




'''simple docstring'''




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()
a_   :	Optional[Any]							   = logging.get_logger(__name__)

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

a_   :	Any							   = {
    "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,
}

a_   :	int							   = list("""ARNDCQEGHILKMFPSTWYV""")

a_   :	Any							   = restypes + ["X"]
a_   :	List[Any]							   = restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]





def   a_							(   )							->		Any:



       """simple docstring"""






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





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    List[Any]     )							->		Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=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_							(   __snake_case							:    int     , __snake_case							:    str     , __snake_case							:    Optional[int]     , __snake_case							:    Dict     , __snake_case							:    List[str]     )							->		List[Any]:



       """simple docstring"""






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

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

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

       if is_folding_model:
              lowerCamelCase_							=esm.esm
       else:
              lowerCamelCase_							=esm

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

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

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

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

       lowerCamelCase_							=original_esm_model.emb_layer_norm_after.weight
       lowerCamelCase_							=original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              lowerCamelCase_							=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)     )
              )

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

              if getattr(esm_layer.self_attn     , '''rot_emb'''     , UpperCamelCase_     ) 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.
                     lowerCamelCase_							=esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

       if is_folding_model:
              lowerCamelCase_							=esm.esm_s_combine.data
              lowerCamelCase_							=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:
              lowerCamelCase_							=esm.esm.classification_heads['''mnli'''].dense.weight
              lowerCamelCase_							=esm.classification_heads['''mnli'''].dense.bias
              lowerCamelCase_							=esm.classification_heads['''mnli'''].out_proj.weight
              lowerCamelCase_							=esm.classification_heads['''mnli'''].out_proj.bias
       else:
              # LM Head
              lowerCamelCase_							=esm.lm_head.dense.weight
              lowerCamelCase_							=esm.lm_head.dense.bias
              lowerCamelCase_							=esm.lm_head.layer_norm.weight
              lowerCamelCase_							=esm.lm_head.layer_norm.bias
              lowerCamelCase_							=esm.lm_head.weight
              lowerCamelCase_							=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.
              lowerCamelCase_							=SAMPLE_DATA[:2]
       else:
              lowerCamelCase_							=SAMPLE_DATA

       if is_folding_model:
              lowerCamelCase_							=get_esmfold_tokenizer()
              lowerCamelCase_							=hf_tokenizer(
                  [row[1] for row in sample_data]     , return_tensors='''pt'''     , padding=UpperCamelCase_     , add_special_tokens=UpperCamelCase_     )
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=esmfold_encode_sequences([row[1] for row in sample_data]     )
              lowerCamelCase_							=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.
              lowerCamelCase_							=alphabet.get_batch_converter()
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=batch_converter(UpperCamelCase_     )
              # Prepare tokenizer and make sure it matches
              with TemporaryDirectory() as tempdir:
                     lowerCamelCase_							='''\n'''.join(alphabet.all_toks     )
                     lowerCamelCase_							=Path(UpperCamelCase_     ) / '''vocab.txt'''
                     vocab_file.write_text(UpperCamelCase_     )
                     lowerCamelCase_							=EsmTokenizer(vocab_file=str(UpperCamelCase_     )     )

              lowerCamelCase_							=hf_tokenizer([row[1] for row in sample_data]     , return_tensors='''pt'''     , padding=UpperCamelCase_     )
              lowerCamelCase_							=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.
                     lowerCamelCase_							=esm.cuda().infer([row[1] for row in sample_data]     )
                     lowerCamelCase_							=model.cuda()(
                         input_ids=hf_tokens['''input_ids'''].cuda()     , attention_mask=hf_tokens['''attention_mask'''].cuda()     )
              else:
                     lowerCamelCase_							=model(**UpperCamelCase_     , output_hidden_states=UpperCamelCase_     )
                     lowerCamelCase_							=our_output['''logits''']
                     if classification_head:
                            lowerCamelCase_							=esm.model.classification_heads['''mnli'''](esm.extract_features(UpperCamelCase_     )     )
                     else:
                            lowerCamelCase_							=esm(hf_tokens['''input_ids''']     , repr_layers=list(range(999     )     )     )
                            lowerCamelCase_							=their_output['''logits''']

              if is_folding_model:
                     lowerCamelCase_							=torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']     )     ).item()
                     lowerCamelCase_							=torch.allclose(our_output['''positions''']     , their_output['''positions''']     , atol=1e-5     )
              else:
                     lowerCamelCase_							=torch.max(torch.abs(our_output - their_output     )     ).item()
                     lowerCamelCase_							=torch.allclose(UpperCamelCase_     , UpperCamelCase_     , 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
                     lowerCamelCase_							=model.predict_contacts(hf_tokens['''input_ids''']     , hf_tokens['''attention_mask''']     )
                     lowerCamelCase_							=esm.predict_contacts(hf_tokens['''input_ids''']     )
                     lowerCamelCase_							=torch.max(torch.abs(our_output - their_output     )     ).item()
                     lowerCamelCase_							=torch.allclose(UpperCamelCase_     , UpperCamelCase_     , 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(UpperCamelCase_     ).mkdir(parents=UpperCamelCase_     , exist_ok=UpperCamelCase_     )
              print(F'''Saving model to {pytorch_dump_folder_path}'''     )
              model.save_pretrained(UpperCamelCase_     )

              del esm  # Free up some memory before continuing

       print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''     )
       hf_tokenizer.save_pretrained(UpperCamelCase_     )

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


if __name__ == "__main__":
  a_   :	Dict							   = 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.""")
  a_   :	Dict							   = 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
  )










'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Tuple   		=KandinskyPipeline
       lowercase			:					Optional[int]   		=[
           'prompt',
           'image_embeds',
           'negative_image_embeds',
       ]
       lowercase			:					Union[str, Any]   		=['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
       lowercase			:					Dict   		=[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase			:					Dict   		=False






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 32






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self.time_input_dim * 4






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return 100






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''						)
              return tokenizer






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=MCLIPConfig(
                  numDims=self.cross_attention_dim,	transformerDimensions=self.text_embedder_hidden_size,	hidden_size=self.text_embedder_hidden_size,	intermediate_size=37,	num_attention_heads=4,	num_hidden_layers=5,	vocab_size=1_005,	)

              lowerCamelCase_							=MultilingualCLIP(lowerCAmelCase						)
              lowerCamelCase_							=text_encoder.eval()

              return text_encoder






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)

              lowerCamelCase_							={
                  '''in_channels''': 4,
                  # Out channels is double in channels because predicts mean and variance
                  '''out_channels''': 8,
                  '''addition_embed_type''': '''text_image''',
                  '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
                  '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
                  '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
                  '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
                  '''layers_per_block''': 1,
                  '''encoder_hid_dim''': self.text_embedder_hidden_size,
                  '''encoder_hid_dim_type''': '''text_image_proj''',
                  '''cross_attention_dim''': self.cross_attention_dim,
                  '''attention_head_dim''': 4,
                  '''resnet_time_scale_shift''': '''scale_shift''',
                  '''class_embed_type''': None,
              }

              lowerCamelCase_							=UNetaDConditionModel(**lowerCAmelCase						)
              return model






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return {
                  "block_out_channels": [32, 64],
                  "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                  "in_channels": 3,
                  "latent_channels": 4,
                  "layers_per_block": 1,
                  "norm_num_groups": 8,
                  "norm_type": "spatial",
                  "num_vq_embeddings": 12,
                  "out_channels": 3,
                  "up_block_types": [
                      "AttnUpDecoderBlock2D",
                      "UpDecoderBlock2D",
                  ],
                  "vq_embed_dim": 4,
              }






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=VQModel(**self.dummy_movq_kwargs						)
              return model






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=self.dummy_tokenizer
              lowerCamelCase_							=self.dummy_unet
              lowerCamelCase_							=self.dummy_movq

              lowerCamelCase_							=DDIMScheduler(
                  num_train_timesteps=1_000,	beta_schedule='''linear''',	beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	clip_sample=lowerCAmelCase,	set_alpha_to_one=lowerCAmelCase,	steps_offset=1,	prediction_type='''epsilon''',	thresholding=lowerCAmelCase,	)

              lowerCamelCase_							={
                  '''text_encoder''': text_encoder,
                  '''tokenizer''': tokenizer,
                  '''unet''': unet,
                  '''scheduler''': scheduler,
                  '''movq''': movq,
              }
              return components






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=0						):



              """simple docstring"""







              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(lowerCAmelCase						)						).to(lowerCAmelCase						)
              lowerCamelCase_							=floats_tensor((1, self.cross_attention_dim),	rng=random.Random(seed + 1						)						).to(lowerCAmelCase						)
              if str(lowerCAmelCase						).startswith('''mps'''						):
                     lowerCamelCase_							=torch.manual_seed(lowerCAmelCase						)
              else:
                     lowerCamelCase_							=torch.Generator(device=lowerCAmelCase						).manual_seed(lowerCAmelCase						)
              lowerCamelCase_							={
                  '''prompt''': '''horse''',
                  '''image_embeds''': image_embeds,
                  '''negative_image_embeds''': negative_image_embeds,
                  '''generator''': generator,
                  '''height''': 64,
                  '''width''': 64,
                  '''guidance_scale''': 4.0,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''np''',
              }
              return inputs






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''

              lowerCamelCase_							=self.get_dummy_components()

              lowerCamelCase_							=self.pipeline_class(**lowerCAmelCase						)
              lowerCamelCase_							=pipe.to(lowerCAmelCase						)

              pipe.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							=pipe(**self.get_dummy_inputs(lowerCAmelCase						)						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=pipe(
                  **self.get_dummy_inputs(lowerCAmelCase						),	return_dict=lowerCAmelCase,	)[0]

              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=image_from_tuple[0, -3:, -3:, -1]

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

              lowerCamelCase_							=np.array(
                  [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

              assert (
                  np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
              assert (
                  np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
              ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''						)

              lowerCamelCase_							=KandinskyPriorPipeline.from_pretrained(
                  '''kandinsky-community/kandinsky-2-1-prior''',	torch_dtype=torch.floataa						)
              pipe_prior.to(lowerCAmelCase						)

              lowerCamelCase_							=KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipeline.to(lowerCAmelCase						)
              pipeline.set_progress_bar_config(disable=lowerCAmelCase						)

              lowerCamelCase_							='''red cat, 4k photo'''

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_,   lowerCamelCase_							=pipe_prior(
                  lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=5,	negative_prompt='''''',	).to_tuple()

              lowerCamelCase_							=torch.Generator(device='''cuda'''						).manual_seed(0						)
              lowerCamelCase_							=pipeline(
                  lowerCAmelCase,	image_embeds=lowerCAmelCase,	negative_image_embeds=lowerCAmelCase,	generator=lowerCAmelCase,	num_inference_steps=100,	output_type='''np''',	)

              lowerCamelCase_							=output.images[0]

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

              assert_mean_pixel_difference(lowerCAmelCase,	lowerCAmelCase						)




