mega.layers.load_state_dict(__lowerCamelCase      )      )

			# load the MLM head weights directly
			print(
			    '''HF Mega MLM layer:'''				,						hf_mlm.mlm_head.load_state_dict(
			        torch.load(os.path.join(__lowerCamelCase				,						'''mlm_head_weights.pt'''      )				,						map_location='''cpu'''      )      )				,						)

			# test on a randomly generated input sequence
			lowerCamelCase__						:						str						      =torch.randint(0				,						hf_config.vocab_size				,						size=(4, 256)      )
			lowerCamelCase__						:						List[Any]						      =torch.ones_like(__lowerCamelCase      )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCamelCase__						:						List[str]						      =0

			# run forward passes
			lowerCamelCase__						:						Optional[int]						      =original_mlm(__lowerCamelCase				,						__lowerCamelCase				,						batch_first=__lowerCamelCase				,						ignore_mask_value=0      )
			lowerCamelCase__						:						Optional[int]						      =hf_mlm(__lowerCamelCase				,						__lowerCamelCase      )[0]

			# print shapes and diff
			print(f'''original output {original_output.shape}'''      )
			print(f'''hf output {hf_output.shape}'''      )
			print(f'''max diff: {(original_output - hf_output).max()}'''      )  # 0.0
			lowerCamelCase__						:						List[Any]						      =torch.allclose(__lowerCamelCase				,						__lowerCamelCase				,						atol=1e-3      )

			if success:
						print('''Yay!'''      )
						hf_mlm.save_pretrained(__lowerCamelCase      )
			else:
						raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''      )

			if includes_tokenizer:
						print('''Transferring tokenizer'''      )
						lowerCamelCase__						:						Any						      =AutoTokenizer.from_pretrained(__lowerCamelCase      )
						tokenizer.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
		_lowercase					:					Dict 		=		argparse.ArgumentParser()

		parser.add_argument(
		    "--pretrained_checkpoint_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Point to the directory containing your model weights using the official Mega repo",
		)

		parser.add_argument(
		    "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version"
		)

		parser.add_argument(
		    "--includes_tokenizer",
		    action="store_true",
		    help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo",
		)

		_lowercase					:					Dict 		=		parser.parse_args()

		convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)




