
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


_UpperCAmelCase					: List[str]      =       "\\n\n"

_UpperCAmelCase					: str      =       "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_UpperCAmelCase					: Tuple      =       "\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,   _KWARGS_DESCRIPTION   )
class 				lowercase					(		datasets.Metric   ):



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return datasets.MetricInfo(
				    description=_DESCRIPTION				,      citation=_CITATION				,      inputs_description=_KWARGS_DESCRIPTION				,      features=datasets.Features(
				        {
				            'input_texts': datasets.Value('string'       ),
				        }       )				,      reference_urls=['https://huggingface.co/docs/transformers/perplexity']				,      )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_ = 16				,      A_ = True				,      A_=None       )  ->					List[str]:
				"""simple docstring"""
				if device is not None:
						assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
						if device == "gpu":
								UpperCamelCase		=     'cuda'
				else:
						UpperCamelCase		=     'cuda' if torch.cuda.is_available() else 'cpu'

				UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(A_       )
				UpperCamelCase		=     model.to(A_       )

				UpperCamelCase		=     AutoTokenizer.from_pretrained(A_       )

				# if batch_size > 1 (which generally leads to padding being required), and
				# if there is not an already assigned pad_token, assign an existing
				# special token to also be the padding token
				if tokenizer.pad_token is None and batch_size > 1:
						UpperCamelCase		=     list(tokenizer.special_tokens_map_extended.values()       )
						# check that the model already has at least one special token defined
						assert (
						    len(A_       ) > 0
						), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
						# assign one of the special tokens to also be the pad token
						tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]}       )

				if add_start_token:
						# leave room for <BOS> token to be added:
						assert (
						    tokenizer.bos_token is not None
						), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
						UpperCamelCase		=     model.config.max_length - 1
				else:
						UpperCamelCase		=     model.config.max_length

				UpperCamelCase		=     tokenizer(
				    A_				,      add_special_tokens=A_				,      padding=A_				,      truncation=A_				,      max_length=A_				,      return_tensors='pt'				,      return_attention_mask=A_				,      ).to(A_       )

				UpperCamelCase		=     encodings['input_ids']
				UpperCamelCase		=     encodings['attention_mask']

				# check that each input is long enough:
				if add_start_token:
						assert torch.all(torch.ge(attn_masks.sum(1       )				,      1       )       ), "Each input text must be at least one token long."
				else:
						assert torch.all(
						    torch.ge(attn_masks.sum(1       )				,      2       )       ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

				UpperCamelCase		=     []
				UpperCamelCase		=     CrossEntropyLoss(reduction='none'       )

				for start_index in logging.tqdm(range(0				,      len(A_       )				,      A_       )       ):
						UpperCamelCase		=     min(start_index + batch_size				,      len(A_       )       )
						UpperCamelCase		=     encoded_texts[start_index:end_index]
						UpperCamelCase		=     attn_masks[start_index:end_index]

						if add_start_token:
								UpperCamelCase		=     torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(A_       )
								UpperCamelCase		=     torch.cat([bos_tokens_tensor, encoded_batch]				,      dim=1       )
								UpperCamelCase		=     torch.cat(
								    [torch.ones(bos_tokens_tensor.size()				,      dtype=torch.intaa       ).to(A_       ), attn_mask]				,      dim=1       )

						UpperCamelCase		=     encoded_batch

						with torch.no_grad():
								UpperCamelCase		=     model(A_				,      attention_mask=A_       ).logits

						UpperCamelCase		=     out_logits[..., :-1, :].contiguous()
						UpperCamelCase		=     labels[..., 1:].contiguous()
						UpperCamelCase		=     attn_mask[..., 1:].contiguous()

						UpperCamelCase		=     torch.expa(
						    (loss_fct(shift_logits.transpose(1				,      2       )				,      A_       ) * shift_attention_mask_batch).sum(1       )
						    / shift_attention_mask_batch.sum(1       )       )

						ppls += perplexity_batch.tolist()

				return {"perplexities": ppls, "mean_perplexity": np.mean(A_       )}


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if type(A_       ) is dict:
						return {k: self.to_torch(A_       ) for k, v in x_in.items()}
				elif torch.is_tensor(A_       ):
						return x_in.to(self.unet.device       )
				return torch.tensor(A_				,      device=self.unet.device       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     torch.full((batch_size,)				,      A_				,      device=self.unet.device				,      dtype=torch.long       )
						for _ in range(A_       ):
								with torch.enable_grad():
										x.requires_grad_()

										# permute to match dimension for pre-trained models
										UpperCamelCase		=     self.value_function(x.permute(0				,      2				,      1       )				,      A_       ).sample
										UpperCamelCase		=     torch.autograd.grad([y.sum()]				,      [x]       )[0]

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

						# TODO: verify deprecation of this kwarg
						UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_				,      predict_epsilon=A_       )['prev_sample']

						# apply conditions to the trajectory (set the initial state)
						UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
						UpperCamelCase		=     self.to_torch(A_       )
				return x, y



		def __call__( self				,      A_				,      A_=64				,      A_=32				,      A_=2				,      A_=0.1       )  ->					List[str]:
				"""simple docstring"""
				# normalize the observations and create  batch dimension
				UpperCamelCase		=     self.normalize(A_				,      'observations'       )
				UpperCamelCase		=     obs[None].repeat(A_				,      axis=0       )

				UpperCamelCase		=     {0: self.to_torch(A_       )}
				UpperCamelCase		=     (batch_size, planning_horizon, self.state_dim + self.action_dim)

				# generate initial noise and apply our conditions (to make the trajectories start at current state)
				UpperCamelCase		=     randn_tensor(A_				,      device=self.unet.device       )
				UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
				UpperCamelCase		=     self.to_torch(A_       )

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

				# sort output trajectories by value
				UpperCamelCase		=     y.argsort(0				,      descending=A_       ).squeeze()
				UpperCamelCase		=     x[sorted_idx]
				UpperCamelCase		=     sorted_values[:, :, : self.action_dim]
				UpperCamelCase		=     actions.detach().cpu().numpy()
				UpperCamelCase		=     self.de_normalize(A_				,      key='actions'       )

				# select the action with the highest value
				if y is not None:
						UpperCamelCase		=     0
				else:
						# if we didn't run value guiding, select a random action
						UpperCamelCase		=     np.random.randint(0				,      A_       )

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
