


"""simple docstring"""

import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


UpperCAmelCase__					="bart"
UpperCAmelCase__					=True


@st.cache(allow_output_mutation=UpperCamelCase__			)
def lowerCAmelCase_   (					):






 """simple docstring"""







 if LOAD_DENSE_INDEX:
  __lowercase						=						AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""			)
  __lowercase						=						AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""			).to("""cuda:0"""			)
  __lowercase						=						qar_model.eval()
 else:
  __lowercase				,		__lowercase						=						(None, None)
 if MODEL_TYPE == "bart":
  __lowercase						=						AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""			)
  __lowercase						=						AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""			).to("""cuda:0"""			)
  __lowercase						=						torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""			)
  sas_model.load_state_dict(save_dict["""model"""]			)
  __lowercase						=						sas_model.eval()
 else:
  __lowercase				,		__lowercase						=						make_qa_sas_model(
      model_name="""t5-small"""					, from_file="""seq2seq_models/eli5_t5_model_1024_4.pth"""					, device="""cuda:0"""			)
 return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)


@st.cache(allow_output_mutation=UpperCamelCase__			)
def lowerCAmelCase_   (					):






 """simple docstring"""







 if LOAD_DENSE_INDEX:
  __lowercase						=						faiss.StandardGpuResources()
  __lowercase						=						datasets.load_dataset(path="""wiki_snippets"""					, name="""wiki40b_en_100_0"""			)["""train"""]
  __lowercase						=						np.memmap(
      """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""					, dtype="""float32"""					, mode="""r"""					, shape=(wikiaab_passages.num_rows, 128)					, )
  __lowercase						=						faiss.IndexFlatIP(128			)
  __lowercase						=						faiss.index_cpu_to_gpu(UpperCamelCase__					, 1					, UpperCamelCase__			)
  wikiaab_gpu_index_flat.add(UpperCamelCase__			)  # TODO fix for larger GPU
 else:
  __lowercase				,		__lowercase						=						(None, None)
 __lowercase						=						Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]			)
 return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=UpperCamelCase__			)
def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						datasets.load_dataset("""eli5"""					, name="""LFQA_reddit"""			)
 __lowercase						=						elia["""train_eli5"""]
 __lowercase						=						np.memmap(
     """eli5_questions_reps.dat"""					, dtype="""float32"""					, mode="""r"""					, shape=(elia_train.num_rows, 128)			)
 __lowercase						=						faiss.IndexFlatIP(128			)
 eli5_train_q_index.add(UpperCamelCase__			)
 return (elia_train, eli5_train_q_index)


UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=load_indexes()
UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__					=load_models()
UpperCAmelCase__				,   UpperCAmelCase__					=load_train_data()


def lowerCAmelCase_   (					UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			List[Any]=10			):






 """simple docstring"""







 __lowercase						=						embed_questions_for_retrieval([question]					, UpperCamelCase__					, UpperCamelCase__			)
 __lowercase				,		__lowercase						=						eli5_train_q_index.search(UpperCamelCase__					, UpperCamelCase__			)
 __lowercase						=						[elia_train[int(UpperCamelCase__			)] for i in I[0]]
 return nn_examples


def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Optional[Any]="wiki40b"					, UpperCamelCase__						:			List[Any]="dense"					, UpperCamelCase__						:			List[Any]=10			):






 """simple docstring"""







 if source == "none":
  __lowercase				,		__lowercase						=						(""" <P> """.join(["""""" for _ in range(11			)]			).strip(), [])
 else:
  if method == "dense":
   __lowercase				,		__lowercase						=						query_qa_dense_index(
       UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
  else:
   __lowercase				,		__lowercase						=						query_es_index(
       UpperCamelCase__					, UpperCamelCase__					, index_name="""english_wiki40b_snippets_100w"""					, n_results=UpperCamelCase__					, )
 __lowercase						=						[
     (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
 ]
 __lowercase						=						"""question: {} context: {}""".format(UpperCamelCase__					, UpperCamelCase__			)
 return question_doc, support_list


@st.cache(
    hash_funcs={
        torch.Tensor: (lambda UpperCamelCase__			: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda UpperCamelCase__			: None),
    }			)
def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]					, UpperCamelCase__						:			Optional[int]					, UpperCamelCase__						:			Optional[Any]					, UpperCamelCase__						:			Union[str, Any]=64					, UpperCamelCase__						:			int=256					, UpperCamelCase__						:			int=False					, UpperCamelCase__						:			Tuple=2					, UpperCamelCase__						:			List[Any]=0.95					, UpperCamelCase__						:			Union[str, Any]=0.8			):






 """simple docstring"""







 with torch.no_grad():
  __lowercase						=						qa_sas_generate(
      UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, num_answers=1					, num_beams=UpperCamelCase__					, min_len=UpperCamelCase__					, max_len=UpperCamelCase__					, do_sample=UpperCamelCase__					, temp=UpperCamelCase__					, top_p=UpperCamelCase__					, top_k=UpperCamelCase__					, max_input_length=1024					, device="""cuda:0"""					, )[0]
 return (answer, support_list)


st.title("Long Form Question Answering with ELI5")

# Start sidebar
UpperCAmelCase__					="<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
UpperCAmelCase__					="\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class=\"img-container\"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n" % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
UpperCAmelCase__					="\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)

UpperCAmelCase__					=[
    "Answer the question",
    "View the retrieved document only",
    "View the most similar ELI5 question and answer",
    "Show me everything, please!",
]
UpperCAmelCase__					=st.sidebar.checkbox("Demo options")
if demo_options:
       UpperCAmelCase__					=st.sidebar.selectbox(
           "",
           action_list,
           index=3,
       )
       UpperCAmelCase__					=action_list.index(action_st)
       UpperCAmelCase__					=st.sidebar.selectbox(
           "",
           ["Show full text of passages", "Show passage section titles"],
           index=0,
       )
       UpperCAmelCase__					=show_type == "Show full text of passages"
else:
       UpperCAmelCase__					=3
       UpperCAmelCase__					=True

UpperCAmelCase__					=st.sidebar.checkbox("Retrieval options")
if retrieval_options:
       UpperCAmelCase__					="\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    "
       st.sidebar.markdown(retriever_info)
       UpperCAmelCase__					=st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
       UpperCAmelCase__					=st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
       UpperCAmelCase__					="wiki40b"
       UpperCAmelCase__					="dense"

UpperCAmelCase__					="beam"
UpperCAmelCase__					=2
UpperCAmelCase__					=64
UpperCAmelCase__					=256
UpperCAmelCase__					=None
UpperCAmelCase__					=None
UpperCAmelCase__					=st.sidebar.checkbox("Generation options")
if generate_options:
       UpperCAmelCase__					="\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder's output probabilities.\n    "
       st.sidebar.markdown(generate_info)
       UpperCAmelCase__					=st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
       UpperCAmelCase__					=st.sidebar.slider(
           "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
       )
       UpperCAmelCase__					=st.sidebar.slider(
           "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
       )
       if sampled == "beam":
              UpperCAmelCase__					=st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
       else:
              UpperCAmelCase__					=st.sidebar.slider(
                  "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
              )
              UpperCAmelCase__					=st.sidebar.slider(
                  "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
              )
              UpperCAmelCase__					=None

# start main text
UpperCAmelCase__					=[
    "<MY QUESTION>",
    "How do people make chocolate?",
    "Why do we get a fever when we are sick?",
    "How can different animals perceive different colors?",
    "What is natural language processing?",
    "What's the best way to treat a sunburn?",
    "What exactly are vitamins ?",
    "How does nuclear energy provide electricity?",
    "What's the difference between viruses and bacteria?",
    "Why are flutes classified as woodwinds when most of them are made out of metal ?",
    "Why do people like drinking coffee even though it tastes so bad?",
    "What happens when wine ages? How does it make the wine taste better?",
    "If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?",
    "How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?",
    "How does New Zealand have so many large bird predators?",
]
UpperCAmelCase__					=st.selectbox(
    "What would you like to ask? ---- select <MY QUESTION> to enter a new query",
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
       UpperCAmelCase__					=st.text_input("Enter your question here:", "")
else:
       UpperCAmelCase__					=question_s

if st.button("Show me!"):
       if action in [0, 1, 3]:
              if index_type == "mixed":
                     UpperCAmelCase__				,   UpperCAmelCase__					=make_support(question, source=wiki_source, method="dense", n_results=10)
                     UpperCAmelCase__				,   UpperCAmelCase__					=make_support(question, source=wiki_source, method="sparse", n_results=10)
                     UpperCAmelCase__					=[]
                     for res_d, res_s in zip(support_list_dense, support_list_sparse):
                            if tuple(res_d) not in support_list:
                                   support_list += [tuple(res_d)]
                            if tuple(res_s) not in support_list:
                                   support_list += [tuple(res_s)]
                     UpperCAmelCase__					=support_list[:10]
                     UpperCAmelCase__					="<P> " + " <P> ".join([res[-1] for res in support_list])
              else:
                     UpperCAmelCase__				,   UpperCAmelCase__					=make_support(question, source=wiki_source, method=index_type, n_results=10)
       if action in [0, 3]:
              UpperCAmelCase__				,   UpperCAmelCase__					=answer_question(
                  question_doc,
                  sas_model,
                  sas_tokenizer,
                  min_len=min_len,
                  max_len=int(max_len),
                  sampling=(sampled == "sampled"),
                  n_beams=n_beams,
                  top_p=top_p,
                  temp=temp,
              )
              st.markdown("### The model generated answer is:")
              st.write(answer)
       if action in [0, 1, 3] and wiki_source != "none":
              st.markdown("--- \n ### The model is drawing information from the following Wikipedia passages:")
              for i, res in enumerate(support_list):
                     UpperCAmelCase__					="https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
                     UpperCAmelCase__					=res[1].strip()
                     if sec_titles == "":
                            UpperCAmelCase__					="[{}]({})".format(res[0], wiki_url)
                     else:
                            UpperCAmelCase__					=sec_titles.split(" & ")
                            UpperCAmelCase__					=" & ".join(
                                ["[{}]({}#{})".format(sec.strip(), wiki_url, sec.strip().replace(" ", "_")) for sec in sec_list]
                            )
                     st.markdown(
                         "{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}".format(i + 1, res[0], sections),
                         unsafe_allow_html=True,
                     )
                     if show_passages:
                            st.write(
                                "> <span style=\"font-family:arial; font-size:10pt;\">" + res[-1] + "</span>", unsafe_allow_html=True
                            )
       if action in [2, 3]:
              UpperCAmelCase__					=find_nearest_training(question)
              UpperCAmelCase__					=nn_train_list[0]
              st.markdown(
                  "--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
              )
              UpperCAmelCase__					=[
                  "{}. {}".format(i + 1, "  \n".join([line.strip() for line in ans.split("\n") if line.strip() != ""]))
                  for i, (ans, sc) in enumerate(zip(train_exple["answers"]["text"], train_exple["answers"]["score"]))
                  if i == 0 or sc > 2
              ]
              st.markdown("##### Its answers were: \n\n {}".format("\n".join(answers_st)))


UpperCAmelCase__					="\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"

st.sidebar.markdown(disclaimer, unsafe_allow_html=True)




"""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				)
