






"""simple docstring"""





# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()
class  __lowerCamelCase     (	_a				,						_a				,						_a				,						unittest.TestCase	):
   a   :					int			    =StableDiffusionControlNetInpaintPipeline
   a   :					List[Any]			    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a   :					Dict			    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a   :					int			    =frozenset({"""control_image"""}	)  # skip `image` and `mask` for now, only test for control_image
   a   :					List[Any]			    =TEXT_TO_IMAGE_IMAGE_PARAMS

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       torch.manual_seed(0   )
       UpperCamelCase__								=     UNetaDConditionModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					sample_size=32			,					in_channels=9			,					out_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,					cross_attention_dim=32			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     ControlNetModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					in_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					cross_attention_dim=32			,					conditioning_embedding_out_channels=(16, 32)			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     DDIMScheduler(
           beta_start=0.00_085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=snake_case_			,					set_alpha_to_one=snake_case_			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       UpperCamelCase__								=     CLIPTextModel(snake_case_   )
       UpperCamelCase__								=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

       UpperCamelCase__								=     {
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=0   )		->							Union[str, Any]:
       if str(snake_case_   ).startswith('mps'   ):
           UpperCamelCase__								=     torch.manual_seed(snake_case_   )
       else:
           UpperCamelCase__								=     torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

       UpperCamelCase__								=     2
       UpperCamelCase__								=     randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,					generator=snake_case_			,					device=torch.device(snake_case_   )			,					)
       UpperCamelCase__								=     floats_tensor((1, 3, 32, 32)			,					rng=random.Random(snake_case_   )   ).to(snake_case_   )
       UpperCamelCase__								=     init_image.cpu().permute(0			,					2			,					3			,					1   )[0]

       UpperCamelCase__								=     Image.fromarray(np.uinta(snake_case_   )   ).convert('RGB'   ).resize((64, 64)   )
       UpperCamelCase__								=     Image.fromarray(np.uinta(init_image + 4   )   ).convert('RGB'   ).resize((64, 64)   )

       UpperCamelCase__								=     {
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'mask_image': mask_image,
           'control_image': control_image,
       }

       return inputs

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )

   @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   )		->							int:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







class  __lowerCamelCase     (	_a	):
   a   :					Any			    =StableDiffusionControlNetInpaintPipeline
   a   :					int			    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a   :					Optional[Any]			    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a   :					Optional[Any]			    =frozenset([]	)

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       torch.manual_seed(0   )
       UpperCamelCase__								=     UNetaDConditionModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					sample_size=32			,					in_channels=4			,					out_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,					cross_attention_dim=32			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     ControlNetModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					in_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					cross_attention_dim=32			,					conditioning_embedding_out_channels=(16, 32)			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     DDIMScheduler(
           beta_start=0.00_085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=snake_case_			,					set_alpha_to_one=snake_case_			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       UpperCamelCase__								=     CLIPTextModel(snake_case_   )
       UpperCamelCase__								=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

       UpperCamelCase__								=     {
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components







class  __lowerCamelCase     (	_a				,						_a				,						unittest.TestCase	):
   a   :					Union[str, Any]			    =StableDiffusionControlNetInpaintPipeline
   a   :					Dict			    =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   a   :					Optional[int]			    =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       torch.manual_seed(0   )
       UpperCamelCase__								=     UNetaDConditionModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					sample_size=32			,					in_channels=9			,					out_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,					cross_attention_dim=32			,					)
       torch.manual_seed(0   )

       def init_weights(snake_case_   ):
           if isinstance(snake_case_			,					torch.nn.Convad   ):
               torch.nn.init.normal(m.weight   )
               m.bias.data.fill_(1.0   )

       UpperCamelCase__								=     ControlNetModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					in_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					cross_attention_dim=32			,					conditioning_embedding_out_channels=(16, 32)			,					)
       controlneta.controlnet_down_blocks.apply(snake_case_   )

       torch.manual_seed(0   )
       UpperCamelCase__								=     ControlNetModel(
           block_out_channels=(32, 64)			,					layers_per_block=2			,					in_channels=4			,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,					cross_attention_dim=32			,					conditioning_embedding_out_channels=(16, 32)			,					)
       controlneta.controlnet_down_blocks.apply(snake_case_   )

       torch.manual_seed(0   )
       UpperCamelCase__								=     DDIMScheduler(
           beta_start=0.00_085			,					beta_end=0.012			,					beta_schedule='scaled_linear'			,					clip_sample=snake_case_			,					set_alpha_to_one=snake_case_			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)
       UpperCamelCase__								=     CLIPTextModel(snake_case_   )
       UpperCamelCase__								=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

       UpperCamelCase__								=     MultiControlNetModel([controlneta, controlneta]   )

       UpperCamelCase__								=     {
           'unet': unet,
           'controlnet': controlnet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=0   )		->							int:
       if str(snake_case_   ).startswith('mps'   ):
           UpperCamelCase__								=     torch.manual_seed(snake_case_   )
       else:
           UpperCamelCase__								=     torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

       UpperCamelCase__								=     2

       UpperCamelCase__								=     [
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,					generator=snake_case_			,					device=torch.device(snake_case_   )			,					),
           randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)			,					generator=snake_case_			,					device=torch.device(snake_case_   )			,					),
       ]
       UpperCamelCase__								=     floats_tensor((1, 3, 32, 32)			,					rng=random.Random(snake_case_   )   ).to(snake_case_   )
       UpperCamelCase__								=     init_image.cpu().permute(0			,					2			,					3			,					1   )[0]

       UpperCamelCase__								=     Image.fromarray(np.uinta(snake_case_   )   ).convert('RGB'   ).resize((64, 64)   )
       UpperCamelCase__								=     Image.fromarray(np.uinta(init_image + 4   )   ).convert('RGB'   ).resize((64, 64)   )

       UpperCamelCase__								=     {
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
           'image': image,
           'mask_image': mask_image,
           'control_image': control_image,
       }

       return inputs

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__								=     self.get_dummy_components()
       UpperCamelCase__								=     self.pipeline_class(**snake_case_   )
       pipe.to(snake_case_   )

       UpperCamelCase__								=     10.0
       UpperCamelCase__								=     4

       UpperCamelCase__								=     self.get_dummy_inputs(snake_case_   )
       UpperCamelCase__								=     steps
       UpperCamelCase__								=     scale
       UpperCamelCase__								=     pipe(**snake_case_   )[0]

       UpperCamelCase__								=     self.get_dummy_inputs(snake_case_   )
       UpperCamelCase__								=     steps
       UpperCamelCase__								=     scale
       UpperCamelCase__								=     pipe(**snake_case_			,					control_guidance_start=0.1			,					control_guidance_end=0.2   )[0]

       UpperCamelCase__								=     self.get_dummy_inputs(snake_case_   )
       UpperCamelCase__								=     steps
       UpperCamelCase__								=     scale
       UpperCamelCase__								=     pipe(**snake_case_			,					control_guidance_start=[0.1, 0.3]			,					control_guidance_end=[0.2, 0.7]   )[0]

       UpperCamelCase__								=     self.get_dummy_inputs(snake_case_   )
       UpperCamelCase__								=     steps
       UpperCamelCase__								=     scale
       UpperCamelCase__								=     pipe(**snake_case_			,					control_guidance_start=0.4			,					control_guidance_end=[0.5, 0.8]   )[0]

       # make sure that all outputs are different
       assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
       assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
       assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )

   @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   )		->							Dict:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       UpperCamelCase__								=     self.get_dummy_components()
       UpperCamelCase__								=     self.pipeline_class(**snake_case_   )
       pipe.to(snake_case_   )
       pipe.set_progress_bar_config(disable=snake_case_   )
       with tempfile.TemporaryDirectory() as tmpdir:
           try:
               # save_pretrained is not implemented for Multi-ControlNet
               pipe.save_pretrained(snake_case_   )
           except NotImplementedError:
               pass







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

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       UpperCamelCase__								=     ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'   )

       UpperCamelCase__								=     StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-inpainting'			,					safety_checker=snake_case_			,					controlnet=snake_case_   )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=snake_case_   )

       UpperCamelCase__								=     torch.Generator(device='cpu'   ).manual_seed(0   )
       UpperCamelCase__								=     load_image(
           'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'   ).resize((512, 512)   )

       UpperCamelCase__								=     load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
           '/stable_diffusion_inpaint/input_bench_mask.png'   ).resize((512, 512)   )

       UpperCamelCase__								=     'pitch black hole'

       UpperCamelCase__								=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'   ).resize((512, 512)   )

       UpperCamelCase__								=     pipe(
           snake_case_			,					image=snake_case_			,					mask_image=snake_case_			,					control_image=snake_case_			,					generator=snake_case_			,					output_type='np'			,					num_inference_steps=3			,					)

       UpperCamelCase__								=     output.images[0]

       assert image.shape == (512, 512, 3)

       UpperCamelCase__								=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'   )

       assert np.abs(expected_image - image   ).max() < 9E-2







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'   )

       UpperCamelCase__								=     StableDiffusionControlNetInpaintPipeline.from_pretrained(
           'runwayml/stable-diffusion-v1-5'			,					safety_checker=snake_case_			,					controlnet=snake_case_   )
       UpperCamelCase__								=     DDIMScheduler.from_config(pipe.scheduler.config   )
       pipe.enable_model_cpu_offload()
       pipe.set_progress_bar_config(disable=snake_case_   )

       UpperCamelCase__								=     torch.Generator(device='cpu'   ).manual_seed(33   )

       UpperCamelCase__								=     load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'   )
       UpperCamelCase__								=     init_image.resize((512, 512)   )

       UpperCamelCase__								=     load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'   )
       UpperCamelCase__								=     mask_image.resize((512, 512)   )

       UpperCamelCase__								=     'a handsome man with ray-ban sunglasses'

       def make_inpaint_condition(snake_case_			,					snake_case_   ):
           UpperCamelCase__								=     np.array(image.convert('RGB'   )   ).astype(np.floataa   ) / 255.0
           UpperCamelCase__								=     np.array(image_mask.convert('L'   )   ).astype(np.floataa   ) / 255.0

           assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
           UpperCamelCase__								=     -1.0  # set as masked pixel
           UpperCamelCase__								=     np.expand_dims(snake_case_			,					0   ).transpose(0			,					3			,					1			,					2   )
           UpperCamelCase__								=     torch.from_numpy(snake_case_   )
           return image

       UpperCamelCase__								=     make_inpaint_condition(snake_case_			,					snake_case_   )

       UpperCamelCase__								=     pipe(
           snake_case_			,					image=snake_case_			,					mask_image=snake_case_			,					control_image=snake_case_			,					guidance_scale=9.0			,					eta=1.0			,					generator=snake_case_			,					num_inference_steps=20			,					output_type='np'			,					)
       UpperCamelCase__								=     output.images[0]

       assert image.shape == (512, 512, 3)

       UpperCamelCase__								=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'   )

       assert np.abs(expected_image - image   ).max() < 9E-2












"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

    if name is None:
        UpperCamelCase__								=     None
    else:
        UpperCamelCase__								=     '.' * max(0			,						spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
        UpperCamelCase__								=     fmt.format(SCREAMING_SNAKE_CASE       )

    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        if msg is not None:
            print(SCREAMING_SNAKE_CASE       )
        for k in val.keys():
            recursive_print(SCREAMING_SNAKE_CASE			,						val[k]			,						spaces + 2       )
    elif isinstance(SCREAMING_SNAKE_CASE			,						torch.Tensor       ):
        print(SCREAMING_SNAKE_CASE			,						':'			,						val.size()       )
    else:
        print(SCREAMING_SNAKE_CASE			,						':'			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

    UpperCamelCase__								=     param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCamelCase__								=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     param.transpose(0			,						1       ).contiguous()
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    return param







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     config.n_positions
    UpperCamelCase__								=     config.n_layer
    UpperCamelCase__								=     config.vocab_size
    UpperCamelCase__								=     config.n_head
    UpperCamelCase__								=     config.n_embd // config.n_head

    UpperCamelCase__								=     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    UpperCamelCase__								=     {
        'transformer.wte.weight': word_embeddings,
        'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
        'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
        'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
    }

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    UpperCamelCase__								=     word_embeddings

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     6_40_00
    UpperCamelCase__								=     config_megatron['encoder_seq_length']
    UpperCamelCase__								=     config_megatron['hidden_size']
    UpperCamelCase__								=     config_megatron['num_layers']
    UpperCamelCase__								=     config_megatron['num_attention_heads']
    UpperCamelCase__								=     config_megatron['ffn_hidden_size']
    UpperCamelCase__								=     'gelu'
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     config_megatron['layernorm_epsilon']  # 1e-5
    UpperCamelCase__								=     config_megatron['init_method_std']  # 0.02
    UpperCamelCase__								=     config_megatron['apply_query_key_layer_scaling']  # True
    UpperCamelCase__								=     True
    UpperCamelCase__								=     True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 40_96:
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     0  # <unk>
    else:
        UpperCamelCase__								=     2  # <s>
        UpperCamelCase__								=     3  # <|endoftext|>
        UpperCamelCase__								=     0  # <pad>

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

    # Load the config.
    UpperCamelCase__								=     checkpoint['hyper_parameters']['cfg']
    UpperCamelCase__								=     GPTaConfig()
    UpperCamelCase__								=     copy_config(config_hf=SCREAMING_SNAKE_CASE			,						config_megatron=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     ['GPT2LMHeadModel']

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     'GPTSw3Tokenizer'

    # Store the config to file.
    print('Saving config'       )
    config_hf.save_pretrained(SCREAMING_SNAKE_CASE       )

    # Store the state_dict to file.
    UpperCamelCase__								=     os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )
    print(F'Saving checkpoint to "{output_checkpoint_file}"'       )
    torch.save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			Optional[Any]=						argparse.ArgumentParser()
  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      required=True,
      help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
  )
  parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
  parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
  A__       :			Any=						parser.parse_args()
  main(_args)




