"""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


SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE__:List[Any]			=	"""RegNetConfig"""

# Base docstring
SCREAMING_SNAKE_CASE__:List[str]			=	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE__:Tuple			=	[1, 1088, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE__:Dict			=	"""facebook/regnet-y-040"""
SCREAMING_SNAKE_CASE__:str			=	"""tabby, tabby cat"""

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






class 				snake_case__     (   nn.Module   ):


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






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.convolution(lowerCamelCase		)
        __a     				=  self.normalization(lowerCamelCase		)
        __a     				=  self.activation(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__()
        __a     				=  RegNetConvLayer(
            config.num_channels  ,						config.embedding_size  ,						kernel_size=3  ,						stride=2  ,						activation=config.hidden_act		)
        __a     				=  config.num_channels






 def 		a__ (				self  ,						lowerCamelCase		):
        __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(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


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






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.convolution(lowerCamelCase		)
        __a     				=  self.normalization(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()

        __a     				=  nn.AdaptiveAvgPoolad((1, 1)		)
        __a     				=  nn.Sequential(
            nn.Convad(lowerCamelCase  ,						lowerCamelCase  ,						kernel_size=1		)  ,						nn.ReLU()  ,						nn.Convad(lowerCamelCase  ,						lowerCamelCase  ,						kernel_size=1		)  ,						nn.Sigmoid()  ,						)






 def 		a__ (				self  ,						lowerCamelCase		):
        # b c h w -> b c 1 1
        __a     				=  self.pooler(lowerCamelCase		)
        __a     				=  self.attention(lowerCamelCase		)
        __a     				=  hidden_state * attention
        return hidden_state






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = 1		):
        super().__init__()
        __a     				=  in_channels != out_channels or stride != 1
        __a     				=  max(1  ,						out_channels // config.groups_width		)
        __a     				=  (
            RegNetShortCut(lowerCamelCase  ,						lowerCamelCase  ,						stride=lowerCamelCase		) if should_apply_shortcut else nn.Identity()
        )
        __a     				=  nn.Sequential(
            RegNetConvLayer(lowerCamelCase  ,						lowerCamelCase  ,						kernel_size=1  ,						activation=config.hidden_act		)  ,						RegNetConvLayer(lowerCamelCase  ,						lowerCamelCase  ,						stride=lowerCamelCase  ,						groups=lowerCamelCase  ,						activation=config.hidden_act		)  ,						RegNetConvLayer(lowerCamelCase  ,						lowerCamelCase  ,						kernel_size=1  ,						activation=lowerCamelCase		)  ,						)
        __a     				=  ACTaFN[config.hidden_act]






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  hidden_state
        __a     				=  self.layer(lowerCamelCase		)
        __a     				=  self.shortcut(lowerCamelCase		)
        hidden_state += residual
        __a     				=  self.activation(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


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






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  hidden_state
        __a     				=  self.layer(lowerCamelCase		)
        __a     				=  self.shortcut(lowerCamelCase		)
        hidden_state += residual
        __a     				=  self.activation(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


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

        __a     				=  RegNetXLayer if config.layer_type == "x" else RegNetYLayer

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






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.layers(lowerCamelCase		)
        return hidden_state






class 				snake_case__     (   nn.Module   ):


 def __init__(				self  ,						lowerCamelCase		):
        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(
            RegNetStage(
                lowerCamelCase  ,						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(lowerCamelCase  ,						config.depths[1:]		):
               self.stages.append(RegNetStage(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						depth=lowerCamelCase		)		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = False  ,						lowerCamelCase = 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(lowerCamelCase		)

        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=lowerCamelCase  ,						hidden_states=lowerCamelCase		)






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		RegNetConfig
 _snake_case   :			List[Any]           =		"""regnet"""
 _snake_case   :			Optional[int]           =		"""pixel_values"""
 _snake_case   :			Optional[Any]           =		True


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






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        if isinstance(lowerCamelCase  ,						lowerCamelCase		):
               __a     				=  value
SCREAMING_SNAKE_CASE__:Dict			=	R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

SCREAMING_SNAKE_CASE__:List[Any]			=	R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
"""






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


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config
        __a     				=  RegNetEmbeddings(lowerCamelCase		)
        __a     				=  RegNetEncoder(lowerCamelCase		)
        __a     				=  nn.AdaptiveAvgPoolad((1, 1)		)
        # Initialize weights and apply final processing
        self.post_init()






 @add_start_docstrings_to_model_forward(lowerCamelCase		)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC  ,						output_type=lowerCamelCase  ,						config_class=_CONFIG_FOR_DOC  ,						modality="vision"  ,						expected_output=_EXPECTED_OUTPUT_SHAPE  ,						)
 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = 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(lowerCamelCase		)

        __a     				=  self.encoder(
            lowerCamelCase  ,						output_hidden_states=lowerCamelCase  ,						return_dict=lowerCamelCase		)

        __a     				=  encoder_outputs[0]

        __a     				=  self.pooler(lowerCamelCase		)

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

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






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


 def __init__(				self  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase		)
        __a     				=  config.num_labels
        __a     				=  RegNetModel(lowerCamelCase		)
        # 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(lowerCamelCase		)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT  ,						output_type=lowerCamelCase  ,						config_class=_CONFIG_FOR_DOC  ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,						)
 def 		a__ (				self  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						):
        __a     				=  return_dict if return_dict is not None else self.config.use_return_dict

        __a     				=  self.regnet(lowerCamelCase  ,						output_hidden_states=lowerCamelCase  ,						return_dict=lowerCamelCase		)

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

        __a     				=  self.classifier(lowerCamelCase		)

        __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(lowerCamelCase  ,						lowerCamelCase		)
               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(lowerCamelCase  ,						lowerCamelCase		)

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

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

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_barthez import BarthezTokenizer
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	None

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:Dict			=	{
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE__:List[str]			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		VOCAB_FILES_NAMES
 _snake_case   :			Optional[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			str           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Dict           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		BarthezTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
