



'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class 	lowerCAmelCase_    (    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				'''mobilenet_v2'''




  def __init__(   self  ,__UpperCAmelCase=3  ,__UpperCAmelCase=224  ,__UpperCAmelCase=1.0  ,__UpperCAmelCase=8  ,__UpperCAmelCase=8  ,__UpperCAmelCase=6  ,__UpperCAmelCase=32  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase="relu6"  ,__UpperCAmelCase=True  ,__UpperCAmelCase=0.8  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=0.0_0_1  ,__UpperCAmelCase=255  ,**__UpperCAmelCase  ,)   ->		Tuple:
      super().__init__(**__UpperCAmelCase				)

      if depth_multiplier <= 0:
          raise ValueError("""depth_multiplier must be greater than zero."""				)

      lowerCAmelCase__			:  Tuple											= num_channels
      lowerCAmelCase__			:  Tuple											= image_size
      lowerCAmelCase__			:  List[str]											= depth_multiplier
      lowerCAmelCase__			:  Optional[int]											= depth_divisible_by
      lowerCAmelCase__			:  Optional[int]											= min_depth
      lowerCAmelCase__			:  Tuple											= expand_ratio
      lowerCAmelCase__			:  Optional[int]											= output_stride
      lowerCAmelCase__			:  Optional[int]											= first_layer_is_expansion
      lowerCAmelCase__			:  Optional[Any]											= finegrained_output
      lowerCAmelCase__			:  Tuple											= hidden_act
      lowerCAmelCase__			:  Any											= tf_padding
      lowerCAmelCase__			:  List[Any]											= classifier_dropout_prob
      lowerCAmelCase__			:  Dict											= initializer_range
      lowerCAmelCase__			:  int											= layer_norm_eps
      lowerCAmelCase__			:  str											= semantic_loss_ignore_index







class 	lowerCAmelCase_    (    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				version.parse('''1.11'''					)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Mapping[str, Mapping[int, str]]:
      return OrderedDict([("""pixel_values""", {0: """batch"""})]				)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Mapping[str, Mapping[int, str]]:
      if self.task == "image-classification":
          return OrderedDict([("""logits""", {0: """batch"""})]				)
      else:
          return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)






  @property
  def 							UpperCAmelCase_     (   self				)   ->		float:
      return 1E-4







'''simple docstring'''





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
 from transformers.models.rag.configuration_rag import RagConfig
 from transformers.models.rag.tokenization_rag import RagTokenizer






@require_faiss
@require_torch
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  str											= tempfile.mkdtemp()
      lowerCAmelCase__			:  List[Any]											= 8

      # DPR tok
      lowerCAmelCase__			:  int											= [
          """[UNK]""",
          """[CLS]""",
          """[SEP]""",
          """[PAD]""",
          """[MASK]""",
          """want""",
          """##want""",
          """##ed""",
          """wa""",
          """un""",
          """runn""",
          """##ing""",
          """,""",
          """low""",
          """lowest""",
      ]
      lowerCAmelCase__			:  List[Any]											= os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,DPR_VOCAB_FILES_NAMES["""vocab_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as vocab_writer:
          vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

      # BART tok
      lowerCAmelCase__			:  str											= [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """\u0120""",
          """\u0120l""",
          """\u0120n""",
          """\u0120lo""",
          """\u0120low""",
          """er""",
          """\u0120lowest""",
          """\u0120newer""",
          """\u0120wider""",
          """<unk>""",
      ]
      lowerCAmelCase__			:  List[Any]											= dict(zip(__UpperCAmelCase  ,range(len(__UpperCAmelCase				)				)				)				)
      lowerCAmelCase__			:  Optional[Any]											= ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
      lowerCAmelCase__			:  Any											= {"""unk_token""": """<unk>"""}

      lowerCAmelCase__			:  str											= os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)
      os.makedirs(__UpperCAmelCase  ,exist_ok=__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""vocab_file"""]				)
      lowerCAmelCase__			:  Dict											= os.path.join(__UpperCAmelCase  ,BART_VOCAB_FILES_NAMES["""merges_file"""]				)
      with open(self.vocab_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write(json.dumps(__UpperCAmelCase				) + """\n"""				)
      with open(self.merges_file  ,"""w"""  ,encoding="""utf-8"""				) as fp:
          fp.write("""\n""".join(__UpperCAmelCase				)				)




  def 							UpperCAmelCase_     (   self				)   ->		DPRQuestionEncoderTokenizer:
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""dpr_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		BartTokenizer:
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,"""bart_tokenizer"""				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      shutil.rmtree(self.tmpdirname				)




  @require_tokenizers
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Any											= os.path.join(self.tmpdirname  ,"""rag_tokenizer"""				)
      lowerCAmelCase__			:  Any											= RagConfig(question_encoder=DPRConfig().to_dict()  ,generator=BartConfig().to_dict()				)
      lowerCAmelCase__			:  str											= RagTokenizer(question_encoder=self.get_dpr_tokenizer()  ,generator=self.get_bart_tokenizer()				)
      rag_config.save_pretrained(__UpperCAmelCase				)
      rag_tokenizer.save_pretrained(__UpperCAmelCase				)
      lowerCAmelCase__			:  Any											= RagTokenizer.from_pretrained(__UpperCAmelCase  ,config=__UpperCAmelCase				)
      self.assertIsInstance(new_rag_tokenizer.question_encoder  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()  ,rag_tokenizer.question_encoder.get_vocab()				)
      self.assertIsInstance(new_rag_tokenizer.generator  ,__UpperCAmelCase				)
      self.assertEqual(new_rag_tokenizer.generator.get_vocab()  ,rag_tokenizer.generator.get_vocab()				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= RagTokenizer.from_pretrained("""facebook/rag-token-nq"""				)
      lowerCAmelCase__			:  Optional[Any]											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Dict											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  str											= RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""				)
      lowerCAmelCase__			:  str											= [
          """who got the first nobel prize in physics""",
          """when is the next deadpool movie being released""",
          """which mode is used for short wave broadcast service""",
          """who is the owner of reading football club""",
          """when is the next scandal episode coming out""",
          """when is the last time the philadelphia won the superbowl""",
          """what is the most current adobe flash player version""",
          """how many episodes are there in dragon ball z""",
          """what is the first step in the evolution of the eye""",
          """where is gall bladder situated in human body""",
          """what is the main mineral in lithium batteries""",
          """who is the president of usa right now""",
          """where do the greasers live in the outsiders""",
          """panda is a national animal of which country""",
          """what is the name of manchester united stadium""",
      ]
      lowerCAmelCase__			:  Tuple											= tokenizer(__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)


