

"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	StableUnCLIPPipeline
 __UpperCAmelCase	:					List[Any]   		=	TEXT_TO_IMAGE_PARAMS
 __UpperCAmelCase	:					Tuple   		=	TEXT_TO_IMAGE_BATCH_PARAMS
 __UpperCAmelCase	:					List[Any]   		=	TEXT_TO_IMAGE_IMAGE_PARAMS
 __UpperCAmelCase	:					Union[str, Any]   		=	TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 __UpperCAmelCase	:					int   		=	False


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						3_2
    __lowercase								=						embedder_hidden_size

    # prior components

    torch.manual_seed(0	)
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPTextModelWithProjection(
        CLIPTextConfig(
            bos_token_id=0,					eos_token_id=2,					hidden_size=UpperCAmelCase__,					projection_dim=UpperCAmelCase__,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)	)

    torch.manual_seed(0	)
    __lowercase								=						PriorTransformer(
        num_attention_heads=2,					attention_head_dim=1_2,					embedding_dim=UpperCAmelCase__,					num_layers=1,					)

    torch.manual_seed(0	)
    __lowercase								=						DDPMScheduler(
        variance_type="fixed_small_log",					prediction_type="sample",					num_train_timesteps=1_0_0_0,					clip_sample=UpperCAmelCase__,					clip_sample_range=5.0,					beta_schedule="squaredcos_cap_v2",					)

    # regular denoising components

    torch.manual_seed(0	)
    __lowercase								=						StableUnCLIPImageNormalizer(embedding_dim=UpperCAmelCase__	)
    __lowercase								=						DDPMScheduler(beta_schedule="squaredcos_cap_v2"	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

    torch.manual_seed(0	)
    __lowercase								=						CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0,					eos_token_id=2,					hidden_size=UpperCAmelCase__,					projection_dim=3_2,					intermediate_size=3_7,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1_0_0_0,					)	)

    torch.manual_seed(0	)
    __lowercase								=						UNetaDConditionModel(
        sample_size=3_2,					in_channels=4,					out_channels=4,					down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),					up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),					block_out_channels=(3_2, 6_4),					attention_head_dim=(2, 4),					class_embed_type="projection",					projection_class_embeddings_input_dim=embedder_projection_dim * 2,					cross_attention_dim=UpperCAmelCase__,					layers_per_block=1,					upcast_attention=UpperCAmelCase__,					use_linear_projection=UpperCAmelCase__,					)

    torch.manual_seed(0	)
    __lowercase								=						DDIMScheduler(
        beta_schedule="scaled_linear",					beta_start=0.00_085,					beta_end=0.012,					prediction_type="v_prediction",					set_alpha_to_one=UpperCAmelCase__,					steps_offset=1,					)

    torch.manual_seed(0	)
    __lowercase								=						AutoencoderKL()

    __lowercase								=						{
        # prior components
        "prior_tokenizer": prior_tokenizer,
        "prior_text_encoder": prior_text_encoder,
        "prior": prior,
        "prior_scheduler": prior_scheduler,
        # image noising components
        "image_normalizer": image_normalizer,
        "image_noising_scheduler": image_noising_scheduler,
        # regular denoising components
        "tokenizer": tokenizer,
        "text_encoder": text_encoder,
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
    }

    return components


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[str]=0	):
    if str(UpperCAmelCase__	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    else:
       __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "prior_num_inference_steps": 2,
        "output_type": "numpy",
    }
    return inputs


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						torch_device == "cpu"

    self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase__	)



 def 					_lowercase (			self							:			int	):
    __lowercase								=						torch_device in ["cpu", "mps"]

    self._test_inference_batch_single_identical(test_max_difference=UpperCAmelCase__	)







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







 """simple docstring"""







 def 					_lowercase (			self							:			int	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"	)

    __lowercase								=						StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l",					torch_dtype=torch.floataa	)
    pipe.to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowercase								=						torch.Generator(device="cpu"	).manual_seed(0	)
    __lowercase								=						pipe("anime turle",					generator=UpperCAmelCase__,					output_type="np"	)

    __lowercase								=						output.images[0]

    assert image.shape == (7_6_8, 7_6_8, 3)

    assert_mean_pixel_difference(UpperCAmelCase__,					UpperCAmelCase__	)



 def 					_lowercase (			self							:			Dict	):
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    __lowercase								=						StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l",					torch_dtype=torch.floataa	)
    __lowercase								=						pipe.to(UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    __lowercase								=						pipe(
        "anime turtle",					prior_num_inference_steps=2,					num_inference_steps=2,					output_type="np",					)

    __lowercase								=						torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 1_0**9







"""simple docstring"""





import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_a       =							2




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Dict,					*,  # begin keyword-only arguments
     UpperCAmelCase__							:			str="<s>",					UpperCAmelCase__							:			Tuple="<pad>",					UpperCAmelCase__							:			str="</s>",					UpperCAmelCase__							:			Optional[Any]="<unk>",					UpperCAmelCase__							:			List[Any]=None,					):
    __lowercase		,__lowercase		,__lowercase		,__lowercase								=						bos, unk, pad, eos
    __lowercase								=						[]
    __lowercase								=						[]
    __lowercase								=						{}
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						self.add_symbol(UpperCAmelCase__	)
    if extra_special_symbols:
       for s in extra_special_symbols:
          self.add_symbol(UpperCAmelCase__	)
    __lowercase								=						len(self.symbols	)


 def __eq__(			self							:			List[str],					UpperCAmelCase__							:			Dict	):
    return self.indices == other.indices


 def __getitem__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str]	):
    if idx < len(self.symbols	):
       return self.symbols[idx]
    return self.unk_word


 def __len__(			self							:			str	):
    return len(self.symbols	)


 def __contains__(			self							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    return sym in self.indices


 @classmethod
 def 					_lowercase (			cls							:			List[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						cls()
    d.add_from_file(UpperCAmelCase__	)
    return d


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]=1,					UpperCAmelCase__							:			str=False	):
    if word in self.indices and not overwrite:
       __lowercase								=						self.indices[word]
       __lowercase								=						self.count[idx] + n
       return idx
    else:
       __lowercase								=						len(self.symbols	)
       __lowercase								=						idx
       self.symbols.append(UpperCAmelCase__	)
       self.count.append(UpperCAmelCase__	)
       return idx


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			str	):
    return 0



 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			List[Any]	):
    if isinstance(UpperCAmelCase__,					UpperCAmelCase__	):
       try:
          with open(UpperCAmelCase__,					"r",					encoding="utf-8"	) as fd:
             self.add_from_file(UpperCAmelCase__	)
       except FileNotFoundError as fnfe:
          raise fnfe
       except UnicodeError:
          raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCAmelCase__	)	)
       return

    __lowercase								=						f.readlines()
    __lowercase								=						self._load_meta(UpperCAmelCase__	)

    for line in lines[indices_start_line:]:
       try:
          __lowercase		,__lowercase								=						line.rstrip().rsplit(" ",					1	)
          if field == "#fairseq:overwrite":
             __lowercase								=						True
             __lowercase		,__lowercase								=						line.rsplit(" ",					1	)
          else:
             __lowercase								=						False
          __lowercase								=						int(UpperCAmelCase__	)
          __lowercase								=						line
          if word in self and not overwrite:
             raise RuntimeError(
                 "Duplicate word found when loading Dictionary: '{}'. "
                 "Duplicate words can overwrite earlier ones by adding the "
                 "#fairseq:overwrite flag at the end of the corresponding row "
                 "in the dictionary file. If using the Camembert model, please "
                 "download an updated copy of the model file.".format(UpperCAmelCase__	)	)
          self.add_symbol(UpperCAmelCase__,					n=UpperCAmelCase__,					overwrite=UpperCAmelCase__	)
       except ValueError:
          raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"	)







def   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


   __lowercase								=						dict((re.sub(r"@@$",			"",			UpperCamelCase_), v) if k.endswith("@@") else (re.sub(r"$",			"</w>",			UpperCamelCase_), v) for k, v in d.items())
   __lowercase								=						"<s> <pad> </s> <unk>".split()
   # restore the special tokens
   for k in keep_keys:
      del da[F"""{k}</w>"""]
      __lowercase								=						d[k]  # restore
   return da




def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						List[Any]:
   '''simple docstring'''


   if not os.path.exists(UpperCamelCase_):
      raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""")
   os.makedirs(UpperCamelCase_,			exist_ok=UpperCamelCase_)
   print(F"""Writing results to {pytorch_dump_folder_path}""")

   # handle various types of models

   __lowercase								=						os.path.join(UpperCamelCase_,			"checkpoint.pt")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {checkpoint_file} does not exist!""")
   __lowercase								=						torch.load(UpperCamelCase_,			map_location="cpu")

   __lowercase								=						chkpt["cfg"]["model"]

   # dicts
   __lowercase								=						os.path.join(UpperCamelCase_,			"dict.txt")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {dict_file} does not exist!""")
   __lowercase								=						Dictionary.load(UpperCamelCase_)
   __lowercase								=						rewrite_dict_keys(src_dict.indices)
   __lowercase								=						len(UpperCamelCase_)
   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["vocab_file"])
   print(F"""Generating {src_vocab_file} of {src_vocab_size} records""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # merges_file (bpecodes)
   __lowercase								=						os.path.join(UpperCamelCase_,			"bpecodes")
   if not os.path.isfile(UpperCamelCase_):
      raise ValueError(F"""path to the file {bpecodes_file} does not exist!""")

   __lowercase								=						os.path.join(UpperCamelCase_,			VOCAB_FILES_NAMES["merges_file"])
   shutil.copyfile(UpperCamelCase_,			UpperCamelCase_)

   # model config
   __lowercase								=						os.path.join(UpperCamelCase_,			"config.json")

   __lowercase								=						{
       "activation_dropout": args["activation_dropout"],
       "architectures": ["BioGptForCausalLM"],
       "attention_probs_dropout_prob": args["attention_dropout"],
       "bos_token_id": 0,
       "eos_token_id": 2,
       "hidden_act": args["activation_fn"],
       "hidden_dropout_prob": args["dropout"],
       "hidden_size": args["decoder_embed_dim"],
       "initializer_range": 0.02,
       "intermediate_size": args["decoder_ffn_embed_dim"],
       "layer_norm_eps": 1E-12,
       "layerdrop": args["decoder_layerdrop"],
       "max_position_embeddings": args["max_target_positions"],
       "model_type": "biogpt",
       "num_attention_heads": args["decoder_attention_heads"],
       "num_hidden_layers": args["decoder_layers"],
       "pad_token_id": 1,
       "scale_embedding": not args["no_scale_embedding"],
       "tie_word_embeddings": args["share_decoder_input_output_embed"],
       "vocab_size": src_vocab_size,
   }

   # good hparam defaults to start with

   print(F"""Generating {biogpt_model_config_file}""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # tokenizer config
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						{
       "bos_token": "<s>",
       "eos_token": "</s>",
       "model_max_length": 1024,
       "pad_token": "<pad>",
       "special_tokens_map_file": None,
       "tokenizer_class": "BioGptTokenizer",
       "unk_token": "<unk>",
   }

   print(F"""Generating {biogpt_tokenizer_config_file}""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(json.dumps(UpperCamelCase_,			ensure_ascii=UpperCamelCase_,			indent=UpperCamelCase_))

   # model
   __lowercase								=						chkpt["model"]

   # remove unneeded keys
   __lowercase								=						[
       "decoder.version",
   ]
   for k in ignore_keys:
      model_state_dict.pop(UpperCamelCase_,			UpperCamelCase_)

   __lowercase								=						list(model_state_dict.keys())
   for layer_name in layer_names:
      if layer_name.endswith("output_projection.weight"):
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)
      else:
         __lowercase								=						model_state_dict.pop(UpperCamelCase_)

   __lowercase								=						BioGptConfig.from_pretrained(UpperCamelCase_)
   __lowercase								=						BioGptForCausalLM(UpperCamelCase_)

   # check that it loads ok
   model_new.load_state_dict(UpperCamelCase_)

   # save
   __lowercase								=						os.path.join(UpperCamelCase_,			UpperCamelCase_)
   print(F"""Generating {pytorch_weights_dump_path}""")
   torch.save(UpperCamelCase_,			UpperCamelCase_)

   print("Conversion is done!")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--biogpt_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help=(
           'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
           ' bpecodes, etc.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




