




import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_					:					str       						=			logging.get_logger(__name__)

snake_case_					:					Optional[Any]       						=			[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

snake_case_					:					Optional[int]       						=			{
    "esm1b_t33_650M_UR50S": esm_module.pretrained.esm1b_t33_650M_UR50S,
    "esm1v_t33_650M_UR90S_1": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    "esm1v_t33_650M_UR90S_2": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    "esm1v_t33_650M_UR90S_3": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    "esm1v_t33_650M_UR90S_4": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    "esm1v_t33_650M_UR90S_5": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    "esm2_t48_15B_UR50D": esm_module.pretrained.esm2_t48_15B_UR50D,
    "esm2_t36_3B_UR50D": esm_module.pretrained.esm2_t36_3B_UR50D,
    "esm2_t33_650M_UR50D": esm_module.pretrained.esm2_t33_650M_UR50D,
    "esm2_t30_150M_UR50D": esm_module.pretrained.esm2_t30_150M_UR50D,
    "esm2_t12_35M_UR50D": esm_module.pretrained.esm2_t12_35M_UR50D,
    "esm2_t6_8M_UR50D": esm_module.pretrained.esm2_t6_8M_UR50D,
    "esmfold_v1": esmfold_va,
}

snake_case_					:					str       						=			list("ARNDCQEGHILKMFPSTWYV")

snake_case_					:					Tuple       						=			restypes + ["X"]
snake_case_					:					Tuple       						=			restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]


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







     """simple docstring"""


     with TemporaryDirectory() as tempdir:
          UpperCAmelCase_		=				'''\n'''.join(__A			)
          UpperCAmelCase_		=				Path(__A			) / '''vocab.txt'''
          vocab_file.write_text(__A			)
          UpperCAmelCase_		=				EsmTokenizer(vocab_file=str(__A			)			)
     UpperCAmelCase_		=				0  # Overlaps with 'A' but that seems to be what they want
     return hf_tokenizer


def   A		(__A  :       Any  ,							__A  :       List[Any]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				our_module.load_state_dict(original_module.state_dict()			)
     if status.missing_keys:
          raise ValueError(F"""Missing keys: {status.missing_keys}"""			)
     if status.unexpected_keys:
          raise ValueError(F"""Unexpected keys: {status.unexpected_keys}"""			)


def   A		(__A  :       str  ,							__A  :       str  ,							__A  :       bool  ,							__A  :       str  ,							__A  :       str			)  -> List[Any]:







     """simple docstring"""


     if model.startswith('''esmfold'''			):
          UpperCAmelCase_		=				MODEL_MAPPING[model]()
     else:
          UpperCAmelCase_	,			UpperCAmelCase_		=				MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

     if model.startswith('''esmfold'''			):
          UpperCAmelCase_		=				esm.esm.embed_dim
          UpperCAmelCase_		=				esm.esm.num_layers
          UpperCAmelCase_		=				esm.esm.attention_heads
          UpperCAmelCase_		=				4 * embed_dim
          UpperCAmelCase_		=				esm.esm.token_dropout
          UpperCAmelCase_		=				False  # This code path does not exist in ESM-2
          UpperCAmelCase_		=				'''rotary'''
          UpperCAmelCase_		=				True
          UpperCAmelCase_		=				EsmFoldConfig()
          for key, val in esm.cfg.items():
               if hasattr(__A  ,							__A			) and key != "trunk":
                    setattr(__A  ,							__A  ,							__A			)
          for key, val in esm.cfg.trunk.items():
               if hasattr(esmfold_config.trunk  ,							__A			) and key != "structure_module":
                    setattr(esmfold_config.trunk  ,							__A  ,							__A			)
          for key, val in esm.cfg.trunk.structure_module.items():
               if hasattr(esmfold_config.trunk.structure_module  ,							__A			):
                    setattr(esmfold_config.trunk.structure_module  ,							__A  ,							__A			)
     elif hasattr(__A  ,							'''args'''			):
          # Indicates an ESM-1b or ESM-1v model
          UpperCAmelCase_		=				esm.args.embed_dim
          UpperCAmelCase_		=				esm.args.layers
          UpperCAmelCase_		=				esm.args.attention_heads
          UpperCAmelCase_		=				esm.args.ffn_embed_dim
          UpperCAmelCase_		=				esm.args.token_dropout
          UpperCAmelCase_		=				True if esm.emb_layer_norm_before else False
          UpperCAmelCase_		=				'''absolute'''
          UpperCAmelCase_		=				False
          UpperCAmelCase_		=				None
     else:
          # Indicates an ESM-2 model
          UpperCAmelCase_		=				esm.embed_dim
          UpperCAmelCase_		=				esm.num_layers
          UpperCAmelCase_		=				esm.attention_heads
          UpperCAmelCase_		=				4 * embed_dim  # This is hardcoded in ESM-2
          UpperCAmelCase_		=				esm.token_dropout
          UpperCAmelCase_		=				False  # This code path does not exist in ESM-2
          UpperCAmelCase_		=				'''rotary'''
          UpperCAmelCase_		=				False
          UpperCAmelCase_		=				None

     if is_folding_model:
          UpperCAmelCase_		=				esm.esm.alphabet
     UpperCAmelCase_		=				tuple(alphabet.all_toks			)
     UpperCAmelCase_		=				alphabet.mask_idx
     UpperCAmelCase_		=				alphabet.padding_idx

     if is_folding_model:
          UpperCAmelCase_		=				esm.esm
     else:
          UpperCAmelCase_		=				esm

     UpperCAmelCase_		=				EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings  ,							mask_token_id=__A  ,							hidden_size=__A  ,							num_hidden_layers=__A  ,							num_attention_heads=__A  ,							intermediate_size=__A  ,							max_position_embeddings=1026  ,							layer_norm_eps=1E-5  ,							attention_probs_dropout_prob=0.0  ,							hidden_dropout_prob=0.0  ,							pad_token_id=__A  ,							emb_layer_norm_before=__A  ,							token_dropout=__A  ,							position_embedding_type=__A  ,							is_folding_model=__A  ,							esmfold_config=__A  ,							vocab_list=__A  ,							)
     if classification_head:
          UpperCAmelCase_		=				esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our ESM config:'''  ,							__A			)

     if model.startswith('''esmfold'''			):
          UpperCAmelCase_		=				EsmForProteinFolding
     elif classification_head:
          UpperCAmelCase_		=				EsmForSequenceClassification
     else:
          UpperCAmelCase_		=				EsmForMaskedLM
     UpperCAmelCase_		=				model_class(__A			)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase_		=				original_esm_model.embed_tokens.weight
     if position_embedding_type == "absolute":
          UpperCAmelCase_		=				original_esm_model.embed_positions.weight

     if config.emb_layer_norm_before:
          UpperCAmelCase_		=				original_esm_model.emb_layer_norm_before.weight
          UpperCAmelCase_		=				original_esm_model.emb_layer_norm_before.bias

     UpperCAmelCase_		=				original_esm_model.emb_layer_norm_after.weight
     UpperCAmelCase_		=				original_esm_model.emb_layer_norm_after.bias

     for i in range(config.num_hidden_layers			):
          # Encoder: start of layer
          UpperCAmelCase_		=				model.esm.encoder.layer[i]
          # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
          UpperCAmelCase_		=				original_esm_model.layers[i]

          # self attention
          UpperCAmelCase_		=				layer.attention.self
          assert (
              esm_layer.self_attn.k_proj.weight.data.shape
              == esm_layer.self_attn.q_proj.weight.data.shape
              == esm_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)			)
          )

          UpperCAmelCase_		=				esm_layer.self_attn.q_proj.weight
          UpperCAmelCase_		=				esm_layer.self_attn.q_proj.bias
          UpperCAmelCase_		=				esm_layer.self_attn.k_proj.weight
          UpperCAmelCase_		=				esm_layer.self_attn.k_proj.bias
          UpperCAmelCase_		=				esm_layer.self_attn.v_proj.weight
          UpperCAmelCase_		=				esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn  ,							'''rot_emb'''  ,							__A			) is not None:
               # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
               # During the training of ESM-2 the model was converted to float16 precision, which also converts
               # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
               # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
               # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
               # we make sure the new model copies the data from the old inv_freq.
               UpperCAmelCase_		=				esm_layer.self_attn.rot_emb.inv_freq

          # LayerNorm changes for pre-activation
          UpperCAmelCase_		=				esm_layer.self_attn_layer_norm.weight
          UpperCAmelCase_		=				esm_layer.self_attn_layer_norm.bias
          UpperCAmelCase_		=				esm_layer.final_layer_norm.weight
          UpperCAmelCase_		=				esm_layer.final_layer_norm.bias

          # self-attention output
          UpperCAmelCase_		=				layer.attention.output
          assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase_		=				esm_layer.self_attn.out_proj.weight
          UpperCAmelCase_		=				esm_layer.self_attn.out_proj.bias

          # intermediate
          UpperCAmelCase_		=				layer.intermediate
          assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
          UpperCAmelCase_		=				esm_layer.fca.weight
          UpperCAmelCase_		=				esm_layer.fca.bias

          # output
          UpperCAmelCase_		=				layer.output
          assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
          UpperCAmelCase_		=				esm_layer.fca.weight
          UpperCAmelCase_		=				esm_layer.fca.bias
          # end of layer

     if is_folding_model:
          UpperCAmelCase_		=				esm.esm_s_combine.data
          UpperCAmelCase_		=				esm.afa_to_esm.data
          transfer_and_check_weights(esm.embedding  ,							model.embedding			)
          transfer_and_check_weights(esm.esm_s_mlp  ,							model.esm_s_mlp			)
          transfer_and_check_weights(esm.trunk  ,							model.trunk			)
          transfer_and_check_weights(esm.distogram_head  ,							model.distogram_head			)
          transfer_and_check_weights(esm.ptm_head  ,							model.ptm_head			)
          transfer_and_check_weights(esm.lm_head  ,							model.lm_head			)
          transfer_and_check_weights(esm.lddt_head  ,							model.lddt_head			)

     elif classification_head:
          UpperCAmelCase_		=				esm.esm.classification_heads['''mnli'''].dense.weight
          UpperCAmelCase_		=				esm.classification_heads['''mnli'''].dense.bias
          UpperCAmelCase_		=				esm.classification_heads['''mnli'''].out_proj.weight
          UpperCAmelCase_		=				esm.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_		=				esm.lm_head.dense.weight
          UpperCAmelCase_		=				esm.lm_head.dense.bias
          UpperCAmelCase_		=				esm.lm_head.layer_norm.weight
          UpperCAmelCase_		=				esm.lm_head.layer_norm.bias
          UpperCAmelCase_		=				esm.lm_head.weight
          UpperCAmelCase_		=				esm.lm_head.bias

     # Contact prediction head
     transfer_and_check_weights(esm.contact_head  ,							model.esm.contact_head			)

     # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
     if is_folding_model:
          # Folding models aren't trained on masked inputs and don't like mask tokens.
          UpperCAmelCase_		=				SAMPLE_DATA[:2]
     else:
          UpperCAmelCase_		=				SAMPLE_DATA

     if is_folding_model:
          UpperCAmelCase_		=				get_esmfold_tokenizer()
          UpperCAmelCase_		=				hf_tokenizer(
              [row[1] for row in sample_data]  ,							return_tensors='''pt'''  ,							padding=__A  ,							add_special_tokens=__A			)
          UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				esmfold_encode_sequences([row[1] for row in sample_data]			)
          UpperCAmelCase_		=				torch.all(hf_tokens['''input_ids'''] == esmfold_aas			) and torch.all(
              hf_tokens['''attention_mask'''] == esmfold_mask			)
     else:
          # Let's check that we get the same results.
          UpperCAmelCase_		=				alphabet.get_batch_converter()
          UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_		=				batch_converter(__A			)
          # Prepare tokenizer and make sure it matches
          with TemporaryDirectory() as tempdir:
               UpperCAmelCase_		=				'''\n'''.join(alphabet.all_toks			)
               UpperCAmelCase_		=				Path(__A			) / '''vocab.txt'''
               vocab_file.write_text(__A			)
               UpperCAmelCase_		=				EsmTokenizer(vocab_file=str(__A			)			)

          UpperCAmelCase_		=				hf_tokenizer([row[1] for row in sample_data]  ,							return_tensors='''pt'''  ,							padding=__A			)
          UpperCAmelCase_		=				torch.all(hf_tokens['''input_ids'''] == batch_tokens			)

     print('''Do both models tokenizers output the same tokens?'''  ,							'''🔥''' if success else '''💩'''			)
     if not success:
          raise Exception('''Tokenization does not match!'''			)

     with torch.no_grad():
          if is_folding_model:
               # Let's test the model in parts
               # ESMFold always converts the ESM stem to float16, which requires float16 ops
               # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
               # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
               # original and the converted model on the GPU at the same time.
               UpperCAmelCase_		=				esm.cuda().infer([row[1] for row in sample_data]			)
               UpperCAmelCase_		=				model.cuda()(
                   input_ids=hf_tokens['''input_ids'''].cuda()  ,							attention_mask=hf_tokens['''attention_mask'''].cuda()			)
          else:
               UpperCAmelCase_		=				model(**__A  ,							output_hidden_states=__A			)
               UpperCAmelCase_		=				our_output['''logits''']
               if classification_head:
                    UpperCAmelCase_		=				esm.model.classification_heads['''mnli'''](esm.extract_features(__A			)			)
               else:
                    UpperCAmelCase_		=				esm(hf_tokens['''input_ids''']  ,							repr_layers=list(range(999			)			)			)
                    UpperCAmelCase_		=				their_output['''logits''']

          if is_folding_model:
               UpperCAmelCase_		=				torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']			)			).item()
               UpperCAmelCase_		=				torch.allclose(our_output['''positions''']  ,							their_output['''positions''']  ,							atol=1E-5			)
          else:
               UpperCAmelCase_		=				torch.max(torch.abs(our_output - their_output			)			).item()
               UpperCAmelCase_		=				torch.allclose(__A  ,							__A  ,							atol=1E-5			)

          print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-5
          print('''Do both models output the same tensors?'''  ,							'''🔥''' if success else '''💩'''			)

          if not success:
               raise Exception('''Something went wRoNg'''			)

          if not is_folding_model:
               # Let's check contact prediction too
               UpperCAmelCase_		=				model.predict_contacts(hf_tokens['''input_ids''']  ,							hf_tokens['''attention_mask''']			)
               UpperCAmelCase_		=				esm.predict_contacts(hf_tokens['''input_ids''']			)
               UpperCAmelCase_		=				torch.max(torch.abs(our_output - their_output			)			).item()
               UpperCAmelCase_		=				torch.allclose(__A  ,							__A  ,							atol=1E-5			)

               print('''Contact prediction testing:'''			)
               print(F"""max_absolute_diff = {max_absolute_diff}"""			)  # ~ 1e-5
               print('''Do both models output the same tensors?'''  ,							'''🔥''' if success else '''💩'''			)

               if not success:
                    raise Exception('''Something went wRoNg'''			)

          pathlib.Path(__A			).mkdir(parents=__A  ,							exist_ok=__A			)
          print(F"""Saving model to {pytorch_dump_folder_path}"""			)
          model.save_pretrained(__A			)

          del esm  # Free up some memory before continuing

     print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
     hf_tokenizer.save_pretrained(__A			)

     if push_to_repo:
          model.push_to_hub(repo_id=__A  ,							use_auth_token=__A			)
          hf_tokenizer.push_to_hub(repo_id=__A  ,							use_auth_token=__A			)


if __name__ == "__main__":
    snake_case_					:					Tuple       						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--classification_head", action="store_true", help="Whether to convert a final classification head."
    )
    parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
    parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
    parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
    snake_case_					:					Tuple       						=			parser.parse_args()
    convert_esm_checkpoint_to_pytorch(
        args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
    )












import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       str		=    PaintByExamplePipeline
   UpperCAmelCase__    :       Tuple		=    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   UpperCAmelCase__    :       Any		=    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   UpperCAmelCase__    :       Union[str, Any]		=    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        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   ,		)
        UpperCAmelCase_		=				PNDMScheduler(skip_prk_steps=_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_		=				CLIPVisionConfig(
            hidden_size=32   ,		projection_dim=32   ,		intermediate_size=37   ,		layer_norm_eps=1e-05   ,		num_attention_heads=4   ,		num_hidden_layers=5   ,		image_size=32   ,		patch_size=4   ,		)
        UpperCAmelCase_		=				PaintByExampleImageEncoder(_snake_case   ,		proj_size=32)
        UpperCAmelCase_		=				CLIPImageProcessor(crop_size=32   ,		size=32)

        UpperCAmelCase_		=				{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''image_encoder''': image_encoder,
            '''safety_checker''': None,
            '''feature_extractor''': feature_extractor,
        }
        return components







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				np.array(image.convert('''RGB'''))
        UpperCAmelCase_		=				image[None].transpose(0   ,		3   ,		1   ,		2)
        UpperCAmelCase_		=				torch.from_numpy(_snake_case).to(dtype=torch.floataa) / 1_2_7.5 - 1.0
        return image







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		Dict="cpu"   ,		_snake_case						:		int=0):

        """simple docstring"""


        UpperCAmelCase_		=				floats_tensor((1, 3, 32, 32)   ,		rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_		=				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(image + 4)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((32, 32))

        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_		=				torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_		=				torch.Generator(device=_snake_case).manual_seed(_snake_case)
        UpperCAmelCase_		=				{
            '''example_image''': example_image,
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        UpperCAmelCase_		=				self.get_dummy_components()

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**_snake_case)
        UpperCAmelCase_		=				pipe.to('''cpu''')
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				self.get_dummy_inputs()
        UpperCAmelCase_		=				pipe(**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        UpperCAmelCase_		=				np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3])

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







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				'''cpu'''
        UpperCAmelCase_		=				self.get_dummy_inputs()
        inputs.pop('''mask_image''')
        UpperCAmelCase_		=				self.convert_to_pt(inputs.pop('''image'''))
        UpperCAmelCase_		=				image.clamp(0   ,		1) / 2

        # make sure here that pndm scheduler skips prk
        UpperCAmelCase_		=				PaintByExamplePipeline(**self.get_dummy_components())
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				pipe(image=_snake_case   ,		mask_image=mask_image[:, 0]   ,		**_snake_case)
        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image.cpu().permute(0   ,		2   ,		3   ,		1)[0]
        UpperCAmelCase_		=				mask_image.cpu().permute(0   ,		2   ,		3   ,		1)[0]

        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')
        UpperCAmelCase_		=				Image.fromarray(np.uinta(_snake_case)).convert('''RGB''')

        UpperCAmelCase_		=				pipe(**self.get_dummy_inputs())
        UpperCAmelCase_		=				output.images

        assert out_a.shape == (1, 64, 64, 3)
        assert np.abs(out_a.flatten() - out_a.flatten()).max() < 5e-2







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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







   def lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/dog_in_bucket.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/mask.png''')
        UpperCAmelCase_		=				load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/paint_by_example/panda.jpg''')

        UpperCAmelCase_		=				PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''')
        UpperCAmelCase_		=				pipe.to(_snake_case)
        pipe.set_progress_bar_config(disable=_snake_case)

        UpperCAmelCase_		=				torch.manual_seed(321)
        UpperCAmelCase_		=				pipe(
            image=_snake_case   ,		mask_image=_snake_case   ,		example_image=_snake_case   ,		generator=_snake_case   ,		guidance_scale=5.0   ,		num_inference_steps=50   ,		output_type='''np'''   ,		)

        UpperCAmelCase_		=				output.images

        UpperCAmelCase_		=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_		=				np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4])

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






