from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase		:       Optional[int]									=     logging.get_logger(__name__)

__UpperCamelCase		:       Optional[Any]									=     {
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""convbert"""


						def __init__(       self	:				List[Any]    ,		UpperCamelCase__	:				Any=3_0522    ,		UpperCamelCase__	:				Optional[Any]=768    ,		UpperCamelCase__	:				int=12    ,		UpperCamelCase__	:				Any=12    ,		UpperCamelCase__	:				str=3072    ,		UpperCamelCase__	:				Tuple="gelu"    ,		UpperCamelCase__	:				List[str]=0.1    ,		UpperCamelCase__	:				Any=0.1    ,		UpperCamelCase__	:				List[Any]=512    ,		UpperCamelCase__	:				Union[str, Any]=2    ,		UpperCamelCase__	:				List[Any]=0.02    ,		UpperCamelCase__	:				Any=1E-12    ,		UpperCamelCase__	:				str=1    ,		UpperCamelCase__	:				Any=0    ,		UpperCamelCase__	:				Union[str, Any]=2    ,		UpperCamelCase__	:				List[str]=768    ,		UpperCamelCase__	:				Optional[Any]=2    ,		UpperCamelCase__	:				Tuple=9    ,		UpperCamelCase__	:				str=1    ,		UpperCamelCase__	:				Dict=None    ,		**UpperCamelCase__	:				Union[str, Any]    ,		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							vocab_size
												SCREAMING_SNAKE_CASE							:    int							=							hidden_size
												SCREAMING_SNAKE_CASE							:    List[str]							=							num_hidden_layers
												SCREAMING_SNAKE_CASE							:    Tuple							=							num_attention_heads
												SCREAMING_SNAKE_CASE							:    List[Any]							=							intermediate_size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hidden_act
												SCREAMING_SNAKE_CASE							:    Dict							=							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    Any							=							type_vocab_size
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							initializer_range
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							layer_norm_eps
												SCREAMING_SNAKE_CASE							:    int							=							embedding_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							head_ratio
												SCREAMING_SNAKE_CASE							:    List[str]							=							conv_kernel_size
												SCREAMING_SNAKE_CASE							:    Dict							=							num_groups
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							classifier_dropout






class 					lowercase__						(   UpperCamelCase_):


						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





