




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec."   )     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""

				__lowerCAmelCase					=  []
				for i in range(config.num_hidden_layers   ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')   )
								rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')   )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')   )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')   )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')   )
								rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')   )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')   )
								rename_keys.append(
								    (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')   )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')   )
								rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')   )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
				        (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
				        (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
				    ]   )

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ("""mask_token""", F'''{hf_prefix}embeddings.mask_token'''),
								        (
								            """rel_pos_bias.relative_position_bias_table""",
								            F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
								        ),
								        (
								            """rel_pos_bias.relative_position_index""",
								            F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
								        ),
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								    ]   )
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
								        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
								        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
								        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
								    ]   )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""fc_norm.weight""", F'''{hf_prefix}pooler.layernorm.weight'''),
								        ("""fc_norm.bias""", F'''{hf_prefix}pooler.layernorm.bias'''),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]   )

				return rename_keys





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec_vision."   )     ->     Optional[Any]:







				'''simple docstring'''


				for i in range(config.num_hidden_layers   ):
								__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""
								# queries, keys and values
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

								__lowerCAmelCase					=  in_proj_weight[
								    : config.hidden_size, :
								]
								__lowerCAmelCase					=  q_bias
								__lowerCAmelCase					=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__lowerCAmelCase					=  in_proj_weight[
								    -config.hidden_size :, :
								]
								__lowerCAmelCase					=  v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''   )

								__lowerCAmelCase					=  gamma_a
								__lowerCAmelCase					=  gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''   )
												__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''   )

												__lowerCAmelCase					=  table
												__lowerCAmelCase					=  index





def      UpperCAmelCase  (						)     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  argparse.ArgumentParser(
				    """Convert Data2VecVision to HF for image classification and pretraining"""						,			add_help=UpperCamelCase__   )
				parser.add_argument("""--hf_checkpoint_name"""						,			type=UpperCamelCase__   )
				parser.add_argument("""--input_size"""						,			default=2_24						,			type=UpperCamelCase__						,			help="""images input size"""   )
				parser.add_argument("""--beit_checkpoint"""						,			default=""""""						,			help="""beit checkpoint"""   )

				return parser.parse_args()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


				def load_state_dict(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=""						,			UpperCamelCase__="relative_position_index"   ):
								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								# copy state_dict so _load_from_state_dict can modify it
								__lowerCAmelCase					=  getattr(UpperCamelCase__						,			"""_metadata"""						,			UpperCamelCase__   )
								__lowerCAmelCase					=  state_dict.copy()
								if metadata is not None:
												__lowerCAmelCase					=  metadata

								def load(UpperCamelCase__						,			UpperCamelCase__=""   ):
												__lowerCAmelCase					=  {} if metadata is None else metadata.get(prefix[:-1]						,			{}   )
												module._load_from_state_dict(
												    UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
												for name, child in module._modules.items():
																if child is not None:
																				load(UpperCamelCase__						,			prefix + name + """."""   )

								load(UpperCamelCase__						,			prefix=UpperCamelCase__   )

								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								for key in missing_keys:
												__lowerCAmelCase					=  True
												for ignore_key in ignore_missing.split("""|"""   ):
																if ignore_key in key:
																				__lowerCAmelCase					=  False
																				break
												if keep_flag:
																warn_missing_keys.append(UpperCamelCase__   )
												else:
																ignore_missing_keys.append(UpperCamelCase__   )

								__lowerCAmelCase					=  warn_missing_keys

								if len(UpperCamelCase__   ) > 0:
												print(
												    """Weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__						,			UpperCamelCase__   )   )
								if len(UpperCamelCase__   ) > 0:
												print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__						,			UpperCamelCase__   )   )
								if len(UpperCamelCase__   ) > 0:
												print(
												    """Ignored weights of {} not initialized from pretrained model: {}""".format(
												        model.__class__.__name__						,			UpperCamelCase__   )   )
								if len(UpperCamelCase__   ) > 0:
												print("""\n""".join(UpperCamelCase__   )   )

				__lowerCAmelCase					=  {
				    """pretrained""": False,
				    """use_shared_rel_pos_bias""": True,
				    """use_abs_pos_emb""": False,
				    """init_values""": 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        """num_classes""": 10_00,
								        """use_mean_pooling""": True,
								        """init_scale""": 0.001,
								        """use_rel_pos_bias""": True,
								    }   )

				__lowerCAmelCase					=  create_model(
				    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""						,			**UpperCamelCase__						,			)
				__lowerCAmelCase					=  model.patch_embed.patch_size
				__lowerCAmelCase					=  (args.input_size // patch_size[0], args.input_size // patch_size[1])
				__lowerCAmelCase					=  torch.load(args.beit_checkpoint						,			map_location="""cpu"""   )

				print(F'''Load ckpt from {args.beit_checkpoint}'''   )
				__lowerCAmelCase					=  None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												__lowerCAmelCase					=  checkpoint[model_key]
												print(F'''Load state_dict by model_key = {model_key}'''   )
												break

				__lowerCAmelCase					=  list(checkpoint_model.keys()   )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(UpperCamelCase__   )

								if "relative_position_bias_table" in key:
												__lowerCAmelCase					=  checkpoint_model[key]
												__lowerCAmelCase	,				__lowerCAmelCase					=  rel_pos_bias.size()
												__lowerCAmelCase	,				__lowerCAmelCase					=  model.state_dict()[key].size()
												__lowerCAmelCase					=  model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(UpperCamelCase__						,			UpperCamelCase__						,			prefix=""""""   )

				return model





def      UpperCAmelCase  (						)     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  get_args()

				__lowerCAmelCase					=  """ft1k""" in args.hf_checkpoint_name
				__lowerCAmelCase					=  """large""" in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				__lowerCAmelCase					=  DataaVecVisionConfig()
				if is_finetuned:
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  10_00

								__lowerCAmelCase					=  """huggingface/label-files"""
								__lowerCAmelCase					=  """imagenet-1k-id2label.json"""
								__lowerCAmelCase					=  json.load(open(hf_hub_download(UpperCamelCase__						,			UpperCamelCase__						,			repo_type="""dataset"""   )						,			"""r"""   )   )
								__lowerCAmelCase					=  {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
								__lowerCAmelCase					=  idalabel
								__lowerCAmelCase					=  {v: k for k, v in idalabel.items()}
				else:
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False

				if is_large:
								__lowerCAmelCase					=  10_24
								__lowerCAmelCase					=  40_96
								__lowerCAmelCase					=  24
								__lowerCAmelCase					=  16

				# 2. Load Beit model
				__lowerCAmelCase					=  load_beit_model(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				orig_model.eval()

				# 3. Forward Beit model
				__lowerCAmelCase					=  BeitImageProcessor(size=config.image_size						,			do_center_crop=UpperCamelCase__   )
				__lowerCAmelCase					=  Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				__lowerCAmelCase					=  image_processor(images=UpperCamelCase__						,			return_tensors="""pt"""   )
				__lowerCAmelCase					=  encoding["""pixel_values"""]

				__lowerCAmelCase					=  (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								__lowerCAmelCase					=  orig_model(*UpperCamelCase__   )

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								__lowerCAmelCase					=  DataaVecVisionForImageClassification(UpperCamelCase__   )
								hf_model.eval()
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  """data2vec_vision."""
				else:
								__lowerCAmelCase					=  DataaVecVisionModel(UpperCamelCase__   )
								hf_model.eval()
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  """"""

				__lowerCAmelCase					=  create_rename_keys(UpperCamelCase__						,			hf_prefix=UpperCamelCase__						,			has_lm_head=UpperCamelCase__   )
				__lowerCAmelCase					=  orig_model.state_dict()
				for src, dest in rename_keys:
								__lowerCAmelCase					=  state_dict.pop(UpperCamelCase__   )
								__lowerCAmelCase					=  val

				read_in_q_k_v(UpperCamelCase__						,			UpperCamelCase__						,			hf_prefix=UpperCamelCase__						,			has_lm_head=UpperCamelCase__   )
				__lowerCAmelCase	,				__lowerCAmelCase					=  hf_model.load_state_dict(UpperCamelCase__						,			strict=UpperCamelCase__   )
				print("""HF missing"""						,			UpperCamelCase__   )
				print("""HF unexpected_keys"""						,			UpperCamelCase__   )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								__lowerCAmelCase					=  hf_model(UpperCamelCase__   )

				__lowerCAmelCase					=  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				__lowerCAmelCase					=  torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

				print(F'''max_absolute_diff = {max_absolute_diff}'''   )
				__lowerCAmelCase					=  torch.allclose(UpperCamelCase__						,			UpperCamelCase__						,			atol=1e-3   )
				print("""Do both models output the same tensors?"""						,			"""🔥""" if success else """💩"""   )
				if not success:
								raise Exception("""Something went wRoNg"""   )

				# 7. Save
				print(F'''Saving to {args.hf_checkpoint_name}'''   )
				hf_model.save_pretrained(args.hf_checkpoint_name   )
				image_processor.save_pretrained(args.hf_checkpoint_name   )


if __name__ == "__main__":
	main()
	# Run the following to convert checkpoints
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_base.pt \
	#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./pretrained_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large"
	#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
	#          --beit_checkpoint ./finetuned_large.pt \
	#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"









"""simple docstring"""
from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
__SCREAMING_SNAKE_CASE				:     str		=     '\\n'

__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     '\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

__SCREAMING_SNAKE_CASE				:     Any		=     '\nCompute XTREME-S evaluation metric associated to each XTREME-S dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

__SCREAMING_SNAKE_CASE				:     List[str]		=     ['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
__SCREAMING_SNAKE_CASE				:     List[str]		=     ''

try:
				from jiwer import transforms as tr

				__SCREAMING_SNAKE_CASE				:     Dict		=     True
except ImportError:
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):





				class   __A  (tr.AbstractTransform):





											'''simple docstring'''

											def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   str = " "							)       ->str:



																		"""simple docstring"""



																		snake_case_     							=		sentence_delimiter



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   str							)       ->List[str]:



																		"""simple docstring"""



																		return list(UpperCAmelCase_							)



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   List[str]							)       ->Any:



																		"""simple docstring"""



																		snake_case_     							=		[]
																		for sent_idx, sentence in enumerate(UpperCAmelCase_							):
																									chars.extend(self.process_string(UpperCAmelCase_							)							)
																									if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCAmelCase_							) - 1:
																																chars.append(self.sentence_delimiter							)
																		return chars


				__SCREAMING_SNAKE_CASE				:     str		=     tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				__SCREAMING_SNAKE_CASE				:     Any		=     None







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							return float((preds == labels).mean()					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							return {
							    "f1": float(fa_score(y_true=_SCREAMING_SNAKE_CASE					,			y_pred=_SCREAMING_SNAKE_CASE					,			average="""macro"""					)					),
							    "accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					),
							}







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="exp"					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			)     ->					Optional[Any]:
							# xtreme-s can only have one label
							snake_case_     							=		[[label] for label in labels]
							snake_case_     							=		list(_SCREAMING_SNAKE_CASE					)
							try:
														import sacrebleu as scb
							except ImportError:
														raise ValueError(
														    """sacrebleu has to be installed in order to apply the bleu metric for covost2."""
														    """You can install it via `pip install sacrebleu`."""					)

							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\"`."""					)

							snake_case_     							=		len(labels[0]					)
							if any(len(_SCREAMING_SNAKE_CASE					) != references_per_prediction for refs in labels					):
														raise ValueError("""Sacrebleu requires the same number of references for each prediction"""					)
							snake_case_     							=		[[refs[i] for refs in labels] for i in range(_SCREAMING_SNAKE_CASE					)]
							snake_case_     							=		scb.corpus_bleu(
							    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			smooth_method=_SCREAMING_SNAKE_CASE					,			smooth_value=_SCREAMING_SNAKE_CASE					,			force=_SCREAMING_SNAKE_CASE					,			lowercase=_SCREAMING_SNAKE_CASE					,			use_effective_order=_SCREAMING_SNAKE_CASE					,			**({"""tokenize""": tokenize} if tokenize else {})					,			)
							return {"bleu": output.score}







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							try:
														from jiwer import compute_measures
							except ImportError:
														raise ValueError(
														    f"""jiwer has to be installed in order to apply the wer metric for {config_name}."""
														    """You can install it via `pip install jiwer`."""					)

							if concatenate_texts:
														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)["""wer"""]

														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			truth_transform=_SCREAMING_SNAKE_CASE					,			hypothesis_transform=_SCREAMING_SNAKE_CASE					)["""wer"""]
														return {"wer": wer, "cer": cer}
							else:

														def compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="wer"					):
																					snake_case_     							=		0
																					snake_case_     							=		0
																					for prediction, reference in zip(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
																												if score_type == "wer":
																																			snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												elif score_type == "cer":
																																			snake_case_     							=		compute_measures(
																																			    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			truth_transform=_SCREAMING_SNAKE_CASE					,			hypothesis_transform=_SCREAMING_SNAKE_CASE					)
																												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																					return incorrect / total

														return {"wer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""wer"""					), "cer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""cer"""					)}





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





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   Any							)       ->List[str]:



														"""simple docstring"""



														if self.config_name not in _CONFIG_NAMES:
																					raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""							)

														snake_case_     							=		"""int64""" if self.config_name in ["""fleurs-lang_id""", """minds14"""] else """string"""

														return datasets.MetricInfo(
														    description=_DESCRIPTION		,							citation=_CITATION		,							inputs_description=_KWARGS_DESCRIPTION		,							features=datasets.Features(
														        {"""predictions""": datasets.Value(UpperCAmelCase_							), """references""": datasets.Value(UpperCAmelCase_							)}							)		,							codebase_urls=[]		,							reference_urls=[]		,							format="""numpy"""		,							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[str]=None		,							UpperCAmelCase_				:   List[Any]=None							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		bleu_kwargs if bleu_kwargs is not None else {}
														snake_case_     							=		wer_kwargs if wer_kwargs is not None else {}

														if self.config_name == "fleurs-lang_id":
																					return {"accuracy": simple_accuracy(UpperCAmelCase_		,							UpperCAmelCase_							)}
														elif self.config_name == "minds14":
																					return fa_and_simple_accuracy(UpperCAmelCase_		,							UpperCAmelCase_							)
														elif self.config_name == "covost2":
																					snake_case_     							=		bleu_kwargs.pop("""smooth_method"""		,							"""exp"""							)
																					snake_case_     							=		bleu_kwargs.pop("""smooth_value"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""force"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""lowercase"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""tokenize"""		,							UpperCAmelCase_							)
																					snake_case_     							=		bleu_kwargs.pop("""use_effective_order"""		,							UpperCAmelCase_							)
																					return bleu(
																					    preds=UpperCAmelCase_		,							labels=UpperCAmelCase_		,							smooth_method=UpperCAmelCase_		,							smooth_value=UpperCAmelCase_		,							force=UpperCAmelCase_		,							lowercase=UpperCAmelCase_		,							tokenize=UpperCAmelCase_		,							use_effective_order=UpperCAmelCase_		,							)
														elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
																					snake_case_     							=		wer_kwargs.pop("""concatenate_texts"""		,							UpperCAmelCase_							)
																					return wer_and_cer(UpperCAmelCase_		,							UpperCAmelCase_		,							UpperCAmelCase_		,							self.config_name							)
														else:
																					raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""							)



