

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


UpperCAmelCase_							:					str		 =   logging.get_logger(__name__)

# General docstring
UpperCAmelCase_							:					Optional[int]		 =   '''ResNetConfig'''

# Base docstring
UpperCAmelCase_							:					Optional[int]		 =   '''microsoft/resnet-50'''
UpperCAmelCase_							:					List[Any]		 =   [1, 2_048, 7, 7]

# Image classification docstring
UpperCAmelCase_							:					str		 =   '''microsoft/resnet-50'''
UpperCAmelCase_							:					Tuple		 =   '''tiger cat'''

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






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     str					,      __A						:     Dict					,      __A						:     Optional[int]					,      __A						:     Any = 3					,      __A						:     int = 1					,      __A						:     Optional[Any] = "relu"							):
		super().__init__()
		__A :			Optional[int]		    =			nn.Convad(
		    a__					,      a__					,      kernel_size=a__					,      stride=a__					,      padding=kernel_size // 2					,      bias=a__							)
		__A :			Optional[int]		    =			nn.BatchNormad(a__							)
		__A :			List[Any]		    =			ACTaFN[activation] if activation is not None else nn.Identity()




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     Optional[Any]							):
		__A :			int		    =			self.convolution(a__							)
		__A :			int		    =			self.normalization(a__							)
		__A :			Optional[int]		    =			self.activation(a__							)
		return hidden_state






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     Any					,      __A						:     str							):
		super().__init__()
		__A :			List[str]		    =			ResNetConvLayer(
		    config.num_channels					,      config.embedding_size					,      kernel_size=7					,      stride=2					,      activation=config.hidden_act							)
		__A :			Union[str, Any]		    =			nn.MaxPoolad(kernel_size=3					,      stride=2					,      padding=1							)
		__A :			Optional[int]		    =			config.num_channels




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     Tuple							):
		__A :			int		    =			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 :			Any		    =			self.embedder(a__							)
		__A :			Optional[Any]		    =			self.pooler(a__							)
		return embedding






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     Dict					,      __A						:     Optional[int]					,      __A						:     int					,      __A						:     Optional[Any] = 2							):
		super().__init__()
		__A :			Union[str, Any]		    =			nn.Convad(a__					,      a__					,      kernel_size=1					,      stride=a__					,      bias=a__							)
		__A :			str		    =			nn.BatchNormad(a__							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     str							):
		__A :			List[str]		    =			self.convolution(a__							)
		__A :			Any		    =			self.normalization(a__							)
		return hidden_state






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     Any					,      __A						:     Union[str, Any]					,      __A						:     Tuple					,      __A						:     Any = 1					,      __A						:     List[Any] = "relu"							):
		super().__init__()
		__A :			List[str]		    =			in_channels != out_channels or stride != 1
		__A :			List[str]		    =			(
		    ResNetShortCut(a__					,      a__					,      stride=a__							) if should_apply_shortcut else nn.Identity()
		)
		__A :			Tuple		    =			nn.Sequential(
		    ResNetConvLayer(a__					,      a__					,      stride=a__							)					,      ResNetConvLayer(a__					,      a__					,      activation=a__							)					,      )
		__A :			Tuple		    =			ACTaFN[activation]




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     int							):
		__A :			int		    =			hidden_state
		__A :			Optional[Any]		    =			self.layer(a__							)
		__A :			Optional[Any]		    =			self.shortcut(a__							)
		hidden_state += residual
		__A :			List[str]		    =			self.activation(a__							)
		return hidden_state






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     str					,      __A						:     str					,      __A						:     Tuple					,      __A						:     Tuple = 1					,      __A						:     Optional[Any] = "relu"					,      __A						:     Tuple = 4							):
		super().__init__()
		__A :			Optional[Any]		    =			in_channels != out_channels or stride != 1
		__A :			int		    =			out_channels // reduction
		__A :			Optional[Any]		    =			(
		    ResNetShortCut(a__					,      a__					,      stride=a__							) if should_apply_shortcut else nn.Identity()
		)
		__A :			Union[str, Any]		    =			nn.Sequential(
		    ResNetConvLayer(a__					,      a__					,      kernel_size=1							)					,      ResNetConvLayer(a__					,      a__					,      stride=a__							)					,      ResNetConvLayer(a__					,      a__					,      kernel_size=1					,      activation=a__							)					,      )
		__A :			Union[str, Any]		    =			ACTaFN[activation]




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     List[str]							):
		__A :			List[str]		    =			hidden_state
		__A :			Dict		    =			self.layer(a__							)
		__A :			Optional[int]		    =			self.shortcut(a__							)
		hidden_state += residual
		__A :			Union[str, Any]		    =			self.activation(a__							)
		return hidden_state






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     Union[str, Any]					,      __A						:     Optional[Any]					,      __A						:     Optional[int]					,      __A						:     Tuple					,      __A						:     str = 2					,      __A						:     Union[str, Any] = 2					,      ):
		super().__init__()

		__A :			str		    =			ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

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




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     Any							):
		__A :			str		    =			input
		for layer in self.layers:
			__A :			List[Any]		    =			layer(a__							)
		return hidden_state






class      lowerCamelCase_      (	nn.Module	):




	def __init__(     self						:     str					,      __A						:     Optional[int]							):
		super().__init__()
		__A :			Union[str, Any]		    =			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(
		        a__					,      config.embedding_size					,      config.hidden_sizes[0]					,      stride=2 if config.downsample_in_first_stage else 1					,      depth=config.depths[0]					,      )							)
		__A :			List[Any]		    =			zip(config.hidden_sizes					,      config.hidden_sizes[1:]							)
		for (in_channels, out_channels), depth in zip(a__					,      config.depths[1:]							):
			self.stages.append(ResNetStage(a__					,      a__					,      a__					,      depth=a__							)							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     int					,      __A						:     List[str] = False					,      __A						:     Tuple = True							):
		__A :			Dict		    =			() if output_hidden_states else None

		for stage_module in self.stages:
			if output_hidden_states:
				__A :			Optional[Any]		    =			hidden_states + (hidden_state,)

			__A :			Union[str, Any]		    =			stage_module(a__							)

		if output_hidden_states:
			__A :			Dict		    =			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=a__					,      hidden_states=a__					,      )






class      lowerCamelCase_      (	lowerCamelCase__	):
	_lowercase		:   str     					=				ResNetConfig
	_lowercase		:   str     					=				'''resnet'''
	_lowercase		:   List[Any]     					=				'''pixel_values'''
	_lowercase		:   Optional[Any]     					=				True




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     List[str]							):





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




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Any					,      __A						:     Dict=False							):





		if isinstance(a__					,      a__							):
			__A :			Tuple		    =			value






UpperCAmelCase_							:					Optional[int]		 =   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'''

UpperCAmelCase_							:					Union[str, Any]		 =   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.'''						,      lowerCamelCase__						,      )
class      lowerCamelCase_      (	lowerCamelCase__	):




	def __init__(     self						:     int					,      __A						:     Dict							):
		super().__init__(a__							)
		__A :			Tuple		    =			config
		__A :			Optional[int]		    =			ResNetEmbeddings(a__							)
		__A :			Optional[int]		    =			ResNetEncoder(a__							)
		__A :			int		    =			nn.AdaptiveAvgPoolad((1, 1)							)
		# Initialize weights and apply final processing
		self.post_init()




	@add_start_docstrings_to_model_forward(a__							)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC					,      output_type=a__					,      config_class=_CONFIG_FOR_DOC					,      modality="""vision"""					,      expected_output=_EXPECTED_OUTPUT_SHAPE					,      )
	def        lowerCAmelCase_       (     self						:     str					,      __A						:     int					,      __A						:     List[str] = None					,      __A						:     Union[str, Any] = None							):
		__A :			Tuple		    =			(
		    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)
		__A :			List[Any]		    =			return_dict if return_dict is not None else self.config.use_return_dict

		__A :			Union[str, Any]		    =			self.embedder(a__							)

		__A :			Union[str, Any]		    =			self.encoder(
		    a__					,      output_hidden_states=a__					,      return_dict=a__							)

		__A :			List[Any]		    =			encoder_outputs[0]

		__A :			Optional[int]		    =			self.pooler(a__							)

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

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






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




	def __init__(     self						:     str					,      __A						:     Optional[Any]							):
		super().__init__(a__							)
		__A :			Any		    =			config.num_labels
		__A :			int		    =			ResNetModel(a__							)
		# classification head
		__A :			str		    =			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(a__							)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT					,      output_type=a__					,      config_class=_CONFIG_FOR_DOC					,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,      )
	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Optional[Any] = None					,      __A						:     Optional[Any] = None					,      __A						:     Any = None					,      __A						:     str = None					,      ):
		__A :			Dict		    =			return_dict if return_dict is not None else self.config.use_return_dict

		__A :			Optional[int]		    =			self.resnet(a__					,      output_hidden_states=a__					,      return_dict=a__							)

		__A :			str		    =			outputs.pooler_output if return_dict else outputs[1]

		__A :			Tuple		    =			self.classifier(a__							)

		__A :			Union[str, Any]		    =			None

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

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

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






@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''						,      lowerCamelCase__						,      )
class      lowerCamelCase_      (	lowerCamelCase__						,      lowerCamelCase__	):




	def __init__(     self						:     Optional[Any]					,      __A						:     List[Any]							):
		super().__init__(a__							)
		super()._init_backbone(a__							)

		__A :			str		    =			[config.embedding_size] + config.hidden_sizes
		__A :			Optional[int]		    =			ResNetEmbeddings(a__							)
		__A :			Optional[Any]		    =			ResNetEncoder(a__							)

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




	@add_start_docstrings_to_model_forward(a__							)
	@replace_return_docstrings(output_type=a__					,      config_class=_CONFIG_FOR_DOC							)
	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     List[str]					,      __A						:     List[str] = None					,      __A						:     int = None							):
		__A :			Any		    =			return_dict if return_dict is not None else self.config.use_return_dict
		__A :			Optional[int]		    =			(
		    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
		)

		__A :			Dict		    =			self.embedder(a__							)

		__A :			Any		    =			self.encoder(a__					,      output_hidden_states=a__					,      return_dict=a__							)

		__A :			Optional[int]		    =			outputs.hidden_states

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

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

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












import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   OpenAIGPTTokenizer
      UpperCAmelCase__							=   OpenAIGPTTokenizerFast
      UpperCAmelCase__							=   True
      UpperCAmelCase__							=   False


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             _lowerCamelCase				:  Tuple						=		[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''w</w>''',
                 '''r</w>''',
                 '''t</w>''',
                 '''lo''',
                 '''low''',
                 '''er</w>''',
                 '''low</w>''',
                 '''lowest</w>''',
                 '''newer</w>''',
                 '''wider</w>''',
                 '''<unk>''',
             ]
             _lowerCamelCase				:  Any						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  List[str]						=		['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

             _lowerCamelCase				:  str						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  int						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file'''])
             with open(self.vocab_file   ,				'''w''') as fp:
                    fp.write(json.dumps(a__))
             with open(self.merges_file   ,				'''w''') as fp:
                    fp.write('''\n'''.join(a__))


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return "lower newer", "lower newer"


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		OpenAIGPTTokenizer(self.vocab_file   ,				self.merges_file)

             _lowerCamelCase				:  Dict						=		'''lower'''
             _lowerCamelCase				:  Optional[Any]						=		['''low''', '''er</w>''']
             _lowerCamelCase				:  List[Any]						=		tokenizer.tokenize(a__)
             self.assertListEqual(a__   ,				a__)

             _lowerCamelCase				:  Any						=		tokens + ['''<unk>''']
             _lowerCamelCase				:  Optional[int]						=		[14, 15, 20]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)   ,				a__)


      def 					__snake_case      (		self   ,				a__=15):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  Dict						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)

                           # Simple input
                           _lowerCamelCase				:  Optional[int]						=		'''This is a simple input'''
                           _lowerCamelCase				:  Optional[Any]						=		['''This is a simple input 1''', '''This is a simple input 2''']
                           _lowerCamelCase				:  Optional[int]						=		('''This is a simple input''', '''This is a pair''')
                           _lowerCamelCase				:  List[Any]						=		[
                               ('''This is a simple input 1''', '''This is a simple input 2'''),
                               ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                           ]

                           # Simple input tests
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Simple input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(a__   ,				tokenizer_r.encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length''')

                           # Pair input
                           self.assertRaises(
                               a__   ,				tokenizer_r.batch_encode_plus   ,				a__   ,				max_length=a__   ,				padding='''max_length'''   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             pass







@require_ftfy
@require_spacy
@require_tokenizers
class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      pass

