


'''simple docstring'''


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 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     BlenderbotSmallTokenizer
			lowerCAmelCase :	List[Any]          =     False




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					super().setUp()

					_UpperCAmelCase		:       List[Any]								=      ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
					_UpperCAmelCase		:       Dict								=      dict(zip(lowerCamelCase__	,  range(len(lowerCamelCase__ ) ) ) )

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

					_UpperCAmelCase		:       List[str]								=      os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"] )
					_UpperCAmelCase		:       Any								=      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(lowerCamelCase__ ) + "\n" )
					with open(self.merges_file	,  "w"	,  encoding="utf-8" ) as fp:
							fp.write("\n".join(lowerCamelCase__ ) )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  **lowerCamelCase__  :     List[Any] )   ->List[Any]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      "adapt act apte"
					_UpperCAmelCase		:       Tuple								=      "adapt act apte"
					return input_text, output_text




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      BlenderbotSmallTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map )
					_UpperCAmelCase		:       Optional[Any]								=      "adapt act apte"
					_UpperCAmelCase		:       Union[str, Any]								=      ["adapt", "act", "ap@@", "te"]
					_UpperCAmelCase		:       Optional[Any]								=      tokenizer.tokenize(lowerCamelCase__ )
					self.assertListEqual(lowerCamelCase__	,  lowerCamelCase__ )

					_UpperCAmelCase		:       Dict								=      [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

					_UpperCAmelCase		:       int								=      [0, 1, 2, 3, 4, 5]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ )	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
					assert tok("sam" ).input_ids == [13_84]
					_UpperCAmelCase		:       List[Any]								=      "I am a small frog."
					_UpperCAmelCase		:       List[Any]								=      tok([src_text]	,  padding=lowerCamelCase__	,  truncation=lowerCamelCase__ )["input_ids"]
					_UpperCAmelCase		:       List[Any]								=      tok.batch_decode(lowerCamelCase__	,  skip_special_tokens=lowerCamelCase__	,  clean_up_tokenization_spaces=lowerCamelCase__ )[0]
					assert src_text != decoded  # I wish it did!
					assert decoded == "i am a small frog ."






			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M" )
					_UpperCAmelCase		:       Tuple								=      "I am a small frog ."
					_UpperCAmelCase		:       int								=      "."
					_UpperCAmelCase		:       Optional[int]								=      tok(lowerCamelCase__ )["input_ids"]
					_UpperCAmelCase		:       int								=      tok(lowerCamelCase__ )["input_ids"]

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







'''simple docstring'''


from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .multicontrolnet import MultiControlNetModel
						from .pipeline_controlnet import StableDiffusionControlNetPipeline
						from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
						from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
						from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline



