'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						List[Any]					,  _a						:						str=0      ):




    """simple docstring"""



    if name is None:
        __UpperCamelCase						:						Tuple		     =	None
    else:
        __UpperCamelCase						:						List[str]		     =	'.' * max(0					,  spaces - 2      ) + '# {:' + str(5_0 - spaces      ) + 's}'
        __UpperCamelCase						:						List[Any]		     =	fmt.format(_a      )

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







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						Dict					,  _a						:						Optional[Any]					,  _a						:						Union[str, Any]					,  _a						:						Union[str, Any]      ):




    """simple docstring"""



    __UpperCamelCase						:						str		     =	param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        __UpperCamelCase						:						Optional[int]		     =	(num_heads, hidden_size, num_splits) + input_shape[1:]
        __UpperCamelCase						:						Dict		     =	param.view(*_a      )
        __UpperCamelCase						:						Any		     =	param.transpose(0					,  2      )
        __UpperCamelCase						:						Optional[Any]		     =	param.transpose(1					,  2      ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        __UpperCamelCase						:						Dict		     =	(num_heads, num_splits, hidden_size) + input_shape[1:]
        __UpperCamelCase						:						List[str]		     =	param.view(*_a      )
        __UpperCamelCase						:						Optional[int]		     =	param.transpose(0					,  1      ).contiguous()
    __UpperCamelCase						:						Dict		     =	param.view(*_a      )
    return param







def 			_UpperCamelCase			(  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						List[Any]      ):




    """simple docstring"""



    __UpperCamelCase						:						Dict		     =	{}

    # old versions did not store training args
    __UpperCamelCase						:						Optional[int]		     =	input_state_dict.get('args'					,  _a      )
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        __UpperCamelCase						:						Any		     =	ds_args.tokenizer_type
        __UpperCamelCase						:						Any		     =	ds_args.padded_vocab_size
        __UpperCamelCase						:						List[Any]		     =	ds_args.max_position_embeddings
        __UpperCamelCase						:						Union[str, Any]		     =	ds_args.hidden_size
        __UpperCamelCase						:						Any		     =	ds_args.num_layers
        __UpperCamelCase						:						Optional[int]		     =	ds_args.num_attention_heads
        __UpperCamelCase						:						List[str]		     =	ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

    # The number of heads.
    __UpperCamelCase						:						Union[str, Any]		     =	config.num_attention_heads
    # The hidden_size per head.
    __UpperCamelCase						:						Optional[Any]		     =	config.hidden_size // heads
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        __UpperCamelCase						:						Dict		     =	input_state_dict['checkpoint_version']
    else:
        __UpperCamelCase						:						List[str]		     =	0.0

    # The model.
    __UpperCamelCase						:						Tuple		     =	input_state_dict['model']
    # The language model.
    __UpperCamelCase						:						Tuple		     =	model['language_model']
    # The embeddings.
    __UpperCamelCase						:						Union[str, Any]		     =	lm['embedding']

    # The word embeddings.
    __UpperCamelCase						:						int		     =	embeddings['word_embeddings']['weight']
    # Truncate the embedding table to vocab_size rows.
    __UpperCamelCase						:						Any		     =	word_embeddings[: config.vocab_size, :]
    # Store the word embeddings.
    __UpperCamelCase						:						Optional[Any]		     =	word_embeddings

    # The position embeddings.
    __UpperCamelCase						:						List[str]		     =	embeddings['position_embeddings']['weight']
    assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
    # Store the position embeddings.
    __UpperCamelCase						:						Optional[Any]		     =	pos_embeddings

    # The token-type embeddings.
    __UpperCamelCase						:						Optional[Any]		     =	embeddings['tokentype_embeddings']['weight']
    # Store the position embeddings.
    __UpperCamelCase						:						Union[str, Any]		     =	tokentype_embeddings

    # The transformer.
    __UpperCamelCase						:						Optional[Any]		     =	lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

    # The regex to extract layer names.
    __UpperCamelCase						:						Union[str, Any]		     =	re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'      )

    # The simple map of names for "automated" rules.
    __UpperCamelCase						:						List[Any]		     =	{
        'attention.dense': '.attention.output.dense.',
        'self_attention.dense': '.attention.output.dense.',
        'mlp.dense_h_to_4h': '.intermediate.dense.',
        'mlp.dense_4h_to_h': '.output.dense.',
    }

    # Keep track of the attention/query/value tensor.
    __UpperCamelCase						:						int		     =	None

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        __UpperCamelCase						:						str		     =	layer_re.match(_a      )

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        __UpperCamelCase						:						int		     =	int(m.group(1      )      )
        # The name of the operation.
        __UpperCamelCase						:						List[str]		     =	m.group(2      )
        # Is it a weight or a bias?
        __UpperCamelCase						:						Tuple		     =	m.group(3      )

        # The name of the layer.
        __UpperCamelCase						:						Any		     =	f"""bert.encoder.layer.{layer_idx}"""

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith('layernorm'      ):
            __UpperCamelCase						:						List[Any]		     =	'attention.ln' if op_name.startswith('input'      ) else 'ln'
            __UpperCamelCase						:						List[str]		     =	val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Make sure the QKV pointer is nil.
            assert attention_qkv_weight is None, ""

            __UpperCamelCase						:						Union[str, Any]		     =	fix_query_key_value_ordering(_a					,  _a					,  3					,  _a					,  _a      )
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            __UpperCamelCase						:						Tuple		     =	out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            # Make sure we read the weight tensor.
            assert attention_qkv_weight is not None, ""

            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
            __UpperCamelCase						:						Tuple		     =	attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            __UpperCamelCase						:						Optional[int]		     =	attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            __UpperCamelCase						:						Optional[Any]		     =	attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            __UpperCamelCase						:						Tuple		     =	fix_query_key_value_ordering(_a					,  _a					,  3					,  _a					,  _a      )
            # Split the bias.
            __UpperCamelCase						:						List[Any]		     =	out_val[0 * config.hidden_size : 1 * config.hidden_size]
            __UpperCamelCase						:						List[str]		     =	out_val[1 * config.hidden_size : 2 * config.hidden_size]
            __UpperCamelCase						:						Union[str, Any]		     =	out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            __UpperCamelCase						:						List[str]		     =	q
            __UpperCamelCase						:						Optional[int]		     =	q_bias
            __UpperCamelCase						:						Dict		     =	k
            __UpperCamelCase						:						Dict		     =	k_bias
            __UpperCamelCase						:						str		     =	v
            __UpperCamelCase						:						str		     =	v_bias

            # Clear the stored tensor.
            __UpperCamelCase						:						Optional[Any]		     =	None

        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
            __UpperCamelCase						:						List[str]		     =	megatron_to_transformers[op_name]
            __UpperCamelCase						:						Union[str, Any]		     =	val

    # The final layernorm.
    __UpperCamelCase						:						Optional[int]		     =	transformer['final_layernorm.weight']
    __UpperCamelCase						:						Dict		     =	transformer['final_layernorm.bias']

    # The pooler.
    __UpperCamelCase						:						List[Any]		     =	lm['pooler']

    # Store the matrix and the bias.
    __UpperCamelCase						:						Optional[Any]		     =	pooler['dense.weight']
    __UpperCamelCase						:						Dict		     =	pooler['dense.bias']

    # The LM head from Megatron (for RACE).
    __UpperCamelCase						:						int		     =	model['lm_head']

    # The transform matrix.
    __UpperCamelCase						:						Tuple		     =	lm_head['dense.weight']
    __UpperCamelCase						:						Any		     =	lm_head['dense.bias']

    # The transform LN.
    __UpperCamelCase						:						List[Any]		     =	lm_head['layernorm.weight']
    __UpperCamelCase						:						List[str]		     =	lm_head['layernorm.bias']

    # For the decoder, we replicate the weights.
    __UpperCamelCase						:						Dict		     =	word_embeddings
    __UpperCamelCase						:						Dict		     =	lm_head['bias']

    # The classifier from Megatron (for MLNI).
    __UpperCamelCase						:						str		     =	model['binary_head']

    # Store the classifier.
    __UpperCamelCase						:						Any		     =	binary_head['weight']
    __UpperCamelCase						:						Dict		     =	binary_head['bias']

    # It should be done!
    return output_state_dict







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						List[str]		     =	argparse.ArgumentParser()
    parser.add_argument('--print-checkpoint-structure'					,  action='store_true'      )
    parser.add_argument('path_to_checkpoint'					,  type=_a					,  help='Path to the ZIP file containing the checkpoint'      )
    parser.add_argument(
        '--config_file'					,  default=''					,  type=_a					,  help='An optional config json file describing the pre-trained model.'					,  )
    __UpperCamelCase						:						int		     =	parser.parse_args()

    # Extract the basename.
    __UpperCamelCase						:						Optional[Any]		     =	os.path.dirname(args.path_to_checkpoint      )

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(f"""Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""""      )
    if args.path_to_checkpoint.endswith('.zip'      ):
        with zipfile.ZipFile(args.path_to_checkpoint					,  'r'      ) as checkpoint:
            with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'      ) as pytorch_dict:
                __UpperCamelCase						:						List[Any]		     =	torch.load(_a					,  map_location='cpu'      )
    else:
        __UpperCamelCase						:						Tuple		     =	torch.load(args.path_to_checkpoint					,  map_location='cpu'      )

    if args.config_file == "":
        # Default config of megatron-bert 345m
        __UpperCamelCase						:						Tuple		     =	MegatronBertConfig()

        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        __UpperCamelCase						:						List[Any]		     =	input_state_dict['model']['lm_head']['bias'].numel()
    else:
        __UpperCamelCase						:						Tuple		     =	MegatronBertConfig.from_json_file(args.config_file      )

    # Convert.
    print('Converting'      )
    __UpperCamelCase						:						Optional[int]		     =	convert_megatron_checkpoint(_a					,  _a					,  _a      )

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

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

    # Store the state_dict to file.
    __UpperCamelCase						:						Tuple		     =	os.path.join(_a					,  'pytorch_model.bin'      )
    print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""      )
    torch.save(_a					,  _a      )


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################




'''simple docstring'''
import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	{}
    for name in unet.attn_processors.keys():
        __UpperCamelCase						:						List[Any]		     =	None if name.endswith('attn1.processor'      ) else unet.config.cross_attention_dim
        if name.startswith('mid_block'      ):
            __UpperCamelCase						:						Optional[int]		     =	unet.config.block_out_channels[-1]
        elif name.startswith('up_blocks'      ):
            __UpperCamelCase						:						Optional[Any]		     =	int(name[len('up_blocks.'      )]      )
            __UpperCamelCase						:						List[Any]		     =	list(reversed(unet.config.block_out_channels      )      )[block_id]
        elif name.startswith('down_blocks'      ):
            __UpperCamelCase						:						Union[str, Any]		     =	int(name[len('down_blocks.'      )]      )
            __UpperCamelCase						:						Tuple		     =	unet.config.block_out_channels[block_id]
        __UpperCamelCase						:						int		     =	(
            LoRAAttnProcessor2_0 if hasattr(_a					,  'scaled_dot_product_attention'      ) else LoRAAttnProcessor
        )
        __UpperCamelCase						:						Tuple		     =	lora_attn_processor_class(
            hidden_size=_a					,  cross_attention_dim=_a      )
    __UpperCamelCase						:						List[str]		     =	AttnProcsLayers(_a      )
    return lora_attn_procs, unet_lora_layers







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	{}
    __UpperCamelCase						:						List[str]		     =	(
        LoRAAttnProcessor2_0 if hasattr(_a					,  'scaled_dot_product_attention'      ) else LoRAAttnProcessor
    )
    for name, module in text_encoder.named_modules():
        if name.endswith(_a      ):
            __UpperCamelCase						:						List[Any]		     =	lora_attn_processor_class(
                hidden_size=module.out_proj.out_features					,  cross_attention_dim=_a      )
    return text_lora_attn_procs







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	create_text_encoder_lora_attn_procs(_a      )
    __UpperCamelCase						:						Any		     =	AttnProcsLayers(_a      )
    return text_encoder_lora_layers







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						List[Any]=False      ):




    """simple docstring"""



    for _, attn_proc in text_lora_attn_procs.items():
        # set up.weights
        for layer_name, layer_module in attn_proc.named_modules():
            if layer_name.endswith('_lora'      ):
                __UpperCamelCase						:						List[str]		     =	(
                    torch.randn_like(layer_module.up.weight      )
                    if randn_weight
                    else torch.zeros_like(layer_module.up.weight      )
                )
                __UpperCamelCase						:						Any		     =	torch.nn.Parameter(_a      )






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,      cross_attention_dim=3_2  ,      )
         __UpperCamelCase						:						int		     =	DDIMScheduler(
             beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      beta_schedule='scaled_linear'  ,      clip_sample=_lowerCamelCase  ,      set_alpha_to_one=_lowerCamelCase  ,      steps_offset=1  ,      )
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	AutoencoderKL(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=4  ,      )
         __UpperCamelCase						:						str		     =	CLIPTextConfig(
             bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=3_2  ,      intermediate_size=3_7  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_0_0_0  ,      )
         __UpperCamelCase						:						List[str]		     =	CLIPTextModel(_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	create_unet_lora_layers(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_layers(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         __UpperCamelCase						:						List[str]		     =	{
             'unet_lora_layers': unet_lora_layers,
             'text_encoder_lora_layers': text_encoder_lora_layers,
             'unet_lora_attn_procs': unet_lora_attn_procs,
         }
         return pipeline_components, lora_components







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	1_0
         __UpperCamelCase						:						Dict		     =	4
         __UpperCamelCase						:						Optional[Any]		     =	(3_2, 3_2)

         __UpperCamelCase						:						Any		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	floats_tensor((batch_size, num_channels) + sizes     )
         __UpperCamelCase						:						List[str]		     =	torch.randint(1  ,      _lowerCamelCase  ,      size=(batch_size, sequence_length)  ,      generator=_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }

         return noise, input_ids, pipeline_inputs

         # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	7_7

         __UpperCamelCase						:						Any		     =	torch.randint(2  ,      5_6  ,      size=(1, max_seq_length)  ,      generator=torch.manual_seed(0     )     )

         __UpperCamelCase						:						List[str]		     =	{}
         __UpperCamelCase						:						Optional[int]		     =	inputs
         return prepared_inputs







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_components()
         LoraLoaderMixin.save_lora_weights(
             save_directory=_lowerCamelCase  ,      unet_lora_layers=lora_components['unet_lora_layers']  ,      text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,      )
         self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[str]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Dict		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Any		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						int		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_lowerCamelCase  ,      unet_lora_layers=lora_components['unet_lora_layers']  ,      text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,      )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase     )  ,      torch.from_numpy(_lowerCamelCase     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_components()
         __UpperCamelCase						:						str		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_lowerCamelCase  ,      unet_lora_layers=lora_components['unet_lora_layers']  ,      text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,      safe_serialization=_lowerCamelCase  ,      )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.safetensors'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase     )  ,      torch.from_numpy(_lowerCamelCase     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[Any]		     =	lora_components['unet_lora_attn_procs']
         __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.unet
             unet.set_attn_processor(_lowerCamelCase     )
             unet.save_attn_procs(_lowerCamelCase     )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[Any]		     =	lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase     )  ,      torch.from_numpy(_lowerCamelCase     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	self.get_dummy_tokens()

         # inference without lora
         __UpperCamelCase						:						List[str]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with zeroed out up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __UpperCamelCase						:						Tuple		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

         assert torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

         # create lora_attn_procs with randn up.weights
         __UpperCamelCase						:						Optional[int]		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __UpperCamelCase						:						Optional[int]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

         assert not torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

         __UpperCamelCase						:						List[Any]		     =	self.get_dummy_tokens()

         # inference without lora
         __UpperCamelCase						:						str		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with randn up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __UpperCamelCase						:						Any		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

         assert not torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "lora outputs should be different to without lora outputs"

         # remove monkey patch
         pipe._remove_text_encoder_monkey_patch()

         # inference with removed lora
         __UpperCamelCase						:						Dict		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

         assert torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "remove lora monkey patch should restore the original outputs"







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_lowerCamelCase  ,      unet_lora_layers=lora_components['unet_lora_layers']  ,      text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,      )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

         __UpperCamelCase						:						List[str]		     =	sd_pipe(**_lowerCamelCase  ,      cross_attention_kwargs={'scale': 0.5}     ).images
         __UpperCamelCase						:						Union[str, Any]		     =	lora_images_with_scale[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(
             torch.allclose(torch.from_numpy(_lowerCamelCase     )  ,      torch.from_numpy(_lowerCamelCase     )     )     )







     def lowerCAmelCase  (		self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_lowerCamelCase     )

             __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
             __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						Tuple		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

             # check if vanilla attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assertIsInstance(module.processor  ,      (AttnProcessor, AttnProcessor2_0)     )

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	(
                         LoRAAttnProcessor2_0 if hasattr(_lowerCamelCase  ,      'scaled_dot_product_attention'     ) else LoRAAttnProcessor
                     )
                     self.assertIsInstance(module.processor  ,      _lowerCamelCase     )







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test is supposed to run on GPU'     )
     def lowerCAmelCase  (		self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_lowerCamelCase     )

             __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
             __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

             # enable XFormers
             sd_pipe.enable_xformers_memory_efficient_attention()

             # check if xFormers attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assertIsInstance(module.processor  ,      _lowerCamelCase     )

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     self.assertIsInstance(module.processor  ,      _lowerCamelCase     )







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test is supposed to run on GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_inputs()

         # enable XFormers
         sd_pipe.enable_xformers_memory_efficient_attention()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             LoraLoaderMixin.save_lora_weights(
                 save_directory=_lowerCamelCase  ,      unet_lora_layers=lora_components['unet_lora_layers']  ,      text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,      )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_lowerCamelCase     )  ,      torch.from_numpy(_lowerCamelCase     )     )     )



