"""simple docstring"""
from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE__   :      Optional[Any]							='\\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'

SCREAMING_SNAKE_CASE__   :      str							='\\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'

SCREAMING_SNAKE_CASE__   :      Union[str, Any]							='\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      _UpperCAmelCase						( datasets.Metric						):






    """simple docstring"""







    def 				a__					(			self					)	->	List[str]:
        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 				a__					(			self    , _lowercase=None    , _lowercase=None    , _lowercase=False					)	->	str:
        if concatenate_texts:
            return compute_measures(_lowercase    , _lowercase					)["wer"]
        else:
            _lowerCamelCase	: Tuple       =       0
            _lowerCamelCase	: List[str]       =       0
            for prediction, reference in zip(_lowercase    , _lowercase					):
                _lowerCamelCase	: List[Any]       =       compute_measures(_lowercase    , _lowercase					)
                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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase__											=logging.get_logger(__name__)

# General docstring
lowercase__											='ResNetConfig'

# Base docstring
lowercase__											='microsoft/resnet-50'
lowercase__											=[1, 20_48, 7, 7]

# Image classification docstring
lowercase__											='microsoft/resnet-50'
lowercase__											='tiger cat'

lowercase__											=[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 3					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"	):
				super().__init__()
				a_      				=						nn.Convad(
				    UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=UpperCAmelCase					,   stride=UpperCAmelCase					,   padding=kernel_size // 2					,   bias=UpperCAmelCase	)
				a_      				=						nn.BatchNormad(UpperCAmelCase	)
				a_      				=						ACTaFN[activation] if activation is not None else nn.Identity()






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.convolution(UpperCAmelCase	)
				a_      				=						self.normalization(UpperCAmelCase	)
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__()
				a_      				=						ResNetConvLayer(
				    config.num_channels					,   config.embedding_size					,   kernel_size=7					,   stride=2					,   activation=config.hidden_act	)
				a_      				=						nn.MaxPoolad(kernel_size=3					,   stride=2					,   padding=1	)
				a_      				=						config.num_channels






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						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."""	)
				a_      				=						self.embedder(UpperCAmelCase	)
				a_      				=						self.pooler(UpperCAmelCase	)
				return embedding







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 2	):
				super().__init__()
				a_      				=						nn.Convad(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1					,   stride=UpperCAmelCase					,   bias=UpperCAmelCase	)
				a_      				=						nn.BatchNormad(UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						self.convolution(UpperCAmelCase	)
				a_      				=						self.normalization(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"	):
				super().__init__()
				a_      				=						in_channels != out_channels or stride != 1
				a_      				=						(
				    ResNetShortCut(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	) if should_apply_shortcut else nn.Identity()
				)
				a_      				=						nn.Sequential(
				    ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   activation=UpperCAmelCase	)					,   )
				a_      				=						ACTaFN[activation]






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						hidden_state
				a_      				=						self.layer(UpperCAmelCase	)
				a_      				=						self.shortcut(UpperCAmelCase	)
				hidden_state += residual
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 1					,   UpperCAmelCase = "relu"					,   UpperCAmelCase = 4	):
				super().__init__()
				a_      				=						in_channels != out_channels or stride != 1
				a_      				=						out_channels // reduction
				a_      				=						(
				    ResNetShortCut(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	) if should_apply_shortcut else nn.Identity()
				)
				a_      				=						nn.Sequential(
				    ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase	)					,   ResNetConvLayer(UpperCAmelCase					,   UpperCAmelCase					,   kernel_size=1					,   activation=UpperCAmelCase	)					,   )
				a_      				=						ACTaFN[activation]






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						hidden_state
				a_      				=						self.layer(UpperCAmelCase	)
				a_      				=						self.shortcut(UpperCAmelCase	)
				hidden_state += residual
				a_      				=						self.activation(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = 2					,   UpperCAmelCase = 2					,   ):
				super().__init__()

				a_      				=						ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

				a_      				=						nn.Sequential(
				    # downsampling is done in the first layer with stride of 2
				    layer(UpperCAmelCase					,   UpperCAmelCase					,   stride=UpperCAmelCase					,   activation=config.hidden_act	)					,   *[layer(UpperCAmelCase					,   UpperCAmelCase					,   activation=config.hidden_act	) for _ in range(depth - 1	)]					,   )






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				a_      				=						input
				for layer in self.layers:
					a_      				=						layer(UpperCAmelCase	)
				return hidden_state







class   a_ (      nn.Module					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__()
				a_      				=						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(
				    ResNetStage(
				        UpperCAmelCase					,   config.embedding_size					,   config.hidden_sizes[0]					,   stride=2 if config.downsample_in_first_stage else 1					,   depth=config.depths[0]					,   )	)
				a_      				=						zip(config.hidden_sizes					,   config.hidden_sizes[1:]	)
				for (in_channels, out_channels), depth in zip(UpperCAmelCase					,   config.depths[1:]	):
					self.stages.append(ResNetStage(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   depth=UpperCAmelCase	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = False					,   UpperCAmelCase = True	):
				a_      				=						() if output_hidden_states else None

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

					a_      				=						stage_module(UpperCAmelCase	)

				if output_hidden_states:
					a_      				=						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=UpperCAmelCase					,   hidden_states=UpperCAmelCase					,   )







class   a_ (      UpperCamelCase__					):
			lowerCamelCase__	:			List[Any]       =    ResNetConfig
			lowerCamelCase__	:			Dict       =    'resnet'
			lowerCamelCase__	:			Dict       =    'pixel_values'
			lowerCamelCase__	:			str       =    True




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if isinstance(UpperCAmelCase					,   nn.Convad	):
					nn.init.kaiming_normal_(module.weight					,   mode="""fan_out"""					,   nonlinearity="""relu"""	)
				elif isinstance(UpperCAmelCase					,   (nn.BatchNormad, nn.GroupNorm)	):
					nn.init.constant_(module.weight					,   1	)
					nn.init.constant_(module.bias					,   0	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=False	):
				if isinstance(UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						value







lowercase__											=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 ([`ResNetConfig`]): 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'

lowercase__											=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 [`~utils.ModelOutput`] instead of a plain tuple.\n'







@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				a_      				=						config
				a_      				=						ResNetEmbeddings(UpperCAmelCase	)
				a_      				=						ResNetEncoder(UpperCAmelCase	)
				a_      				=						nn.AdaptiveAvgPoolad((1, 1)	)
				# Initialize weights and apply final processing
				self.post_init()






			@add_start_docstrings_to_model_forward(UpperCAmelCase	)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC					,   output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC					,   modality="""vision"""					,   expected_output=_EXPECTED_OUTPUT_SHAPE					,   )
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None	):
				a_      				=						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict

				a_      				=						self.embedder(UpperCAmelCase	)

				a_      				=						self.encoder(
				    UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

				a_      				=						encoder_outputs[0]

				a_      				=						self.pooler(UpperCAmelCase	)

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

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







@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				a_      				=						config.num_labels
				a_      				=						ResNetModel(UpperCAmelCase	)
				# classification head
				a_      				=						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(UpperCAmelCase	)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT					,   output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC					,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,   )
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   ):
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict

				a_      				=						self.resnet(UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

				a_      				=						outputs.pooler_output if return_dict else outputs[1]

				a_      				=						self.classifier(UpperCAmelCase	)

				a_      				=						None

				if labels is not None:
					if self.config.problem_type is None:
						if self.num_labels == 1:
							a_      				=						"""regression"""
						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
							a_      				=						"""single_label_classification"""
						else:
							a_      				=						"""multi_label_classification"""
					if self.config.problem_type == "regression":
						a_      				=						MSELoss()
						if self.num_labels == 1:
							a_      				=						loss_fct(logits.squeeze()					,   labels.squeeze()	)
						else:
							a_      				=						loss_fct(UpperCAmelCase					,   UpperCAmelCase	)
					elif self.config.problem_type == "single_label_classification":
						a_      				=						CrossEntropyLoss()
						a_      				=						loss_fct(logits.view(-1					,   self.num_labels	)					,   labels.view(-1	)	)
					elif self.config.problem_type == "multi_label_classification":
						a_      				=						BCEWithLogitsLoss()
						a_      				=						loss_fct(UpperCAmelCase					,   UpperCAmelCase	)

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

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







@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '		,  UpperCamelCase__		,  )
class   a_ (      UpperCamelCase__		,  UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase	):
				super().__init__(UpperCAmelCase	)
				super()._init_backbone(UpperCAmelCase	)

				a_      				=						[config.embedding_size] + config.hidden_sizes
				a_      				=						ResNetEmbeddings(UpperCAmelCase	)
				a_      				=						ResNetEncoder(UpperCAmelCase	)

				# initialize weights and apply final processing
				self.post_init()






			@add_start_docstrings_to_model_forward(UpperCAmelCase	)
			@replace_return_docstrings(output_type=UpperCAmelCase					,   config_class=_CONFIG_FOR_DOC	)
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None	):
				a_      				=						return_dict if return_dict is not None else self.config.use_return_dict
				a_      				=						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)

				a_      				=						self.embedder(UpperCAmelCase	)

				a_      				=						self.encoder(UpperCAmelCase					,   output_hidden_states=UpperCAmelCase					,   return_dict=UpperCAmelCase	)

				a_      				=						outputs.hidden_states

				a_      				=						()
				for idx, stage in enumerate(self.stage_names	):
					if stage in self.out_features:
						feature_maps += (hidden_states[idx],)

				if not return_dict:
					a_      				=						(feature_maps,)
					if output_hidden_states:
						output += (outputs.hidden_states,)
					return output

				return BackboneOutput(
				    feature_maps=UpperCAmelCase					,   hidden_states=outputs.hidden_states if output_hidden_states else None					,   attentions=UpperCAmelCase					,   )
