






import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict

@dataclass
class 		_snake_case  (  A__					):
			_lowercase		:    Optional[int]			     =							ComputeEnvironment.AMAZON_SAGEMAKER
			_lowercase		:    int			     =							True
			_lowercase		:    str			     =							'''ml.p3.2xlarge'''
			_lowercase		:    List[str]			     =							'''accelerate_sagemaker_execution_role'''
			_lowercase		:    Optional[Any]			     =							'''hf-sm'''
			_lowercase		:    List[Any]			     =							'''us-east-1'''
			_lowercase		:    str			     =							1
			_lowercase		:    str			     =							'''accelerate-sagemaker-1'''
			_lowercase		:    Union[str, Any]			     =							'''1.6'''
			_lowercase		:    Optional[int]			     =							'''4.4'''
			_lowercase		:    str			     =							'''train.py'''
			_lowercase		:    Union[str, Any]			     =							[
			    '''--model_name_or_path''',
			    '''bert''',
			    '''--do_train''',
			    '''False''',
			    '''--epochs''',
			    '''3''',
			    '''--learning_rate''',
			    '''5e-5''',
			    '''--max_steps''',
			    '''50.5''',
			]
			_lowercase		:    Dict			     =							[
			    '''--model_name_or_path''',
			    '''bert''',
			    '''--do_train''',
			    '''--do_test''',
			    '''False''',
			    '''--do_predict''',
			    '''--epochs''',
			    '''3''',
			    '''--learning_rate''',
			    '''5e-5''',
			    '''--max_steps''',
			    '''50.5''',
			]



class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						SCREAMING_SNAKE_CASE						= _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
						assert isinstance(converted_args['model_name_or_path']		,					a)
						assert isinstance(converted_args['do_train']		,					a)
						assert isinstance(converted_args['epochs']		,					a)
						assert isinstance(converted_args['learning_rate']		,					a)
						assert isinstance(converted_args['max_steps']		,					a)

						with pytest.raises(a):
									_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)












from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




