

'''simple docstring'''


from jiwer import compute_measures

import datasets


lowerCAmelCase__										= "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowerCAmelCase__										= "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowerCAmelCase__										= "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"



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


							def 						_snake_case							(			self)							->				Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION    ,			citation=_CITATION    ,			inputs_description=_KWARGS_DESCRIPTION    ,			features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'    ,			id='sequence'),
								            'references': datasets.Value('string'    ,			id='sequence'),
								        })    ,			codebase_urls=['https://github.com/jitsi/jiwer/']    ,			reference_urls=[
								        'https://en.wikipedia.org/wiki/Word_error_rate',
								    ]    ,			)





							def 						_snake_case							(			self    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=False)							->				Union[str, Any]:
								if concatenate_texts:
									return compute_measures(_snake_case    ,			_snake_case)["wer"]
								else:
									UpperCAmelCase_		:      List[Any]       	=					0
									UpperCAmelCase_		:      Any       	=					0
									for prediction, reference in zip(_snake_case    ,			_snake_case):
										UpperCAmelCase_		:      Any       	=					compute_measures(_snake_case    ,			_snake_case)
										incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
										total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total









'''simple docstring'''


from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase__										= logging.get_logger(__name__)

# General docstring
lowerCAmelCase__										= "RegNetConfig"

# Base docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= [1, 1088, 7, 7]

# Image classification docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= "tabby, tabby cat"

lowerCAmelCase__										= [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 3    ,			_snake_case = 1    ,			_snake_case = 1    ,			_snake_case = "relu"    ,			)							->				int:
								super().__init__()
								UpperCAmelCase_		:      str       	=					nn.Convad(
								    _snake_case    ,			_snake_case    ,			kernel_size=_snake_case    ,			stride=_snake_case    ,			padding=kernel_size // 2    ,			groups=_snake_case    ,			bias=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					nn.BatchNormad(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					ACTaFN[activation] if activation is not None else nn.Identity()





							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      int       	=					self.normalization(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      Tuple       	=					RegNetConvLayer(
								    config.num_channels    ,			config.embedding_size    ,			kernel_size=3    ,			stride=2    ,			activation=config.hidden_act)
								UpperCAmelCase_		:      Optional[Any]       	=					config.num_channels





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					pixel_values.shape[1]
								if num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
								UpperCAmelCase_		:      Any       	=					self.embedder(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2)							->				Optional[Any]:
								super().__init__()
								UpperCAmelCase_		:      Any       	=					nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			stride=_snake_case    ,			bias=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.BatchNormad(_snake_case)





							def 						_snake_case							(			self    ,			_snake_case)							->				Tensor:
								UpperCAmelCase_		:      Optional[Any]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.normalization(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Any:
								super().__init__()

								UpperCAmelCase_		:      Tuple       	=					nn.AdaptiveAvgPoolad((1, 1))
								UpperCAmelCase_		:      int       	=					nn.Sequential(
								    nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.ReLU()    ,			nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.Sigmoid()    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								# b c h w -> b c 1 1
								UpperCAmelCase_		:      Union[str, Any]       	=					self.pooler(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.attention(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					hidden_state * attention
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				str:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Any       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      str       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[int]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      int       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					hidden_state
								UpperCAmelCase_		:      List[Any]       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				int:
								super().__init__()
								UpperCAmelCase_		:      List[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Optional[Any]       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      Optional[int]       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetSELayer(_snake_case    ,			reduced_channels=int(round(in_channels / 4)))    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      Any       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					hidden_state
								UpperCAmelCase_		:      int       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2    ,			_snake_case = 2    ,			)							->				Optional[int]:
								super().__init__()

								UpperCAmelCase_		:      str       	=					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

								UpperCAmelCase_		:      Dict       	=					nn.Sequential(
								    # downsampling is done in the first layer with stride of 2
								    layer(
								        _snake_case    ,			_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			)    ,			*[layer(_snake_case    ,			_snake_case    ,			_snake_case) for _ in range(depth - 1)]    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					self.layers(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      List[str]       	=					nn.ModuleList([])
								# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    RegNetStage(
								        _snake_case    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			))
								UpperCAmelCase_		:      Dict       	=					zip(config.hidden_sizes    ,			config.hidden_sizes[1:])
								for (in_channels, out_channels), depth in zip(_snake_case    ,			config.depths[1:]):
									self.stages.append(RegNetStage(_snake_case    ,			_snake_case    ,			_snake_case    ,			depth=_snake_case))





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False    ,			_snake_case = True)							->				BaseModelOutputWithNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					() if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										UpperCAmelCase_		:      int       	=					hidden_states + (hidden_state,)

									UpperCAmelCase_		:      Tuple       	=					stage_module(_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:      Union[str, Any]       	=					hidden_states + (hidden_state,)

								if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

								return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case    ,			hidden_states=_snake_case)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   RegNetConfig
							_lowerCamelCase		:      Optional[int]=   "regnet"
							_lowerCamelCase		:      Union[str, Any]=   "pixel_values"
							_lowerCamelCase		:      List[str]=   True


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								if isinstance(_snake_case    ,			nn.Convad):
									nn.init.kaiming_normal_(module.weight    ,			mode='fan_out'    ,			nonlinearity='relu')
								elif isinstance(_snake_case    ,			(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight    ,			1)
									nn.init.constant_(module.bias    ,			0)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False)							->				List[Any]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      str       	=					value


lowerCAmelCase__										= r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowerCAmelCase__										= r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top.",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      str       	=					config
								UpperCAmelCase_		:      Optional[int]       	=					RegNetEmbeddings(_snake_case)
								UpperCAmelCase_		:      int       	=					RegNetEncoder(_snake_case)
								UpperCAmelCase_		:      Any       	=					nn.AdaptiveAvgPoolad((1, 1))
								# Initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			modality='vision'    ,			expected_output=_EXPECTED_OUTPUT_SHAPE    ,			)
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = None)							->				BaseModelOutputWithPoolingAndNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:      Optional[Any]       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      int       	=					self.embedder(_snake_case)

								UpperCAmelCase_		:      str       	=					self.encoder(
								    _snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					encoder_outputs[0]

								UpperCAmelCase_		:      str       	=					self.pooler(_snake_case)

								if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case    ,			pooler_output=_snake_case    ,			hidden_states=encoder_outputs.hidden_states    ,			)



@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				Union[str, Any]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					config.num_labels
								UpperCAmelCase_		:      Optional[int]       	=					RegNetModel(_snake_case)
								# classification head
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    nn.Flatten()    ,			nn.Linear(config.hidden_sizes[-1]    ,			config.num_labels) if config.num_labels > 0 else nn.Identity()    ,			)
								# initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
							def 						_snake_case							(			self    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			)							->				ImageClassifierOutputWithNoAttention:
								UpperCAmelCase_		:      str       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      Optional[Any]       	=					self.regnet(_snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      Dict       	=					outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:      int       	=					self.classifier(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					None

								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											UpperCAmelCase_		:      Optional[int]       	=					'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											UpperCAmelCase_		:      Union[str, Any]       	=					'single_label_classification'
										else:
											UpperCAmelCase_		:      Tuple       	=					'multi_label_classification'
									if self.config.problem_type == "regression":
										UpperCAmelCase_		:      int       	=					MSELoss()
										if self.num_labels == 1:
											UpperCAmelCase_		:      Dict       	=					loss_fct(logits.squeeze()    ,			labels.squeeze())
										else:
											UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(_snake_case    ,			_snake_case)
									elif self.config.problem_type == "single_label_classification":
										UpperCAmelCase_		:      Optional[int]       	=					CrossEntropyLoss()
										UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(logits.view(-1    ,			self.num_labels)    ,			labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										UpperCAmelCase_		:      int       	=					BCEWithLogitsLoss()
										UpperCAmelCase_		:      Tuple       	=					loss_fct(_snake_case    ,			_snake_case)

								if not return_dict:
									UpperCAmelCase_		:      Union[str, Any]       	=					(logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=_snake_case    ,			logits=_snake_case    ,			hidden_states=outputs.hidden_states)






