
"""simple docstring"""





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def  lowerCamelCase__						(     __snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    [
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__snake_case,	__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    {}
					for old_key in state_dict.keys():
										_UpperCamelCase									=    old_key
										if "moe_layer.experts." in key:
															if expert_idx is not None:
																				_UpperCamelCase									=    key.replace('''moe_layer.experts.0''',	F'''ffn.experts.expert_{expert_idx}'''      )
															else:
																				_UpperCamelCase									=    key.replace('''moe_layer.experts.''',	'''ffn.experts.expert_'''      )
										if "gate" in key:
															_UpperCamelCase									=    key.replace('''.moe_layer.gate.wg''',	'''.ffn.router.classifier'''      )
										if "fc2" and "experts" not in key:
															_UpperCamelCase									=    key.replace('''.fc2.''',	'''.ffn.fc2.'''      )
										if "fc1" and "experts" not in key:
															_UpperCamelCase									=    key.replace('''.fc1.''',	'''.ffn.fc1.'''      )
										if ".encoder_attn." in key:
															_UpperCamelCase									=    key.replace('''.encoder_attn.''',	'''.cross_attention.'''      )
										if "encoder_attn_layer_norm" in key:
															_UpperCamelCase									=    key.replace('''encoder_attn_layer_norm''',	'''cross_attention_layer_norm'''      )
										if "final_layer_norm" in key:
															_UpperCamelCase									=    key.replace('''final_layer_norm''',	'''ff_layer_norm'''      )
										_UpperCamelCase									=    state_dict[old_key]
					return new_dict



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case = WEIGHTS_NAME      )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    0
					os.makedirs(__snake_case,	exist_ok=__snake_case      )

					for expert in range(__snake_case      ):
										_UpperCamelCase									=    switch_checkpoint_path + F'''-rank-{expert}.pt'''
										if os.path.isfile(__snake_case      ):
															_UpperCamelCase									=    torch.load(__snake_case      )['''model''']
															remove_ignore_keys_(__snake_case      )
															_UpperCamelCase									=    rename_fairseq_keys(__snake_case,	__snake_case      )
															_UpperCamelCase									=    os.path.join(
															    __snake_case,	weights_name.replace('''.bin''',	F'''-{len(__snake_case      )+1:05d}-of-???.bin'''      )      )
															torch.save(__snake_case,	__snake_case      )
															sharded_state_dicts.append(expert_state.keys()      )
															total_size += sum([value.numel() for key, value in expert_state.items()]      ) * dtype_byte_size(
															    expert_state[list(__snake_case      )[0]].dtype      )

    # Add the last block
					_UpperCamelCase									=    os.path.join(__snake_case,	weights_name.replace('''.bin''',	F'''-{len(__snake_case      )+1:05d}-of-???.bin'''      )      )
					_UpperCamelCase									=    torch.load(switch_checkpoint_path + '''-shared.pt'''      )['''model''']
					remove_ignore_keys_(__snake_case      )
					_UpperCamelCase									=    rename_fairseq_keys(__snake_case,	__snake_case      )
					_UpperCamelCase									=    shared_weights['''decoder.embed_tokens.weight''']
					sharded_state_dicts.append(shared_weights.keys()      )

					# If we only have the shared weights (dummy model/experts saved on the same file)
					if len(__snake_case      ) == 1:
										_UpperCamelCase									=    os.path.join(__snake_case,	__snake_case      )
										torch.save(__snake_case,	__snake_case      )
										return {weights_name: sharded_state_dicts[0]}, None
					else:
										torch.save(__snake_case,	__snake_case      )
					# Otherwise, let's build the index
					_UpperCamelCase									=    {}
					for idx, shard in enumerate(__snake_case      ):
										_UpperCamelCase									=    weights_name.replace('''.bin''',	F'''-{idx+1:05d}-of-{len(__snake_case      ):05d}.bin'''      )
										_UpperCamelCase									=    os.path.join(__snake_case,	weights_name.replace('''.bin''',	F'''-{idx+1:05d}-of-???.bin'''      )      )
										os.rename(__snake_case,	os.path.join(__snake_case,	__snake_case      )      )
										for key in shard:
															_UpperCamelCase									=    shard_file

    # Add the metadata
					_UpperCamelCase									=    {'''total_size''': total_size}
					_UpperCamelCase									=    {'''metadata''': metadata, '''weight_map''': weight_map}

					with open(os.path.join(__snake_case,	__snake_case      ),	'''w''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    json.dumps(__snake_case,	indent=2,	sort_keys=__snake_case      ) + '''\n'''
										f.write(__snake_case      )

					return metadata, index


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--nllb_moe_checkpoint_path""",
				    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
				    type=str,
				    required=False,
				    help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
				)
				parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
				    type=str,
				    required=False,
				    help="""Path to the output pytorch model.""",
				)
				_a								=						parser.parse_args()
				_a				,				_a								=						shard_on_the_fly(
				    args.nllb_moe_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    128,
				    args.dtype,
				)

				_a								=						NllbMoeConfig.from_pretrained(
				    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
				)
				config.save_pretrained(args.pytorch_dump_folder_path)
				_a								=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
				print("""Done""")
				model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

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





@skip_mps
class  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  Any           =		IFPipeline
      a			:  str           =		TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
      a			:  Dict           =		TEXT_TO_IMAGE_BATCH_PARAMS
      a			:  int           =		PipelineTesterMixin.required_optional_params - {"""latents"""}
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self._get_dummy_components()
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						int   ,							A_						:						Dict=0				):






       '''simple docstring'''


       if str(A_				).startswith("""mps"""				):
        __lowercase						=						torch.manual_seed(A_				)
       else:
        __lowercase						=						torch.Generator(device=A_				).manual_seed(A_				)

       __lowercase						=						{
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """output_type""": """numpy""",
       }

       return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_save_load_optional_components()
      @unittest.skipIf(torch_device != """cuda"""   ,							reason="""float16 requires CUDA"""				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().test_save_load_floataa(expected_max_diff=1e-1				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       self._test_save_load_local()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2   ,							)






      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()   ,							reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)






@slow
@require_torch_gpu
class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""   ,							variant="""fp16"""   ,							torch_dtype=torch.floataa				)

       __lowercase						=						IFSuperResolutionPipeline.from_pretrained(
           """DeepFloyd/IF-II-L-v1.0"""   ,							variant="""fp16"""   ,							torch_dtype=torch.floataa   ,							text_encoder=A_   ,							tokenizer=A_				)

       # pre compute text embeddings and remove T5 to save memory

       pipe_a.text_encoder.to("""cuda"""				)

       __lowercase				,		__lowercase						=						pipe_a.encode_prompt("""anime turtle"""   ,							device="""cuda"""				)

       del pipe_a.tokenizer
       del pipe_a.text_encoder
       gc.collect()

       __lowercase						=						None
       __lowercase						=						None

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # img2img

       __lowercase						=						IFImgaImgPipeline(**pipe_a.components				)
       __lowercase						=						IFImgaImgSuperResolutionPipeline(**pipe_a.components				)

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if_imgaimg(A_   ,							A_   ,							A_   ,							A_				)

       pipe_a.remove_all_hooks()
       pipe_a.remove_all_hooks()

       # inpainting

       __lowercase						=						IFInpaintingPipeline(**pipe_a.components				)
       __lowercase						=						IFInpaintingSuperResolutionPipeline(**pipe_a.components				)

       pipe_a.enable_model_cpu_offload()
       pipe_a.enable_model_cpu_offload()

       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
       pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

       self._test_if_inpainting(A_   ,							A_   ,							A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Any   ,							A_						:						int   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_3 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Tuple   ,							A_						:						List[Any]   ,							A_						:						List[Any]   ,							A_						:						Any				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						str   ,							A_						:						List[Any]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       _start_torch_memory_measurement()

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)
       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							num_inference_steps=2   ,							generator=A_   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (6_4, 6_4, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 1_0 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)

       # pipeline 2

       _start_torch_memory_measurement()

       __lowercase						=						torch.Generator(device="""cpu"""				).manual_seed(0				)

       __lowercase						=						floats_tensor((1, 3, 6_4, 6_4)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(0				)				).to(A_				)
       __lowercase						=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,							rng=random.Random(1				)				).to(A_				)

       __lowercase						=						pipe_a(
           prompt_embeds=A_   ,							negative_prompt_embeds=A_   ,							image=A_   ,							mask_image=A_   ,							original_image=A_   ,							generator=A_   ,							num_inference_steps=2   ,							output_type="""np"""   ,							)

       __lowercase						=						output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       __lowercase						=						torch.cuda.max_memory_allocated()
       assert mem_bytes < 4 * 1_0**9

       __lowercase						=						load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""				)
       assert_mean_pixel_difference(A_   ,							A_				)






def lowerCAmelCase_   (					):






 """simple docstring"""







 torch.cuda.empty_cache()
 torch.cuda.reset_max_memory_allocated()
 torch.cuda.reset_peak_memory_stats()
