






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				int									=							BlenderbotSmallTokenizer
			__snake_case    :				Tuple									=							False







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									super().setUp()

									_SCREAMING_SNAKE_CASE     				=  ["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)

									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
									_SCREAMING_SNAKE_CASE     				=  {"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     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:  Union[str, Any]				,     **UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									kwargs.update(self.special_tokens_map					)
									return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Union[str, Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """adapt act apte"""
									_SCREAMING_SNAKE_CASE     				=  """adapt act apte"""
									return input_text, output_text







			def 							UpperCamelCase				(     self:  str					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BlenderbotSmallTokenizer(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map					)
									_SCREAMING_SNAKE_CASE     				=  """adapt act apte"""
									_SCREAMING_SNAKE_CASE     				=  ["""adapt""", """act""", """ap@@""", """te"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

									_SCREAMING_SNAKE_CASE     				=  [0, 1, 2, 3, 4, 5]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""					)
									assert tok("""sam"""					).input_ids == [1_384]
									_SCREAMING_SNAKE_CASE     				=  """I am a small frog."""
									_SCREAMING_SNAKE_CASE     				=  tok([src_text]				,     padding=UpperCAmelCase_				,     truncation=UpperCAmelCase_					)["""input_ids"""]
									_SCREAMING_SNAKE_CASE     				=  tok.batch_decode(UpperCAmelCase_				,     skip_special_tokens=UpperCAmelCase_				,     clean_up_tokenization_spaces=UpperCAmelCase_					)[0]
									assert src_text != decoded  # I wish it did!
									assert decoded == "i am a small frog ."







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""					)
									_SCREAMING_SNAKE_CASE     				=  """I am a small frog ."""
									_SCREAMING_SNAKE_CASE     				=  """."""
									_SCREAMING_SNAKE_CASE     				=  tok(UpperCAmelCase_					)["""input_ids"""]
									_SCREAMING_SNAKE_CASE     				=  tok(UpperCAmelCase_					)["""input_ids"""]

									assert encoded[-1] == encoded_dot[0]









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


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"mobilenet_v1"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  List[Any]=3				,     UpperCAmelCase_:  List[str]=224				,     UpperCAmelCase_:  Optional[Any]=1.0				,     UpperCAmelCase_:  Dict=8				,     UpperCAmelCase_:  List[str]="relu6"				,     UpperCAmelCase_:  List[Any]=True				,     UpperCAmelCase_:  Dict=0.9_99				,     UpperCAmelCase_:  Union[str, Any]=0.02				,     UpperCAmelCase_:  Tuple=0.0_01				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

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

									_SCREAMING_SNAKE_CASE     				=  num_channels
									_SCREAMING_SNAKE_CASE     				=  image_size
									_SCREAMING_SNAKE_CASE     				=  depth_multiplier
									_SCREAMING_SNAKE_CASE     				=  min_depth
									_SCREAMING_SNAKE_CASE     				=  hidden_act
									_SCREAMING_SNAKE_CASE     				=  tf_padding
									_SCREAMING_SNAKE_CASE     				=  classifier_dropout_prob
									_SCREAMING_SNAKE_CASE     				=  initializer_range
									_SCREAMING_SNAKE_CASE     				=  layer_norm_eps



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							version.parse("1.11"   )







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return OrderedDict([("""pixel_values""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									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:  int					):







									'''simple docstring'''




									return 1E-4

