





from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







						@register_to_config
						def __init__(    self			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Optional[int] = None ,				lowerCamelCase_			:		int = 5_02_57 ,				lowerCamelCase_			:		int = 10_24 ,				lowerCamelCase_			:		int = 7_68 ,				lowerCamelCase_			:		int = 12 ,				lowerCamelCase_			:		int = 12 ,				lowerCamelCase_			:		Optional[int] = None ,				lowerCamelCase_			:		str = "gelu_new" ,				lowerCamelCase_			:		float = 0.1 ,				lowerCamelCase_			:		float = 0.1 ,				lowerCamelCase_			:		float = 0.1 ,				lowerCamelCase_			:		float = 1e-5 ,				lowerCamelCase_			:		float = 0.02 ,				lowerCamelCase_			:		bool = True ,				lowerCamelCase_			:		bool = True ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Dict  						=   prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
														raise ValueError(
														    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
														    f''' `n_embd`: {n_embd} are not equal.'''					)

										_snake_case :			int  						=   prefix_inner_dim
										_snake_case :			List[Any]  						=   prefix_hidden_dim

										_snake_case :			Optional[Any]  						=   (
										    nn.Linear(self.prefix_inner_dim ,				self.prefix_hidden_dim					)
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										_snake_case :			List[str]  						=   (
										    nn.Linear(self.prefix_hidden_dim ,				__A					) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										_snake_case :			str  						=   GPTaConfig(
										    vocab_size=__A ,				n_positions=__A ,				n_embd=__A ,				n_layer=__A ,				n_head=__A ,				n_inner=__A ,				activation_function=__A ,				resid_pdrop=__A ,				embd_pdrop=__A ,				attn_pdrop=__A ,				layer_norm_epsilon=__A ,				initializer_range=__A ,				scale_attn_weights=__A ,				use_cache=__A ,				scale_attn_by_inverse_layer_idx=__A ,				reorder_and_upcast_attn=__A ,				)
										_snake_case :			Optional[int]  						=   GPTaLMHeadModel(__A					)







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		torch.Tensor ,				lowerCamelCase_			:		torch.Tensor ,				lowerCamelCase_			:		Optional[torch.Tensor] = None ,				lowerCamelCase_			:		Optional[torch.Tensor] = None ,				):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.transformer.transformer.wte(__A					)
										_snake_case :			Optional[int]  						=   self.encode_prefix(__A					)
										_snake_case :			Tuple  						=   self.decode_prefix(__A					)
										_snake_case :			str  						=   torch.cat((prefix_embeds, embedding_text) ,				dim=1					)

										if labels is not None:
														_snake_case :			Any  						=   self.get_dummy_token(input_ids.shape[0] ,				input_ids.device					)
														_snake_case :			Optional[int]  						=   torch.cat((dummy_token, input_ids) ,				dim=1					)
										_snake_case :			Dict  						=   self.transformer(inputs_embeds=__A ,				labels=__A ,				attention_mask=__A					)
										if self.prefix_hidden_dim is not None:
														return out, hidden
										else:
														return out







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		torch.device					):





										'''simple docstring'''







										return torch.zeros(__A ,				self.prefix_length ,				dtype=torch.intaa ,				device=__A					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										return self.encode_prefix(__A					)







						@torch.no_grad()
						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										_snake_case :			int  						=   torch.split(__A ,				1 ,				dim=0					)
										_snake_case :			int  						=   []
										_snake_case :			Union[str, Any]  						=   []
										for feature in features:
														_snake_case :			Any  						=   self.decode_prefix(feature.to(__A					)					)  # back to the clip feature
														# Only support beam search for now
														_snake_case						,			_snake_case :			Dict  						=   self.generate_beam(
														    input_embeds=__A ,				device=__A ,				eos_token_id=__A					)
														generated_tokens.append(output_tokens[0]					)
														generated_seq_lengths.append(seq_lengths[0]					)
										_snake_case :			Any  						=   torch.stack(__A					)
										_snake_case :			Any  						=   torch.stack(__A					)
										return generated_tokens, generated_seq_lengths







						@torch.no_grad()
						def 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		str=None ,				lowerCamelCase_			:		Any=None ,				lowerCamelCase_			:		Dict=None ,				lowerCamelCase_			:		int = 5 ,				lowerCamelCase_			:		int = 67 ,				lowerCamelCase_			:		float = 1.0 ,				lowerCamelCase_			:		Optional[int] = None ,				):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   eos_token_id
										_snake_case :			Dict  						=   None
										_snake_case :			List[Any]  						=   None
										_snake_case :			Any  						=   torch.ones(__A ,				device=__A ,				dtype=torch.int					)
										_snake_case :			Optional[Any]  						=   torch.zeros(__A ,				device=__A ,				dtype=torch.bool					)

										if input_embeds is not None:
														_snake_case :			List[Any]  						=   input_embeds
										else:
														_snake_case :			Tuple  						=   self.transformer.transformer.wte(__A					)

										for i in range(__A					):
														_snake_case :			int  						=   self.transformer(inputs_embeds=__A					)
														_snake_case :			str  						=   outputs.logits
														_snake_case :			Any  						=   logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
														_snake_case :			Optional[int]  						=   logits.softmax(-1					).log()

														if scores is None:
																		_snake_case						,			_snake_case :			Dict  						=   logits.topk(__A ,				-1					)
																		_snake_case :			int  						=   generated.expand(__A ,				*generated.shape[1:]					)
																		_snake_case						,			_snake_case :			Optional[Any]  						=   next_tokens.permute(1 ,				0					), scores.squeeze(0					)
																		if tokens is None:
																						_snake_case :			str  						=   next_tokens
																		else:
																						_snake_case :			List[str]  						=   tokens.expand(__A ,				*tokens.shape[1:]					)
																						_snake_case :			Dict  						=   torch.cat((tokens, next_tokens) ,				dim=1					)
														else:
																		_snake_case :			Dict  						=   -float(np.inf					)
																		_snake_case :			List[Any]  						=   0
																		_snake_case :			Any  						=   scores[:, None] + logits
																		seq_lengths[~is_stopped] += 1
																		_snake_case :			str  						=   scores_sum / seq_lengths[:, None]
																		_snake_case						,			_snake_case :			int  						=   scores_sum_average.view(-1					).topk(__A ,				-1					)
																		_snake_case :			Dict  						=   next_tokens // scores_sum.shape[1]
																		_snake_case :			Optional[Any]  						=   seq_lengths[next_tokens_source]
																		_snake_case :			List[Any]  						=   next_tokens % scores_sum.shape[1]
																		_snake_case :			Union[str, Any]  						=   next_tokens.unsqueeze(1					)
																		_snake_case :			Optional[int]  						=   tokens[next_tokens_source]
																		_snake_case :			Dict  						=   torch.cat((tokens, next_tokens) ,				dim=1					)
																		_snake_case :			Any  						=   generated[next_tokens_source]
																		_snake_case :			Optional[Any]  						=   scores_sum_average * seq_lengths
																		_snake_case :			Any  						=   is_stopped[next_tokens_source]

														_snake_case :			Optional[int]  						=   self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0] ,				1 ,				-1					)
														_snake_case :			int  						=   torch.cat((generated, next_token_embed) ,				dim=1					)
														_snake_case :			Union[str, Any]  						=   is_stopped + next_tokens.eq(__A					).squeeze()
														if is_stopped.all():
																		break

										_snake_case :			Optional[Any]  						=   scores / seq_lengths
										_snake_case :			Tuple  						=   scores.argsort(descending=__A					)
										# tokens tensors are already padded to max_seq_length
										_snake_case :			Optional[Any]  						=   [tokens[i] for i in order]
										_snake_case :			Optional[int]  						=   torch.stack(__A ,				dim=0					)
										_snake_case :			Dict  						=   torch.tensor([seq_lengths[i] for i in order] ,				dtype=seq_lengths.dtype					)
										return output_texts, seq_lengths













import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


lowercase_		:				List[str]				=						'''\
@inproceedings{snover-etal-2006-study,
    title = "A Study of Translation Edit Rate with Targeted Human Annotation",
    author = "Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John",
    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
    month = aug # " 8-12",
    year = "2006",
    address = "Cambridge, Massachusetts, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2006.amta-papers.25",
    pages = "223--231",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowercase_		:				Optional[int]				=						'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

lowercase_		:				Any				=						'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''


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



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										if version.parse(scb.__version__					) < version.parse('1.4.12'					):
														raise ImportWarning(
														    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
														    'You can install it with `pip install "sacrebleu>=1.4.12"`.'					)
										return datasets.MetricInfo(
										    description=_DESCRIPTION ,				citation=_CITATION ,				homepage='http://www.cs.umd.edu/~snover/tercom/' ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
										        {
										            'predictions': datasets.Value('string' ,				id='sequence'					),
										            'references': datasets.Sequence(datasets.Value('string' ,				id='sequence'					) ,				id='references'					),
										        }					) ,				codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] ,				reference_urls=[
										        'https://github.com/jhclark/tercom',
										    ] ,				)







						def 		__UpperCAmelCase       (    self			:		Dict ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										_snake_case :			str  						=   len(references[0]					)
										if any(len(lowerCamelCase_					) != references_per_prediction for refs in references					):
														raise ValueError('Sacrebleu requires the same number of references for each prediction'					)
										_snake_case :			int  						=   [[refs[i] for refs in references] for i in range(lowerCamelCase_					)]

										_snake_case :			Optional[int]  						=   TER(
										    normalized=lowerCamelCase_ ,				no_punct=lowerCamelCase_ ,				asian_support=lowerCamelCase_ ,				case_sensitive=lowerCamelCase_ ,				)
										_snake_case :			Optional[Any]  						=   sb_ter.corpus_score(lowerCamelCase_ ,				lowerCamelCase_					)

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






