

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Any	)			->     Dict:

					"""simple docstring"""

					_a	:    List[str]         =		SwinConfig(
					    embed_dim=192		,depths=(2, 2, 18, 2)		,num_heads=(6, 12, 24, 48)		,window_size=12		,out_features=['''stage2''', '''stage3''', '''stage4''']		,)

					_a	:    Union[str, Any]         =		DetaConfig(
					    backbone_config=__a		,num_queries=900		,encoder_ffn_dim=2_048		,decoder_ffn_dim=2_048		,num_feature_levels=5		,assign_first_stage=__a		,with_box_refine=__a		,two_stage=__a		,)

					# set labels
					_a	:    List[Any]         =		'''huggingface/label-files'''
					if "o365" in model_name:
										_a	:    Any         =		366
										_a	:    Optional[int]         =		'''object365-id2label.json'''
					else:
										_a	:    Any         =		91
										_a	:    List[Any]         =		'''coco-detection-id2label.json'''

					_a	:    List[str]         =		num_labels
					_a	:    List[Any]         =		json.load(open(cached_download(hf_hub_url(__a		,__a		,repo_type='''dataset'''	)	)		,'''r'''	)	)
					_a	:    Tuple         =		{int(__a	): v for k, v in idalabel.items()}
					_a	:    Optional[Any]         =		idalabel
					_a	:    List[Any]         =		{v: k for k, v in idalabel.items()}

					return config



def    __UpperCAmelCase (	__a      :			List[str]	)			->     Any:

					"""simple docstring"""

					_a	:    Tuple         =		[]

					# stem
					# fmt: off
					rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')	)
					rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')	)
					rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')	)
					rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')	)
					# stages
					for i in range(len(config.backbone_config.depths	)	):
										for j in range(config.backbone_config.depths[i]	):
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")	)

										if i < 3:
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.reduction.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")	)
															rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.bias""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")	)

					rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')	)
					rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')	)
					rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')	)
					rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')	)
					rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')	)
					rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')	)

					# transformer encoder
					for i in range(config.encoder_layers	):
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")	)
										rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")	)

					# transformer decoder
					for i in range(config.decoder_layers	):
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")	)
										rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")	)

					# fmt: on

					return rename_keys



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			Dict		,__a      :			Optional[int]	)			->     Dict:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		dct.pop(__a	)
					_a	:    Optional[Any]         =		val



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			List[str]	)			->     Dict:

					"""simple docstring"""

					_a	:    Optional[int]         =		[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
					for i in range(len(backbone_config.depths	)	):
										_a	:    str         =		num_features[i]
										for j in range(backbone_config.depths[i]	):
															# fmt: off
															# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
															_a	:    str         =		state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
															_a	:    Tuple         =		state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
															# next, add query, keys and values (in that order) to the state dict
															_a	:    Dict         =		in_proj_weight[:dim, :]
															_a	:    Optional[Any]         =		in_proj_bias[: dim]
															_a	:    int         =		in_proj_weight[
															    dim : dim * 2, :
															]
															_a	:    Any         =		in_proj_bias[
															    dim : dim * 2
															]
															_a	:    str         =		in_proj_weight[
															    -dim :, :
															]
															_a	:    Tuple         =		in_proj_bias[-dim :]
															# fmt: on



def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Any	)			->     str:

					"""simple docstring"""

					_a	:    Tuple         =		config.d_model
					for i in range(config.decoder_layers	):
										# read in weights + bias of input projection layer of self-attention
										_a	:    Tuple         =		state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
										_a	:    Dict         =		state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""	)
										# next, add query, keys and values (in that order) to the state dict
										_a	:    int         =		in_proj_weight[:hidden_size, :]
										_a	:    Tuple         =		in_proj_bias[:hidden_size]
										_a	:    Dict         =		in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										_a	:    List[str]         =		in_proj_bias[hidden_size : hidden_size * 2]
										_a	:    int         =		in_proj_weight[-hidden_size:, :]
										_a	:    Dict         =		in_proj_bias[-hidden_size:]



def    __UpperCAmelCase (	)			->     Any:

					"""simple docstring"""

					_a	:    Optional[int]         =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a	:    List[str]         =		Image.open(requests.get(__a		,stream=__a	).raw	)

					return im



@torch.no_grad()
def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			int		,__a      :			Optional[int]	)			->     Union[str, Any]:

					"""simple docstring"""

					_a	:    List[Any]         =		get_deta_config(__a	)

					# load original state dict
					if model_name == "deta-swin-large":
										_a	:    List[Any]         =		hf_hub_download(repo_id='''nielsr/deta-checkpoints'''		,filename='''adet_swin_ft.pth'''	)
					elif model_name == "deta-swin-large-o365":
										_a	:    List[Any]         =		hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''		,filename='''deta_swin_pt_o365.pth'''	)
					else:
										raise ValueError(F"""Model name {model_name} not supported"""	)

					_a	:    List[str]         =		torch.load(__a		,map_location='''cpu'''	)['''model''']

					# original state dict
					for name, param in state_dict.items():
										print(__a		,param.shape	)

					# rename keys
					_a	:    Tuple         =		create_rename_keys(__a	)
					for src, dest in rename_keys:
										rename_key(__a		,__a		,__a	)
					read_in_swin_q_k_v(__a		,config.backbone_config	)
					read_in_decoder_q_k_v(__a		,__a	)

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															_a	:    Optional[int]         =		state_dict.pop(__a	)
															_a	:    str         =		val
										if "input_proj" in key:
															_a	:    Union[str, Any]         =		state_dict.pop(__a	)
															_a	:    Optional[int]         =		val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															_a	:    List[str]         =		state_dict.pop(__a	)
															_a	:    Optional[Any]         =		val

    # finally, create HuggingFace model and load state dict
					_a	:    Union[str, Any]         =		DetaForObjectDetection(__a	)
					model.load_state_dict(__a	)
					model.eval()

					_a	:    List[Any]         =		'''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(__a	)

					# load image processor
					_a	:    int         =		DetaImageProcessor(format='''coco_detection'''	)

					# verify our conversion on image
					_a	:    Tuple         =		prepare_img()
					_a	:    str         =		processor(images=__a		,return_tensors='''pt'''	)
					_a	:    Dict         =		encoding['''pixel_values''']
					_a	:    List[str]         =		model(pixel_values.to(__a	)	)

					# verify logits
					print('''Logits:'''		,outputs.logits[0, :3, :3]	)
					print('''Boxes:'''		,outputs.pred_boxes[0, :3, :3]	)
					if model_name == "deta-swin-large":
										_a	:    Union[str, Any]         =		torch.tensor(
										    [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]	)
										_a	:    Tuple         =		torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]	)
					elif model_name == "deta-swin-large-o365":
										_a	:    Dict         =		torch.tensor(
										    [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]	)
										_a	:    Any         =		torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]	)
					assert torch.allclose(outputs.logits[0, :3, :3]		,expected_logits.to(__a	)		,atol=1E-4	)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,expected_boxes.to(__a	)		,atol=1E-4	)
					print('''Everything ok!'''	)

					if pytorch_dump_folder_path:
										# Save model and processor
										logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""	)
										Path(__a	).mkdir(exist_ok=__a	)
										model.save_pretrained(__a	)
										processor.save_pretrained(__a	)

					# Push to hub
					if push_to_hub:
										print('''Pushing model and processor to hub...'''	)
										model.push_to_hub(F"""jozhang97/{model_name}"""	)
										processor.push_to_hub(F"""jozhang97/{model_name}"""	)


if __name__ == "__main__":
			a__				=		argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name''',
			    type=str,
			    default='''deta-swin-large''',
			    choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
			    help='''Name of the model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    help='''Path to the folder to output PyTorch model.''',
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)
			a__				=		parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


a__				=		'''\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
    author = "Lin, Chin-Yew  and
      Och, Franz Josef",
    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
    month = "aug 23{--}aug 27",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/C04-1072",
    pages = "501--507",
}
'''

a__				=		'''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

a__				=		'''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,       _KWARGS_DESCRIPTION   )
class 				UpperCAmelCase_		(			datasets.Metric   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    Optional[int]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
												        {
												            '''predictions''': datasets.Sequence(datasets.Value('''string'''       ,							id='''token'''       )       ,							id='''sequence'''       ),
												            '''references''': datasets.Sequence(
												                datasets.Sequence(datasets.Value('''string'''       ,							id='''token'''       )       ,							id='''sequence'''       )       ,							id='''references'''       ),
												        }       )       ,							codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']       ,							reference_urls=[
												        '''https://en.wikipedia.org/wiki/BLEU''',
												        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
												    ]       ,							)




							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a=4       ,							_a=False       )    ->    Optional[Any]:
												_a	:    List[Any]         =		compute_bleu(
												    reference_corpus=_a       ,							translation_corpus=_a       ,							max_order=_a       ,							smooth=_a       )
												((_a)      ,					(_a)      ,					(_a)      ,					(_a)      ,					(_a)      ,					(_a))	:    Dict         =		score
												return {
												    "bleu": bleu,
												    "precisions": precisions,
												    "brevity_penalty": bp,
												    "length_ratio": ratio,
												    "translation_length": translation_length,
												    "reference_length": reference_length,
												}



