

"""simple docstring"""





from typing import Mapping

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


_SCREAMING_SNAKE_CASE       :  List[str]        = logging.get_logger(__name__)

class     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						'mt5'
			SCREAMING_SNAKE_CASE_						     =						['past_key_values']






			def __init__(							self     :					Union[str, Any]						,  lowercase_     :					Union[str, Any]=25_0112						,  lowercase_     :					int=512						,  lowercase_     :					Optional[Any]=64						,  lowercase_     :					str=1024						,  lowercase_     :					Optional[Any]=8						,  lowercase_     :					Dict=None						,  lowercase_     :					int=6						,  lowercase_     :					Optional[int]=32						,  lowercase_     :					List[str]=128						,  lowercase_     :					Any=0.1						,  lowercase_     :					Dict=1e-6						,  lowercase_     :					Optional[Any]=1.0						,  lowercase_     :					Union[str, Any]="gated-gelu"						,  lowercase_     :					str=True						,  lowercase_     :					Dict=True						,  lowercase_     :					Tuple="T5Tokenizer"						,  lowercase_     :					str=False						,  lowercase_     :					Any=0						,  lowercase_     :					int=1						,  lowercase_     :					str=0						,  **lowercase_     :					Tuple						,  ):
								super().__init__(
								    is_encoder_decoder=lowercase_						,  tokenizer_class=lowercase_						,  tie_word_embeddings=lowercase_						,  pad_token_id=lowercase_						,  eos_token_id=lowercase_						,  decoder_start_token_id=lowercase_						,  **lowercase_						,  )
								UpperCamelCase__		:    Optional[Any]         =vocab_size
								UpperCamelCase__		:    Union[str, Any]         =d_model
								UpperCamelCase__		:    List[Any]         =d_kv
								UpperCamelCase__		:    str         =d_ff
								UpperCamelCase__		:    Optional[Any]         =num_layers
								UpperCamelCase__		:    Tuple         =(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								UpperCamelCase__		:    Tuple         =num_heads
								UpperCamelCase__		:    List[Any]         =relative_attention_num_buckets
								UpperCamelCase__		:    Optional[Any]         =relative_attention_max_distance
								UpperCamelCase__		:    Union[str, Any]         =dropout_rate
								UpperCamelCase__		:    Union[str, Any]         =layer_norm_epsilon
								UpperCamelCase__		:    Tuple         =initializer_factor
								UpperCamelCase__		:    Any         =feed_forward_proj
								UpperCamelCase__		:    List[str]         =use_cache

								UpperCamelCase__		:    Tuple         =self.feed_forward_proj.split('''-'''						)
								UpperCamelCase__		:    int         =act_info[-1]
								UpperCamelCase__		:    Tuple         =act_info[0] == '''gated'''

								if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
													raise ValueError(
													    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
													    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
													    '''\'gated-gelu\' or \'relu\''''						)

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
													UpperCamelCase__		:    List[str]         ='''gelu_new'''






			@property
			def    _lowerCAmelCase			(							self     :					int						):
								return self.d_model






			@property
			def    _lowerCAmelCase			(							self     :					List[str]						):
								return self.num_heads






			@property
			def    _lowerCAmelCase			(							self     :					str						):
								return self.num_layers



class     __a   (      snake_case__    ):

			"""simple docstring"""

			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def    _lowerCAmelCase			(							self     :					List[str]						):
								UpperCamelCase__		:    Union[str, Any]         ={
								    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
								    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
								}
								if self.use_past:
													UpperCamelCase__		:    int         ='''past_encoder_sequence + sequence'''
													UpperCamelCase__		:    Optional[Any]         ={0: '''batch'''}
													UpperCamelCase__		:    str         ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
													UpperCamelCase__		:    Union[str, Any]         ={0: '''batch''', 1: '''decoder_sequence'''}
													UpperCamelCase__		:    str         ={0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
													self.fill_with_past_key_values_(lowercase_						,  direction='''inputs'''						)

								return common_inputs






			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def    _lowerCAmelCase			(							self     :					Dict						):
								return 13






			@property
			def    _lowerCAmelCase			(							self     :					Union[str, Any]						):
								return 5e-4





"""simple docstring"""





import unittest

from transformers.image_processing_utils import get_size_dict

class     __a   (      unittest.TestCase    ):

			"""simple docstring"""

			def    _lowerCAmelCase			(							self     :					Dict						):
								# Test a dict with the wrong keys raises an error
								UpperCamelCase__		:    str         ={'''wrong_key''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								UpperCamelCase__		:    Any         ={'''height''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								UpperCamelCase__		:    List[str]         ={'''width''': 224, '''shortest_edge''': 224}
								with self.assertRaises(lowercase_						):
													get_size_dict(lowercase_						)

								# Test a dict with the correct keys is returned as is
								UpperCamelCase__		:    int         ={'''height''': 224, '''width''': 224}
								UpperCamelCase__		:    str         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  lowercase_						)

								UpperCamelCase__		:    List[Any]         ={'''shortest_edge''': 224}
								UpperCamelCase__		:    List[str]         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224}						)

								UpperCamelCase__		:    List[Any]         ={'''longest_edge''': 224, '''shortest_edge''': 224}
								UpperCamelCase__		:    Tuple         =get_size_dict(lowercase_						)
								self.assertEqual(lowercase_						,  {'''longest_edge''': 224, '''shortest_edge''': 224}						)

								# Test a single int value which  represents (size, size)
								UpperCamelCase__		:    Optional[int]         =get_size_dict(224						)
								self.assertEqual(lowercase_						,  {'''height''': 224, '''width''': 224}						)

								# Test a single int value which represents the shortest edge
								UpperCamelCase__		:    str         =get_size_dict(224						,  default_to_square=lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224}						)

								# Test a tuple of ints which represents (height, width)
								UpperCamelCase__		:    List[str]         =get_size_dict((150, 200)						)
								self.assertEqual(lowercase_						,  {'''height''': 150, '''width''': 200}						)

								# Test a tuple of ints which represents (width, height)
								UpperCamelCase__		:    Optional[int]         =get_size_dict((150, 200)						,  height_width_order=lowercase_						)
								self.assertEqual(lowercase_						,  {'''height''': 200, '''width''': 150}						)

								# Test an int representing the shortest edge and max_size which represents the longest edge
								UpperCamelCase__		:    int         =get_size_dict(224						,  max_size=256						,  default_to_square=lowercase_						)
								self.assertEqual(lowercase_						,  {'''shortest_edge''': 224, '''longest_edge''': 256}						)

								# Test int with default_to_square=True and max_size fails
								with self.assertRaises(lowercase_						):
													get_size_dict(224						,  max_size=256						,  default_to_square=lowercase_						)


