





from typing import Mapping

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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""umt5"""
			lowerCAmelCase_							     =						["""past_key_values"""]





			def __init__(    self       ,  __lowerCAmelCase=2_5_0_1_1_2       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=6_4       ,  __lowerCAmelCase=1_0_2_4       ,  __lowerCAmelCase=8       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=6       ,  __lowerCAmelCase=3_2       ,  __lowerCAmelCase=1_2_8       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=1E-6       ,  __lowerCAmelCase=1.0       ,  __lowerCAmelCase="gated-gelu"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase="T5Tokenizer"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(
				    is_encoder_decoder=__lowerCAmelCase       ,  tokenizer_class=__lowerCAmelCase       ,  tie_word_embeddings=__lowerCAmelCase       ,  pad_token_id=__lowerCAmelCase       ,  eos_token_id=__lowerCAmelCase       ,  decoder_start_token_id=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )
				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		d_model
				lowerCamelCase__								=		d_kv
				lowerCamelCase__								=		d_ff
				lowerCamelCase__								=		num_layers
				lowerCamelCase__								=		(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				lowerCamelCase__								=		num_heads
				lowerCamelCase__								=		relative_attention_num_buckets
				lowerCamelCase__								=		relative_attention_max_distance
				lowerCamelCase__								=		dropout_rate
				lowerCamelCase__								=		layer_norm_epsilon
				lowerCamelCase__								=		initializer_factor
				lowerCamelCase__								=		feed_forward_proj
				lowerCamelCase__								=		use_cache

				lowerCamelCase__								=		self.feed_forward_proj.split('''-'''							)
				lowerCamelCase__								=		act_info[-1]
				lowerCamelCase__								=		act_info[0] == '''gated'''

				if len(__lowerCAmelCase							) > 1 and act_info[0] != "gated" or len(__lowerCAmelCase							) > 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\''''							)

				if feed_forward_proj == "gated-gelu":
					lowerCamelCase__								=		'''gelu_new'''





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.d_model





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.num_heads





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.num_layers






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
				    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
				}
				if self.use_past:
					lowerCamelCase__								=		'''past_encoder_sequence + sequence'''
					lowerCamelCase__								=		{0: '''batch'''}
					lowerCamelCase__								=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
				else:
					lowerCamelCase__								=		{0: '''batch''', 1: '''decoder_sequence'''}
					lowerCamelCase__								=		{0: '''batch''', 1: '''decoder_sequence'''}

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

				return common_inputs





			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_3





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 5E-4










from collections import OrderedDict
from typing import Mapping

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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(pad_token_id=__lowerCAmelCase       ,  bos_token_id=__lowerCAmelCase       ,  eos_token_id=__lowerCAmelCase       ,  **__lowerCAmelCase							)

				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		position_embedding_type
				lowerCamelCase__								=		use_cache
				lowerCamelCase__								=		classifier_dropout






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				if self.task == "multiple-choice":
					lowerCamelCase__								=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
					lowerCamelCase__								=		{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]							)



