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

import datasets
from datasets import logging


_A     =  "\\n\n"

_A     =  "\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"

_A     =  "\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 			_lowerCAmelCase      (			datasets.Metric ):




				def   __a		(      self  )       ->   Optional[Any]:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = 16       ,       _UpperCamelCase = True       ,       _UpperCamelCase=None  )       ->   Optional[int]:
							if device is not None:
										assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
										if device == "gpu":
													lowerCAmelCase_      			=			"cuda"
							else:
										lowerCAmelCase_      			=			"cuda" if torch.cuda.is_available() else "cpu"

							lowerCAmelCase_      			=			AutoModelForCausalLM.from_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			model.to(_UpperCamelCase  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )

							# 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:
										lowerCAmelCase_      			=			list(tokenizer.special_tokens_map_extended.values()  )
										# check that the model already has at least one special token defined
										assert (
										    len(_UpperCamelCase  ) > 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"
										lowerCAmelCase_      			=			model.config.max_length - 1
							else:
										lowerCAmelCase_      			=			model.config.max_length

							lowerCAmelCase_      			=			tokenizer(
							    _UpperCamelCase       ,       add_special_tokens=_UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       return_tensors="pt"       ,       return_attention_mask=_UpperCamelCase       ,       ).to(_UpperCamelCase  )

							lowerCAmelCase_      			=			encodings["input_ids"]
							lowerCAmelCase_      			=			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."

							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			CrossEntropyLoss(reduction="none"  )

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

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

										lowerCAmelCase_      			=			encoded_batch

										with torch.no_grad():
													lowerCAmelCase_      			=			model(_UpperCamelCase       ,       attention_mask=_UpperCamelCase  ).logits

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

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

										ppls += perplexity_batch.tolist()

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





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			_lowerCAmelCase      (			__a      ,   __a ):




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 128       ,       _UpperCamelCase = 256       ,       _UpperCamelCase = 2000.0       ,       _UpperCamelCase = 768       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 64       ,       _UpperCamelCase = 2_048       ,       _UpperCamelCase = 0.1       ,       )       ->   str:
							super().__init__()

							lowerCAmelCase_      			=			nn.Sequential(
							    nn.Linear(_UpperCamelCase       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       nn.Linear(d_model * 4       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       )

							lowerCAmelCase_      			=			nn.Embedding(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			False

							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.ModuleList()
							for lyr_num in range(_UpperCamelCase  ):
										# FiLM conditional T5 decoder
										lowerCAmelCase_      			=			DecoderLayer(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
										self.decoders.append(_UpperCamelCase  )

							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			torch.mul(query_input.unsqueeze(-1  )       ,       key_input.unsqueeze(-2  )  )
							return mask.unsqueeze(-3  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Any:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							lowerCAmelCase_      			=			get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time       ,       embedding_dim=self.config.d_model       ,       max_period=self.config.max_decoder_noise_time       ,       ).to(dtype=self.dtype  )

							lowerCAmelCase_      			=			self.conditioning_emb(_UpperCamelCase  ).unsqueeze(1  )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							lowerCAmelCase_      			=			decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							lowerCAmelCase_      			=			torch.broadcast_to(
							    torch.arange(_UpperCamelCase       ,       device=decoder_input_tokens.device  )       ,       (batch, seq_length)       ,       )

							lowerCAmelCase_      			=			self.position_encoding(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.continuous_inputs_projection(_UpperCamelCase  )
							inputs += position_encodings
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							# decoder: No padding present.
							lowerCAmelCase_      			=			torch.ones(
							    decoder_input_tokens.shape[:2]       ,       device=decoder_input_tokens.device       ,       dtype=inputs.dtype  )

							# Translate encoding masks to encoder-decoder masks.
							lowerCAmelCase_      			=			[(x, self.encoder_decoder_mask(_UpperCamelCase       ,       _UpperCamelCase  )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							lowerCAmelCase_      			=			torch.cat([x[0] for x in encodings_and_encdec_masks]       ,       dim=1  )
							lowerCAmelCase_      			=			torch.cat([x[1] for x in encodings_and_encdec_masks]       ,       dim=-1  )

							for lyr in self.decoders:
										lowerCAmelCase_      			=			lyr(
										    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       encoder_attention_mask=_UpperCamelCase       ,       )[0]

							lowerCAmelCase_      			=			self.decoder_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.post_dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.spec_out(_UpperCamelCase  )
							return spec_out





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )  )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase       ,       )  )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase  )  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Any:
							lowerCAmelCase_      			=			self.layer[0](
							    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							if encoder_hidden_states is not None:
										lowerCAmelCase_      			=			torch.where(encoder_attention_mask > 0       ,       0       ,       -1e1_0  ).to(
										    encoder_hidden_states.dtype  )

										lowerCAmelCase_      			=			self.layer[1](
										    _UpperCamelCase       ,       key_value_states=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							# Apply Film Conditional Feed Forward layer
							lowerCAmelCase_      			=			self.layer[-1](_UpperCamelCase       ,       _UpperCamelCase  )

							return (hidden_states,)





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							super().__init__()
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Dict:
							# pre_self_attention_layer_norm
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )

							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.FiLMLayer(_UpperCamelCase       ,       _UpperCamelCase  )

							# Self-attention block
							lowerCAmelCase_      			=			self.attention(_UpperCamelCase  )

							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )

							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   int:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.attention(
							    _UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       attention_mask=attention_mask.squeeze(1  )       ,       )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return layer_output





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							super().__init__()
							lowerCAmelCase_      			=			TaDenseGatedActDense(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Tuple:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.film(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			self.DenseReluDense(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )
							lowerCAmelCase_      			=			NewGELUActivation()




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.act(self.wi_a(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			self.wi_a(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_gelu * hidden_linear
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.wo(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   int:
							super().__init__()
							lowerCAmelCase_      			=			nn.Parameter(torch.ones(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			eps




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							lowerCAmelCase_      			=			hidden_states.to(torch.floataa  ).pow(2  ).mean(-1       ,       keepdim=_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
										lowerCAmelCase_      			=			hidden_states.to(self.weight.dtype  )

							return self.weight * hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def   __a		(      self       ,       _UpperCamelCase  )       ->   torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044715 * torch.pow(_UpperCamelCase       ,       3.0  ))  ))





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       out_features * 2       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.scale_bias(_UpperCamelCase  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.chunk(_UpperCamelCase       ,       2       ,       -1  )
							lowerCAmelCase_      			=			x * (1 + scale) + shift
							return x



