import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__lowerCamelCase					:	Optional[int]       =  {
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
		assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
		if args.mlm:
				assert os.path.isfile(args.token_counts	)
				assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
		else:
				assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

		assert args.teacher_type == args.student_type or (
		    args.student_type == "distilbert" and args.teacher_type == "bert"
		)
		assert os.path.isfile(args.student_config	)
		if args.student_pretrained_weights is not None:
				assert os.path.isfile(args.student_pretrained_weights	)

		if args.freeze_token_type_embds:
				assert args.student_type in ["roberta"]

		assert args.alpha_ce >= 0.0
		assert args.alpha_mlm >= 0.0
		assert args.alpha_clm >= 0.0
		assert args.alpha_mse >= 0.0
		assert args.alpha_cos >= 0.0
		assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    False
		elif args.student_type == "gpt2":
				SCREAMING_SNAKE_CASE_  :		int				      =    False




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		if args.student_type == "roberta":
				SCREAMING_SNAKE_CASE_  :		str				      =    False




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    argparse.ArgumentParser(description="Training"	)
		parser.add_argument("--force"   , action="store_true"   , help="Overwrite dump_path if it already exists."	)

		parser.add_argument(
		    "--dump_path"   , type=lowerCAmelCase   , required=lowerCAmelCase   , help="The output directory (log, checkpoints, parameters, etc.)"	)
		parser.add_argument(
		    "--data_file"   , type=lowerCAmelCase   , required=lowerCAmelCase   , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."   , )

		parser.add_argument(
		    "--student_type"   , type=lowerCAmelCase   , choices=["distilbert", "roberta", "gpt2"]   , required=lowerCAmelCase   , help="The student type (DistilBERT, RoBERTa)."   , )
		parser.add_argument("--student_config"   , type=lowerCAmelCase   , required=lowerCAmelCase   , help="Path to the student configuration."	)
		parser.add_argument(
		    "--student_pretrained_weights"   , default=lowerCAmelCase   , type=lowerCAmelCase   , help="Load student initialization checkpoint."	)

		parser.add_argument(
		    "--teacher_type"   , choices=["bert", "roberta", "gpt2"]   , required=lowerCAmelCase   , help="Teacher type (BERT, RoBERTa)."	)
		parser.add_argument("--teacher_name"   , type=lowerCAmelCase   , required=lowerCAmelCase   , help="The teacher model."	)

		parser.add_argument("--temperature"   , default=2.0   , type=lowerCAmelCase   , help="Temperature for the softmax temperature."	)
		parser.add_argument(
		    "--alpha_ce"   , default=0.5   , type=lowerCAmelCase   , help="Linear weight for the distillation loss. Must be >=0."	)
		parser.add_argument(
		    "--alpha_mlm"   , default=0.0   , type=lowerCAmelCase   , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."   , )
		parser.add_argument("--alpha_clm"   , default=0.5   , type=lowerCAmelCase   , help="Linear weight for the CLM loss. Must be >=0."	)
		parser.add_argument("--alpha_mse"   , default=0.0   , type=lowerCAmelCase   , help="Linear weight of the MSE loss. Must be >=0."	)
		parser.add_argument(
		    "--alpha_cos"   , default=0.0   , type=lowerCAmelCase   , help="Linear weight of the cosine embedding loss. Must be >=0."	)

		parser.add_argument(
		    "--mlm"   , action="store_true"   , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."	)
		parser.add_argument(
		    "--mlm_mask_prop"   , default=0.15   , type=lowerCAmelCase   , help="Proportion of tokens for which we need to make a prediction."   , )
		parser.add_argument("--word_mask"   , default=0.8   , type=lowerCAmelCase   , help="Proportion of tokens to mask out."	)
		parser.add_argument("--word_keep"   , default=0.1   , type=lowerCAmelCase   , help="Proportion of tokens to keep."	)
		parser.add_argument("--word_rand"   , default=0.1   , type=lowerCAmelCase   , help="Proportion of tokens to randomly replace."	)
		parser.add_argument(
		    "--mlm_smoothing"   , default=0.7   , type=lowerCAmelCase   , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."   , )
		parser.add_argument("--token_counts"   , type=lowerCAmelCase   , help="The token counts in the data_file for MLM."	)

		parser.add_argument(
		    "--restrict_ce_to_mask"   , action="store_true"   , help="If true, compute the distillation loss only the [MLM] prediction distribution."   , )
		parser.add_argument(
		    "--freeze_pos_embs"   , action="store_true"   , help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."   , )
		parser.add_argument(
		    "--freeze_token_type_embds"   , action="store_true"   , help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."   , )

		parser.add_argument("--n_epoch"   , type=lowerCAmelCase   , default=3   , help="Number of pass on the whole dataset."	)
		parser.add_argument("--batch_size"   , type=lowerCAmelCase   , default=5   , help="Batch size (for each process)."	)
		parser.add_argument(
		    "--group_by_size"   , action="store_false"   , help="If true, group sequences that have similar length into the same batch. Default is true."   , )

		parser.add_argument(
		    "--gradient_accumulation_steps"   , type=lowerCAmelCase   , default=5_0   , help="Gradient accumulation for larger training batches."   , )
		parser.add_argument("--warmup_prop"   , default=0.05   , type=lowerCAmelCase   , help="Linear warmup proportion."	)
		parser.add_argument("--weight_decay"   , default=0.0   , type=lowerCAmelCase   , help="Weight decay if we apply some."	)
		parser.add_argument("--learning_rate"   , default=5E-4   , type=lowerCAmelCase   , help="The initial learning rate for Adam."	)
		parser.add_argument("--adam_epsilon"   , default=1E-6   , type=lowerCAmelCase   , help="Epsilon for Adam optimizer."	)
		parser.add_argument("--max_grad_norm"   , default=5.0   , type=lowerCAmelCase   , help="Max gradient norm."	)
		parser.add_argument("--initializer_range"   , default=0.02   , type=lowerCAmelCase   , help="Random initialization range."	)

		parser.add_argument(
		    "--fp16"   , action="store_true"   , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   , )
		parser.add_argument(
		    "--fp16_opt_level"   , type=lowerCAmelCase   , default="O1"   , help=(
		        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
		        "See details at https://nvidia.github.io/apex/amp.html"
		    )   , )
		parser.add_argument("--n_gpu"   , type=lowerCAmelCase   , default=1   , help="Number of GPUs in the node."	)
		parser.add_argument("--local_rank"   , type=lowerCAmelCase   , default=-1   , help="Distributed training - Local rank"	)
		parser.add_argument("--seed"   , type=lowerCAmelCase   , default=5_6   , help="Random seed"	)

		parser.add_argument("--log_interval"   , type=lowerCAmelCase   , default=5_0_0   , help="Tensorboard logging interval."	)
		parser.add_argument("--checkpoint_interval"   , type=lowerCAmelCase   , default=4_0_0_0   , help="Checkpoint interval."	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    parser.parse_args()
		sanity_checks(lowerCAmelCase	)

		# ARGS #
		init_gpu_params(lowerCAmelCase	)
		set_seed(lowerCAmelCase	)
		if args.is_master:
				if os.path.exists(args.dump_path	):
						if not args.force:
								raise ValueError(
								    f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
								    " itUse `--force` if you want to overwrite it"	)
						else:
								shutil.rmtree(args.dump_path	)

				if not os.path.exists(args.dump_path	):
						os.makedirs(args.dump_path	)
				logger.info(f'Experiment will be dumped and logged in {args.dump_path}'	)

				# SAVE PARAMS #
				logger.info(f'Param: {args}'	)
				with open(os.path.join(args.dump_path   , "parameters.json"	)   , "w"	) as f:
						json.dump(vars(lowerCAmelCase	)   , lowerCAmelCase   , indent=4	)
				git_log(args.dump_path	)

		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		List[Any]				      =    MODEL_CLASSES[args.student_type]
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		SCREAMING_SNAKE_CASE_  :		Dict				      =    teacher_tokenizer_class.from_pretrained(args.teacher_name	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    tokenizer.all_special_tokens.index(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer.all_special_ids[idx]
		logger.info(f'Special tokens {special_tok_ids}'	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    special_tok_ids
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer.max_model_input_sizes[args.teacher_name]

		# DATA LOADER #
		logger.info(f'Loading data from {args.data_file}'	)
		with open(args.data_file   , "rb"	) as fp:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pickle.load(lowerCAmelCase	)

		if args.mlm:
				logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'	)
				with open(args.token_counts   , "rb"	) as fp:
						SCREAMING_SNAKE_CASE_  :		str				      =    pickle.load(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		int				      =    np.maximum(lowerCAmelCase   , 1	) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						SCREAMING_SNAKE_CASE_  :		str				      =    0.0  # do not predict special tokens
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.from_numpy(lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    None

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    LmSeqsDataset(params=lowerCAmelCase   , data=lowerCAmelCase	)
		logger.info("Data loader created."	)

		# STUDENT #
		logger.info(f'Loading student config from {args.student_config}'	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    student_config_class.from_pretrained(args.student_config	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    True

		if args.student_pretrained_weights is not None:
				logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'	)
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    student_model_class.from_pretrained(args.student_pretrained_weights   , config=lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    student_model_class(lowerCAmelCase	)

		if args.n_gpu > 0:
				student.to(f'cuda:{args.local_rank}'	)
		logger.info("Student loaded."	)

		# TEACHER #
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    teacher_model_class.from_pretrained(args.teacher_name   , output_hidden_states=lowerCAmelCase	)
		if args.n_gpu > 0:
				teacher.to(f'cuda:{args.local_rank}'	)
		logger.info(f'Teacher loaded from {args.teacher_name}.'	)

		# FREEZING #
		if args.freeze_pos_embs:
				freeze_pos_embeddings(lowerCAmelCase   , lowerCAmelCase	)
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(lowerCAmelCase   , lowerCAmelCase	)

		# SANITY CHECKS #
		assert student.config.vocab_size == teacher.config.vocab_size
		assert student.config.hidden_size == teacher.config.hidden_size
		assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
		if args.mlm:
				assert token_probs.size(0	) == stu_architecture_config.vocab_size

		# DISTILLER #
		torch.cuda.empty_cache()
		SCREAMING_SNAKE_CASE_  :		int				      =    Distiller(
		    params=lowerCAmelCase   , dataset=lowerCAmelCase   , token_probs=lowerCAmelCase   , student=lowerCAmelCase   , teacher=lowerCAmelCase	)
		distiller.train()
		logger.info("Let's go get some drinks."	)


if __name__ == "__main__":
		main()


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


__A				=logging.get_logger(__name__)  # pylint: disable=invalid-name

__A				='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''




@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					super().__init__()

					self.register_modules(
					    prior=lowercase						,  image_encoder=lowercase						,  image_processor=lowercase						,  scheduler=lowercase						,  renderer=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if latents is None:
									lowerCamelCase_						=			randn_tensor(lowercase						,  generator=lowercase						,  device=lowercase						,  dtype=lowercase )
					else:
									if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
									lowerCamelCase_						=			latents.to(lowercase )

					lowerCamelCase_						=			latents * scheduler.init_noise_sigma
					return latents



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=0 )				->       int:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					lowerCamelCase_						=			torch.device(f'cuda:{gpu_id}' )

					lowerCamelCase_						=			[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					if self.device != torch.device("meta" ) or not hasattr(self.image_encoder						,  "_hf_hook" ):
									return self.device
					for module in self.image_encoder.modules():
									if (
									    hasattr(lowercase						,  "_hf_hook" )
									    and hasattr(module._hf_hook						,  "execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					if isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  torch.Tensor ):
									lowerCamelCase_						=			torch.cat(lowercase						,  axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase						,  axis=0 )

					if not isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			self.image_processor(lowercase						,  return_tensors="pt" ).pixel_values[0].unsqueeze(0 )

					lowerCamelCase_						=			image.to(dtype=self.image_encoder.dtype						,  device=lowercase )

					lowerCamelCase_						=			self.image_encoder(lowercase )["last_hidden_state"]
					lowerCamelCase_						=			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCamelCase_						=			image_embeds.repeat_interleave(lowercase						,  dim=0 )

					if do_classifier_free_guidance:
									lowerCamelCase_						=			torch.zeros_like(lowercase )

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCamelCase_						=			torch.cat([negative_image_embeds, image_embeds] )

					return image_embeds



	@torch.no_grad()
	@replace_example_docstring(lowercase )
	def __call__(    self						,  lowercase						,  lowercase = 1						,  lowercase = 25						,  lowercase = None						,  lowercase = None						,  lowercase = 4.0						,  lowercase = 64						,  lowercase = "pil"						,  lowercase = True						,  )				->       Union[str, Any]:

					if isinstance(lowercase						,  PIL.Image.Image ):
									lowerCamelCase_						=			1
					elif isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			image.shape[0]
					elif isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  (torch.Tensor, PIL.Image.Image) ):
									lowerCamelCase_						=			len(lowercase )
					else:
									raise ValueError(
									    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase )}' )

					lowerCamelCase_						=			self._execution_device

					lowerCamelCase_						=			batch_size * num_images_per_prompt

					lowerCamelCase_						=			guidance_scale > 1.0
					lowerCamelCase_						=			self._encode_image(lowercase						,  lowercase						,  lowercase						,  lowercase )

					# prior

					self.scheduler.set_timesteps(lowercase						,  device=lowercase )
					lowerCamelCase_						=			self.scheduler.timesteps

					lowerCamelCase_						=			self.prior.config.num_embeddings
					lowerCamelCase_						=			self.prior.config.embedding_dim

					lowerCamelCase_						=			self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)						,  image_embeds.dtype						,  lowercase						,  lowercase						,  lowercase						,  self.scheduler						,  )

					# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
					lowerCamelCase_						=			latents.reshape(latents.shape[0]						,  lowercase						,  lowercase )

					for i, t in enumerate(self.progress_bar(lowercase ) ):
									# expand the latents if we are doing classifier free guidance
									lowerCamelCase_						=			torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
									lowerCamelCase_						=			self.scheduler.scale_model_input(lowercase						,  lowercase )

									lowerCamelCase_						=			self.prior(
									    lowercase						,  timestep=lowercase						,  proj_embedding=lowercase						,  ).predicted_image_embedding

									# remove the variance
									lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.split(
									    scaled_model_input.shape[2]						,  dim=2 )  # batch_size, num_embeddings, embedding_dim

									if do_classifier_free_guidance is not None:
													lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.chunk(2 )
													lowerCamelCase_						=			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									lowerCamelCase_						=			self.scheduler.step(
									    lowercase						,  timestep=lowercase						,  sample=lowercase						,  ).prev_sample

					if output_type == "latent":
									return ShapEPipelineOutput(images=lowercase )

					lowerCamelCase_						=			[]
					for i, latent in enumerate(lowercase ):
									print()
									lowerCamelCase_						=			self.renderer.decode(
									    latent[None, :]						,  lowercase						,  size=lowercase						,  ray_batch_size=4096						,  n_coarse_samples=64						,  n_fine_samples=128						,  )

									images.append(lowercase )

					lowerCamelCase_						=			torch.stack(lowercase )

					if output_type not in ["np", "pil"]:
									raise ValueError(f'Only the output types `pil` and `np` are supported not output_type={output_type}' )

					lowerCamelCase_						=			images.cpu().numpy()

					if output_type == "pil":
									lowerCamelCase_						=			[self.numpy_to_pil(lowercase ) for image in images]

					# Offload last model to CPU
					if hasattr(self						,  "final_offload_hook" ) and self.final_offload_hook is not None:
									self.final_offload_hook.offload()

					if not return_dict:
									return (images,)

					return ShapEPipelineOutput(images=lowercase )


