


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
    from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase     :    List[Any] 	=				get_tests_dir('fixtures/test_sentencepiece.model')

__UpperCamelCase     :    int 	=				{'target_lang': 'fi', 'source_lang': 'en'}
__UpperCamelCase     :    int 	=				'>>zh<<'
__UpperCamelCase     :    Any 	=				'Helsinki-NLP/'

if is_torch_available():
    __UpperCamelCase     :    str 	=				'pt'
elif is_tf_available():
    __UpperCamelCase     :    List[str] 	=				'tf'
else:
    __UpperCamelCase     :    str 	=				'jax'



@require_sentencepiece
class 				_UpperCamelCase			(    _a,unittest.TestCase    ):

   '''simple docstring'''







   a_					:				Dict     =			MarianTokenizer
   a_					:				Optional[int]     =			False
   a_					:				Any     =			True

   def 	_snake_case     (	self	:			int				):






         '''simple docstring'''



         super().setUp()
         __lowerCamelCase  :   Optional[Any]							    =    ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
         __lowerCamelCase  :   Tuple							    =    dict(zip(A_							,	range(len(A_				)				)				)				)
         __lowerCamelCase  :   int							    =    Path(self.tmpdirname				)
         save_json(A_							,	save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
         save_json(A_							,	save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
         if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
               copyfile(A_							,	save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
               copyfile(A_							,	save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

         __lowerCamelCase  :   List[str]							    =    MarianTokenizer.from_pretrained(self.tmpdirname				)
         tokenizer.save_pretrained(self.tmpdirname				)

   def 	_snake_case     (	self	:			Dict							,	**_lowerCamelCase	:			Any				):






         '''simple docstring'''



         return MarianTokenizer.from_pretrained(self.tmpdirname							,	**A_				)

   def 	_snake_case     (	self	:			int							,	_lowerCamelCase	:			List[str]				):






         '''simple docstring'''



         return (
             "This is a test",
             "This is a test",
         )

   def 	_snake_case     (	self	:			Any				):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    """</s>"""
         __lowerCamelCase  :   int							    =    0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)							,	A_				)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)							,	A_				)

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    list(self.get_tokenizer().get_vocab().keys()				)

         self.assertEqual(vocab_keys[0]							,	"""</s>"""				)
         self.assertEqual(vocab_keys[1]							,	"""<unk>"""				)
         self.assertEqual(vocab_keys[-1]							,	"""<pad>"""				)
         self.assertEqual(len(A_				)							,	9				)

   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         self.assertEqual(self.get_tokenizer().vocab_size							,	9				)

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   List[str]							    =    MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""				)
         __lowerCamelCase  :   List[str]							    =    en_de_tokenizer(["""I am a small frog"""]							,	return_tensors=A_				)
         self.assertIsInstance(A_							,	A_				)
         __lowerCamelCase  :   Optional[int]							    =    [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
         self.assertListEqual(A_							,	batch.input_ids[0]				)

         __lowerCamelCase  :   Optional[int]							    =    tempfile.mkdtemp()
         en_de_tokenizer.save_pretrained(A_				)
         __lowerCamelCase  :   str							    =    [x.name for x in Path(A_				).glob("""*"""				)]
         self.assertIn("""source.spm"""							,	A_				)
         MarianTokenizer.from_pretrained(A_				)

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   int							    =    self.get_tokenizer()

         __lowerCamelCase  :   str							    =    tok(
             ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]							,	padding=A_							,	truncation=A_							,	return_tensors=A_				)
         self.assertIsInstance(A_							,	A_				)
         self.assertEqual(batch.input_ids.shape							,	(2, 5_1_2)				)

   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         __lowerCamelCase  :   Tuple							    =    self.get_tokenizer()
         __lowerCamelCase  :   List[Any]							    =    tok(["""I am a tiny frog""", """I am a small frog"""]							,	padding=A_							,	return_tensors=A_				)
         self.assertIsInstance(A_							,	A_				)
         self.assertEqual(batch_smaller.input_ids.shape							,	(2, 1_0)				)

   @slow
   def 	_snake_case     (	self	:			List[Any]				):






         '''simple docstring'''



         __lowerCamelCase  :   Dict							    =    {"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=A_							,	model_name="""Helsinki-NLP/opus-mt-en-de"""							,	revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""							,	decode_kwargs={"""use_source_tokenizer""": True}							,	)


   def 	_snake_case     (	self	:			Tuple				):






         '''simple docstring'''



         __lowerCamelCase  :   str							    =    MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

         __lowerCamelCase  :   Union[str, Any]							    =    """Tämä on testi"""
         __lowerCamelCase  :   Union[str, Any]							    =    """This is a test"""

         __lowerCamelCase  :   int							    =    [7_6, 7, 2_0_4_7, 2]
         __lowerCamelCase  :   Optional[int]							    =    [6_9, 1_2, 1_1, 9_4_0, 2]

         __lowerCamelCase  :   List[str]							    =    tokenizer(A_				).input_ids
         self.assertListEqual(A_							,	A_				)

         __lowerCamelCase  :   Union[str, Any]							    =    tokenizer(text_target=A_				).input_ids
         self.assertListEqual(A_							,	A_				)

         __lowerCamelCase  :   str							    =    tokenizer.decode(A_							,	skip_special_tokens=A_				)
         self.assertEqual(A_							,	A_				)







"""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


UpperCAmelCase__					=logging.get_logger(__name__)

# General docstring
UpperCAmelCase__					="RegNetConfig"

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

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

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





class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 3   ,							A_						:						int = 1   ,							A_						:						int = 1   ,							A_						:						Optional[str] = "relu"   ,							):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						nn.Convad(
           A_   ,							A_   ,							kernel_size=A_   ,							stride=A_   ,							padding=kernel_size // 2   ,							groups=A_   ,							bias=A_   ,							)
       __lowercase						=						nn.BatchNormad(A_				)
       __lowercase						=						ACTaFN[activation] if activation is not None else nn.Identity()






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Dict   ,							A_						:						RegNetConfig				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						RegNetConvLayer(
           config.num_channels   ,							config.embedding_size   ,							kernel_size=3   ,							stride=2   ,							activation=config.hidden_act				)
       __lowercase						=						config.num_channels






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						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."""				)
       __lowercase						=						self.embedder(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						nn.Convad(A_   ,							A_   ,							kernel_size=1   ,							stride=A_   ,							bias=A_				)
       __lowercase						=						nn.BatchNormad(A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__()

       __lowercase						=						nn.AdaptiveAvgPoolad((1, 1)				)
       __lowercase						=						nn.Sequential(
           nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.ReLU()   ,							nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.Sigmoid()   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.pooler(A_				)
       __lowercase						=						self.attention(A_				)
       __lowercase						=						hidden_state * attention
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Any   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						List[str]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2   ,							A_						:						int = 2   ,							):






       '''simple docstring'''


       super().__init__()

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

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






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.layers(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Tuple   ,							A_						:						RegNetConfig				):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Tensor   ,							A_						:						bool = False   ,							A_						:						bool = True				):






       '''simple docstring'''


       __lowercase						=						() if output_hidden_states else None

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

        __lowercase						=						stage_module(A_				)

       if output_hidden_states:
        __lowercase						=						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__    (		_a     ):
      a			:  List[str]           =		RegNetConfig
      a			:  int           =		"""regnet"""
      a			:  Tuple           =		"""pixel_values"""
      a			:  Dict           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[Any]				):






       '''simple docstring'''


       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 SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Dict   ,							A_						:						List[str]=False				):






       '''simple docstring'''


       if isinstance(A_   ,							A_				):
        __lowercase						=						value






UpperCAmelCase__					=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"

UpperCAmelCase__					=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  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Any   ,							A_						:						Any				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config
       __lowercase						=						RegNetEmbeddings(A_				)
       __lowercase						=						RegNetEncoder(A_				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None				):






       '''simple docstring'''


       __lowercase						=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       __lowercase						=						return_dict if return_dict is not None else self.config.use_return_dict

       __lowercase						=						self.embedder(A_				)

       __lowercase						=						self.encoder(
           A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

       __lowercase						=						encoder_outputs[0]

       __lowercase						=						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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      , _a      , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config.num_labels
       __lowercase						=						RegNetModel(A_				)
       # classification head
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[torch.FloatTensor] = None   ,							A_						:						Optional[torch.LongTensor] = None   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None   ,							):






       '''simple docstring'''


       __lowercase						=						return_dict if return_dict is not None else self.config.use_return_dict

       __lowercase						=						self.regnet(A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

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

       __lowercase						=						self.classifier(A_				)

       __lowercase						=						None

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

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

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