'''simple docstring'''




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

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


__SCREAMING_SNAKE_CASE	:					Union[str, Any]					=  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:					Any					=  {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class      lowerCamelCase_     (snake_case__   ):
						'''simple docstring'''

						__UpperCamelCase:  List[Any]      =						"mbart"
						__UpperCamelCase:  Optional[Any]      =						["past_key_values"]
						__UpperCamelCase:  Tuple      =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(	self						:    Optional[int]						, A						:    Union[str, Any]=50265						, A						:    Union[str, Any]=1024						, A						:    Any=12						, A						:    Tuple=4096						, A						:    Tuple=16						, A						:    Dict=12						, A						:    int=4096						, A						:    List[str]=16						, A						:    List[Any]=0.0						, A						:    List[Any]=0.0						, A						:    str=True						, A						:    int=True						, A						:    List[Any]="gelu"						, A						:    Dict=1024						, A						:    Dict=0.1						, A						:    Optional[int]=0.0						, A						:    int=0.0						, A						:    List[str]=0.02						, A						:    Tuple=0.0						, A						:    List[str]=False						, A						:    Dict=1						, A						:    Dict=0						, A						:    Any=2						, A						:    List[Any]=2						, **A						:    str						, ):
													_UpperCAmelCase					:  Tuple											=   vocab_size
													_UpperCAmelCase					:  Tuple											=   max_position_embeddings
													_UpperCAmelCase					:  Optional[int]											=   d_model
													_UpperCAmelCase					:  Tuple											=   encoder_ffn_dim
													_UpperCAmelCase					:  Dict											=   encoder_layers
													_UpperCAmelCase					:  List[Any]											=   encoder_attention_heads
													_UpperCAmelCase					:  str											=   decoder_ffn_dim
													_UpperCAmelCase					:  List[str]											=   decoder_layers
													_UpperCAmelCase					:  Dict											=   decoder_attention_heads
													_UpperCAmelCase					:  List[str]											=   dropout
													_UpperCAmelCase					:  str											=   attention_dropout
													_UpperCAmelCase					:  Tuple											=   activation_dropout
													_UpperCAmelCase					:  Optional[int]											=   activation_function
													_UpperCAmelCase					:  Tuple											=   init_std
													_UpperCAmelCase					:  int											=   encoder_layerdrop
													_UpperCAmelCase					:  str											=   decoder_layerdrop
													_UpperCAmelCase					:  int											=   classifier_dropout
													_UpperCAmelCase					:  str											=   use_cache
													_UpperCAmelCase					:  Union[str, Any]											=   encoder_layers
													_UpperCAmelCase					:  Any											=   scale_embedding  # scale factor will be sqrt(d_model) if True
													super().__init__(
													    pad_token_id=A						, bos_token_id=A						, eos_token_id=A						, is_encoder_decoder=A						, forced_eos_token_id=A						, **A						, )

class      lowerCamelCase_     (snake_case__   ):
						'''simple docstring'''

						@property
						def 					_A     (	self						:    Tuple	):
													if self.task in ["default", "seq2seq-lm"]:
																				_UpperCAmelCase					:  List[str]											=   OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]	)

																				if self.use_past:
																											_UpperCAmelCase					:  Tuple											=   {0: "batch"}
																											_UpperCAmelCase					:  int											=   {0: "batch", 1: "past_decoder_sequence + sequence"}
																				else:
																											_UpperCAmelCase					:  int											=   {0: "batch", 1: "decoder_sequence"}
																											_UpperCAmelCase					:  Optional[int]											=   {0: "batch", 1: "decoder_sequence"}

																				if self.use_past:
																											self.fill_with_past_key_values_(A						, direction="inputs"	)
													elif self.task == "causal-lm":
																				# TODO: figure this case out.
																				_UpperCAmelCase					:  List[Any]											=   OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]	)
																				if self.use_past:
																											_UpperCAmelCase					:  Optional[int]											=   self.num_layers
																											for i in range(A	):
																																		_UpperCAmelCase					:  str											=   {0: "batch", 2: "past_sequence + sequence"}
																																		_UpperCAmelCase					:  Union[str, Any]											=   {0: "batch", 2: "past_sequence + sequence"}
													else:
																				_UpperCAmelCase					:  Dict											=   OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																				        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																				    ]	)

													return common_inputs




						@property
						def 					_A     (	self						:    int	):
													if self.task in ["default", "seq2seq-lm"]:
																				_UpperCAmelCase					:  Dict											=   super().outputs
													else:
																				_UpperCAmelCase					:  str											=   super(A						, self	).outputs
																				if self.use_past:
																											_UpperCAmelCase					:  Tuple											=   self.num_layers
																											for i in range(A	):
																																		_UpperCAmelCase					:  List[Any]											=   {0: "batch", 2: "past_sequence + sequence"}
																																		_UpperCAmelCase					:  Tuple											=   {0: "batch", 2: "past_sequence + sequence"}
													return common_outputs




						def 					_A     (	self						:    Dict						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
													_UpperCAmelCase					:  Optional[Any]											=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    A						, A						, A						, A						, A	)

													# Generate decoder inputs
													_UpperCAmelCase					:  Union[str, Any]											=   seq_length if not self.use_past else 1
													_UpperCAmelCase					:  Tuple											=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    A						, A						, A						, A						, A	)
													_UpperCAmelCase					:  Dict											=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
													_UpperCAmelCase					:  Tuple											=   dict(**A						, **A	)

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

																				_UpperCAmelCase					:  Tuple											=   torch.cat(
																				    [common_inputs["decoder_attention_mask"], torch.ones(A						, A	)]						, dim=1	)

																				_UpperCAmelCase					:  Dict											=   []
																				# If the number of encoder and decoder layers are present in the model configuration, both are considered
																				_UpperCAmelCase					:  Tuple											=   self.num_layers
																				_UpperCAmelCase					:  Tuple											=   min(A						, A	)
																				_UpperCAmelCase					:  Any											=   max(A						, A	) - min_num_layers
																				_UpperCAmelCase					:  Optional[Any]											=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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




						def 					_A     (	self						:    List[Any]						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
													_UpperCAmelCase					:  Dict											=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    A						, A						, A						, A						, A	)

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
																				else:
																											import torch
																				_UpperCAmelCase					:  Union[str, Any]											=   common_inputs["input_ids"].shape
																				# Not using the same length for past_key_values
																				_UpperCAmelCase					:  Dict											=   seqlen + 2
																				_UpperCAmelCase					:  Tuple											=   self.num_layers
																				_UpperCAmelCase					:  Tuple											=   self.num_attention_heads
																				_UpperCAmelCase					:  Union[str, Any]											=   (
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)

																				_UpperCAmelCase					:  Optional[int]											=   common_inputs["attention_mask"].dtype
																				_UpperCAmelCase					:  Tuple											=   torch.cat(
																				    [common_inputs["attention_mask"], torch.ones(A						, A						, dtype=A	)]						, dim=1	)
																				_UpperCAmelCase					:  List[Any]											=   [
																				    (torch.zeros(A	), torch.zeros(A	)) for _ in range(A	)
																				]
													return common_inputs




						def 					_A     (	self						:    Optional[Any]						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													_UpperCAmelCase					:  Dict											=   compute_effective_axis_dimension(
													    A						, 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					:  int											=   tokenizer.num_special_tokens_to_add(A	)
													_UpperCAmelCase					:  List[Any]											=   compute_effective_axis_dimension(
													    A						, fixed_dimension=OnnxConfig.default_fixed_sequence						, num_token_to_add=A	)

													# Generate dummy inputs according to compute batch and sequence
													_UpperCAmelCase					:  List[Any]											=   [" ".join([tokenizer.unk_token]	) * seq_length] * batch_size
													_UpperCAmelCase					:  Union[str, Any]											=   dict(tokenizer(A						, return_tensors=A	)	)
													return common_inputs




						def 					_A     (	self						:    Dict						, A						:    PreTrainedTokenizer						, A						:    int = -1						, A						:    int = -1						, A						:    bool = False						, A						:    Optional[TensorType] = None						, ):
													if self.task in ["default", "seq2seq-lm"]:
																				_UpperCAmelCase					:  Tuple											=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    A						, batch_size=A						, seq_length=A						, is_pair=A						, framework=A	)

													elif self.task == "causal-lm":
																				_UpperCAmelCase					:  List[str]											=   self._generate_dummy_inputs_for_causal_lm(
																				    A						, batch_size=A						, seq_length=A						, is_pair=A						, framework=A	)
													else:
																				_UpperCAmelCase					:  List[str]											=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																				    A						, batch_size=A						, seq_length=A						, is_pair=A						, framework=A	)

													return common_inputs





						def 					_A     (	self						:    int						, A						:    Optional[Any]						, A						:    List[Any]						, A						:    Optional[Any]						, A						:    str	):
													if self.task in ["default", "seq2seq-lm"]:
																				_UpperCAmelCase					:  Dict											=   super()._flatten_past_key_values_(A						, A						, A						, A	)
													else:
																				_UpperCAmelCase					:  Optional[int]											=   super(A						, self	)._flatten_past_key_values_(
																				    A						, A						, A						, A	)


'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             if isinstance(A						, (str, bytes, type(A	))	):
                    return value
             elif isinstance(A						, (np.character, np.ndarray)	) and np.issubdtype(value.dtype						, np.character	):
                    return value
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # support for torch, tf, jax etc.
             if config.TORCH_AVAILABLE and "torch" in sys.modules:
                    import torch

                    if isinstance(A						, torch.Tensor	):
                           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
             if hasattr(A						, "__array__"	) and not isinstance(A						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

