
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__A				=logging.get_logger(__name__)

__A				=list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__A				=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class     _SCREAMING_SNAKE_CASE     :
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'Model type selected in the list: ' + ', '.join(snake_case_						)}						)
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}						)
	lowerCAmelCase__         = field(
	    default=1_28			,       metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=1_28			,       metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}			,       )
	lowerCAmelCase__         = field(
	    default=64			,       metadata={
	        'help': (
	            'The maximum number of tokens for the question. Questions longer than this will '
	            'be truncated to this length.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=30			,       metadata={
	        'help': (
	            'The maximum length of an answer that can be generated. This is needed because the start '
	            'and end predictions are not conditioned on one another.'
	        )
	    }			,       )
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'Overwrite the cached training and evaluation sets'}						)
	lowerCAmelCase__         = field(
	    default=snake_case_			,       metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}						)
	lowerCAmelCase__         = field(
	    default=0.0			,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
	lowerCAmelCase__         = field(
	    default=20			,       metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}						)
	lowerCAmelCase__         = field(
	    default=0			,       metadata={
	        'help': (
	            'language id of input for language-specific xlm models (see'
	            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
	        )
	    }			,       )
	lowerCAmelCase__         = field(default=1			,       metadata={'help': 'multiple threads for converting example to features'}						)




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'train'
	lowerCAmelCase__         = 'dev'




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42
	lowerCAmelCase__         = 42



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase = None						,  lowercase = Split.train						,  lowercase = False						,  lowercase = None						,  lowercase = "pt"						,  )				->       List[str]:
					lowerCamelCase_						=			args
					lowerCamelCase_						=			is_language_sensitive
					lowerCamelCase_						=			SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
					if isinstance(lowercase						,  lowercase ):
									try:
													lowerCamelCase_						=			Split[mode]
									except KeyError:
													raise KeyError("mode is not a valid split name" )
					lowerCamelCase_						=			mode
					# Load data features from cache or dataset file
					lowerCamelCase_						=			"v2" if args.version_2_with_negative else "v1"
					lowerCamelCase_						=			os.path.join(
					    cache_dir if cache_dir is not None else args.data_dir						,  f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}'						,  )

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					lowerCamelCase_						=			cached_features_file + ".lock"
					with FileLock(lowercase ):
									if os.path.exists(lowercase ) and not args.overwrite_cache:
													lowerCamelCase_						=			time.time()
													lowerCamelCase_						=			torch.load(lowercase )

													# Legacy cache files have only features, while new cache files
													# will have dataset and examples also.
													lowerCamelCase_						=			self.old_features["features"]
													lowerCamelCase_						=			self.old_features.get("dataset"						,  lowercase )
													lowerCamelCase_						=			self.old_features.get("examples"						,  lowercase )
													logger.info(
													    f'Loading features from cached file {cached_features_file} [took %.3f s]'						,  time.time() - start )

													if self.dataset is None or self.examples is None:
																	logger.warning(
																	    f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
																	    " future run" )
									else:
													if mode == Split.dev:
																	lowerCamelCase_						=			self.processor.get_dev_examples(args.data_dir )
													else:
																	lowerCamelCase_						=			self.processor.get_train_examples(args.data_dir )

													lowerCamelCase_	,    lowerCamelCase_						=			squad_convert_examples_to_features(
													    examples=self.examples						,  tokenizer=lowercase						,  max_seq_length=args.max_seq_length						,  doc_stride=args.doc_stride						,  max_query_length=args.max_query_length						,  is_training=mode == Split.train						,  threads=args.threads						,  return_dataset=lowercase						,  )

													lowerCamelCase_						=			time.time()
													torch.save(
													    {"features": self.features, "dataset": self.dataset, "examples": self.examples}						,  lowercase						,  )
													# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
													logger.info(
													    f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )



	def __len__(    self )				->       Tuple:
					return len(self.features )



	def __getitem__(    self						,  lowercase )				->       Dict[str, torch.Tensor]:
					# Convert to Tensors and build dataset
					lowerCamelCase_						=			self.features[i]

					lowerCamelCase_						=			torch.tensor(feature.input_ids						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.attention_mask						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.token_type_ids						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.cls_index						,  dtype=torch.long )
					lowerCamelCase_						=			torch.tensor(feature.p_mask						,  dtype=torch.float )
					lowerCamelCase_						=			torch.tensor(feature.is_impossible						,  dtype=torch.float )

					lowerCamelCase_						=			{
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					    "token_type_ids": token_type_ids,
					}

					if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
									del inputs["token_type_ids"]

					if self.args.model_type in ["xlnet", "xlm"]:
									inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
									if self.args.version_2_with_negative:
													inputs.update({"is_impossible": is_impossible} )
									if self.is_language_sensitive:
													inputs.update({"langs": (torch.ones(input_ids.shape						,  dtype=torch.intaa ) * self.args.lang_id)} )

					if self.mode == Split.train:
									lowerCamelCase_						=			torch.tensor(feature.start_position						,  dtype=torch.long )
									lowerCamelCase_						=			torch.tensor(feature.end_position						,  dtype=torch.long )
									inputs.update({"start_positions": start_positions, "end_positions": end_positions} )

					return inputs




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 )


