"""simple docstring"""







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase_    :    Tuple											=						logging.get_logger(__name__)

UpperCamelCase_    :    List[Any]											=						{
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class      _lowercase						(				lowerCamelCase__					):
				lowerCamelCase__									=    'm2m_100'
				lowerCamelCase__									=    ['past_key_values']
				lowerCamelCase__									=    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

				def __init__(       self    :      int    ,      _lowercase    :      List[Any]=1_2_8_1_1_2    ,      _lowercase    :      str=1_0_2_4    ,      _lowercase    :      Dict=1_2    ,      _lowercase    :      Optional[Any]=4_0_9_6    ,      _lowercase    :      List[Any]=1_6    ,      _lowercase    :      int=1_2    ,      _lowercase    :      Union[str, Any]=4_0_9_6    ,      _lowercase    :      Any=1_6    ,      _lowercase    :      Optional[Any]=0.05    ,      _lowercase    :      Union[str, Any]=0.05    ,      _lowercase    :      Any=True    ,      _lowercase    :      List[Any]=True    ,      _lowercase    :      int="relu"    ,      _lowercase    :      Optional[Any]=1_0_2_4    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      List[str]=0.1    ,      _lowercase    :      str=0.0    ,      _lowercase    :      List[str]=0.02    ,      _lowercase    :      Union[str, Any]=2    ,      _lowercase    :      List[str]=True    ,      _lowercase    :      int=1    ,      _lowercase    :      Optional[Any]=0    ,      _lowercase    :      int=2    ,      **_lowercase    :      Tuple    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						d_model
										UpperCAmelCase__   		=						encoder_ffn_dim
										UpperCAmelCase__   		=						encoder_layers
										UpperCAmelCase__   		=						encoder_attention_heads
										UpperCAmelCase__   		=						decoder_ffn_dim
										UpperCAmelCase__   		=						decoder_layers
										UpperCAmelCase__   		=						decoder_attention_heads
										UpperCAmelCase__   		=						dropout
										UpperCAmelCase__   		=						attention_dropout
										UpperCAmelCase__   		=						activation_dropout
										UpperCAmelCase__   		=						activation_function
										UpperCAmelCase__   		=						init_std
										UpperCAmelCase__   		=						encoder_layerdrop
										UpperCAmelCase__   		=						decoder_layerdrop
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						encoder_layers
										UpperCAmelCase__   		=						scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=lowercase_    ,      bos_token_id=lowercase_    ,      eos_token_id=lowercase_    ,      is_encoder_decoder=lowercase_    ,      decoder_start_token_id=lowercase_    ,      **lowercase_    ,      )


class      _lowercase						(				lowerCamelCase__					):

				@property
				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						OrderedDict(
										    [
										        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
										        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
										    ]       )

										if self.use_past:
																UpperCAmelCase__   		=						{0: """batch"""}
																UpperCAmelCase__   		=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """decoder_sequence"""}
																UpperCAmelCase__   		=						{0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
																self.fill_with_past_key_values_(lowercase_    ,      direction="""inputs"""       )
										return common_inputs

				def     A	(       self    :      List[str]    ,      _lowercase    :      Tuple    ,      _lowercase    :      List[str] = -1    ,      _lowercase    :      Union[str, Any] = -1    ,      _lowercase    :      str = False    ,      _lowercase    :      Optional[int] = None    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						compute_effective_axis_dimension(
										    lowercase_    ,      fixed_dimension=OnnxConfig.default_fixed_batch    ,      num_token_to_add=0       )

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										UpperCAmelCase__   		=						tokenizer.num_special_tokens_to_add(lowercase_       )
										UpperCAmelCase__   		=						compute_effective_axis_dimension(
										    lowercase_    ,      fixed_dimension=OnnxConfig.default_fixed_sequence    ,      num_token_to_add=lowercase_       )

										# Generate dummy inputs according to compute batch and sequence
										UpperCAmelCase__   		=						[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
										UpperCAmelCase__   		=						dict(tokenizer(lowercase_    ,      return_tensors=lowercase_       )       )
										return common_inputs

				def     A	(       self    :      List[str]    ,      _lowercase    :      str    ,      _lowercase    :      Union[str, Any] = -1    ,      _lowercase    :      Tuple = -1    ,      _lowercase    :      int = False    ,      _lowercase    :      List[str] = None    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       )

										# Generate decoder inputs
										UpperCAmelCase__   		=						seq_length if not self.use_past else 1
										UpperCAmelCase__   		=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       )
										UpperCAmelCase__   		=						{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
										UpperCAmelCase__   		=						dict(**lowercase_    ,      **lowercase_       )

										if self.use_past:
																if not is_torch_available():
																						raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
																else:
																						import torch
																UpperCAmelCase__		, UpperCAmelCase__   		=						common_inputs["""input_ids"""].shape
																UpperCAmelCase__   		=						common_inputs["""decoder_input_ids"""].shape[1]
																UpperCAmelCase__		, UpperCAmelCase__   		=						self.num_attention_heads
																UpperCAmelCase__   		=						(
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																UpperCAmelCase__   		=						decoder_seq_length + 3
																UpperCAmelCase__   		=						(
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																UpperCAmelCase__   		=						torch.cat(
																    [common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_    ,      lowercase_       )]    ,      dim=1       )

																UpperCAmelCase__   		=						[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																UpperCAmelCase__		, UpperCAmelCase__   		=						self.num_layers
																UpperCAmelCase__   		=						min(lowercase_    ,      lowercase_       )
																UpperCAmelCase__   		=						max(lowercase_    ,      lowercase_       ) - min_num_layers
																UpperCAmelCase__   		=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

																for _ in range(lowercase_       ):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(lowercase_       ),
																						        torch.zeros(lowercase_       ),
																						        torch.zeros(lowercase_       ),
																						        torch.zeros(lowercase_       ),
																						    )       )
																# TODO: test this.
																UpperCAmelCase__   		=						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
																for _ in range(lowercase_    ,      lowercase_       ):
																						common_inputs["past_key_values"].append((torch.zeros(lowercase_       ), torch.zeros(lowercase_       ))       )
										return common_inputs

				lowerCamelCase__									=    _generate_dummy_inputs_for_default_and_seqaseq_lm


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


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

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

