






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   Dict		,     __A:   Dict		,     __A:   List[str]=None		,     __A:   Optional[Any]=True		,     __A:   str=None		,     **__A:   List[str]   )    ->				Any:
												_A								= parent
												_A								= config_class
												_A								= has_text_modality
												_A								= kwargs
												_A								= common_properties

							def       __A (  self:   int   )    ->				Optional[int]:
												_A								= self.config_class(**self.inputs_dict   )
												_A								= (
												    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
												    if self.common_properties is None
												    else self.common_properties
												)

												# Add common fields for text models
												if self.has_text_modality:
																	common_properties.extend(['''vocab_size''']   )

												# Test that config has the common properties as getters
												for prop in common_properties:
																	self.parent.assertTrue(hasattr(__A		,     __A   )		,     msg=f"""`{prop}` does not exist"""   )

												# Test that config has the common properties as setter
												for idx, name in enumerate(__A   ):
																	try:
																						setattr(__A		,     __A		,     __A   )
																						self.parent.assertEqual(
																						    getattr(__A		,     __A   )		,     __A		,     msg=f"""`{name} value {idx} expected, but was {getattr(__A		,     __A   )}"""   )
																	except NotImplementedError:
																						# Some models might not be able to implement setters for common_properties
																						# In that case, a NotImplementedError is raised
																						pass

        # Test if config class can be called with Config(prop_name=..)
												for idx, name in enumerate(__A   ):
																	try:
																						_A								= self.config_class(**{name: idx}   )
																						self.parent.assertEqual(
																						    getattr(__A		,     __A   )		,     __A		,     msg=f"""`{name} value {idx} expected, but was {getattr(__A		,     __A   )}"""   )
																	except NotImplementedError:
																						# Some models might not be able to implement setters for common_properties
																						# In that case, a NotImplementedError is raised
																						pass

							def       __A (  self:   Tuple   )    ->				List[str]:
												_A								= self.config_class(**self.inputs_dict   )
												_A								= json.loads(config.to_json_string()   )
												for key, value in self.inputs_dict.items():
																	self.parent.assertEqual(obj[key]		,     __A   )

							def       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
												_A								= self.config_class(**self.inputs_dict   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																	_A								= os.path.join(__A		,     '''config.json'''   )
																	config_first.to_json_file(__A   )
																	_A								= self.config_class.from_json_file(__A   )

												self.parent.assertEqual(config_second.to_dict()		,     config_first.to_dict()   )

							def       __A (  self:   str   )    ->				Optional[Any]:
												_A								= self.config_class(**self.inputs_dict   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																	config_first.save_pretrained(__A   )
																	_A								= self.config_class.from_pretrained(__A   )

												self.parent.assertEqual(config_second.to_dict()		,     config_first.to_dict()   )

							def       __A (  self:   Optional[Any]   )    ->				Dict:
												_A								= self.config_class(**self.inputs_dict   )

												_A								= '''test'''
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_A								= os.path.join(__A		,     __A   )
																	config_first.save_pretrained(__A   )
																	_A								= self.config_class.from_pretrained(__A		,     subfolder=__A   )

												self.parent.assertEqual(config_second.to_dict()		,     config_first.to_dict()   )

							def       __A (  self:   int   )    ->				Tuple:
												_A								= self.config_class(**self.inputs_dict		,     num_labels=5   )
												self.parent.assertEqual(len(config.idalabel   )		,     5   )
												self.parent.assertEqual(len(config.labelaid   )		,     5   )

												_A								= 3
												self.parent.assertEqual(len(config.idalabel   )		,     3   )
												self.parent.assertEqual(len(config.labelaid   )		,     3   )

							def       __A (  self:   List[str]   )    ->				List[Any]:
												if self.config_class.is_composition:
																	return
												_A								= self.config_class()
												self.parent.assertIsNotNone(__A   )

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= copy.deepcopy(__A   )
												_A								= self.config_class(**__A   )
												_A								= []
												for key, value in config_common_kwargs.items():
																	if key == "torch_dtype":
																						if not is_torch_available():
																											continue
																						else:
																											import torch

																											if config.torch_dtype != torch.floataa:
																																wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)   )
																	elif getattr(__A		,     __A   ) != value:
																						wrong_values.append((key, getattr(__A		,     __A   ), value)   )

												if len(__A   ) > 0:
																	_A								= '''\n'''.join([f"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]   )
																	raise ValueError(f"""The following keys were not properly set in the config:\n{errors}"""   )



							def       __A (  self:   List[Any]   )    ->				str:
												self.create_and_test_config_common_properties()
												self.create_and_test_config_to_json_string()
												self.create_and_test_config_to_json_file()
												self.create_and_test_config_from_and_save_pretrained()
												self.create_and_test_config_from_and_save_pretrained_subfolder()
												self.create_and_test_config_with_num_labels()
												self.check_config_can_be_init_without_params()
												self.check_config_arguments_init()











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 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= 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(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

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

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

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



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

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



