"""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       :    Any         =		logging.get_logger(__name__)

__UpperCamelCase       :    Optional[int]         =		{
    "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   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      """m2m_100"""
    snake_case_											=      ["""past_key_values"""]
    snake_case_											=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
    def __init__(	self			:       List[str]   ,						_lowercase			:       int=1_2_8_1_1_2   ,						_lowercase			:       int=1_0_2_4   ,						_lowercase			:       Union[str, Any]=1_2   ,						_lowercase			:       Any=4_0_9_6   ,						_lowercase			:       List[Any]=1_6   ,						_lowercase			:       Tuple=1_2   ,						_lowercase			:       Optional[int]=4_0_9_6   ,						_lowercase			:       int=1_6   ,						_lowercase			:       Tuple=0.05   ,						_lowercase			:       Union[str, Any]=0.05   ,						_lowercase			:       List[Any]=True   ,						_lowercase			:       Tuple=True   ,						_lowercase			:       Union[str, Any]="relu"   ,						_lowercase			:       Tuple=1_0_2_4   ,						_lowercase			:       Any=0.1   ,						_lowercase			:       Optional[int]=0.1   ,						_lowercase			:       List[str]=0.0   ,						_lowercase			:       Optional[int]=0.02   ,						_lowercase			:       str=2   ,						_lowercase			:       Any=True   ,						_lowercase			:       Optional[Any]=1   ,						_lowercase			:       Optional[Any]=0   ,						_lowercase			:       List[str]=2   ,						**_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       vocab_size
       _lowercase							     =       max_position_embeddings
       _lowercase							     =       d_model
       _lowercase							     =       encoder_ffn_dim
       _lowercase							     =       encoder_layers
       _lowercase							     =       encoder_attention_heads
       _lowercase							     =       decoder_ffn_dim
       _lowercase							     =       decoder_layers
       _lowercase							     =       decoder_attention_heads
       _lowercase							     =       dropout
       _lowercase							     =       attention_dropout
       _lowercase							     =       activation_dropout
       _lowercase							     =       activation_function
       _lowercase							     =       init_std
       _lowercase							     =       encoder_layerdrop
       _lowercase							     =       decoder_layerdrop
       _lowercase							     =       use_cache
       _lowercase							     =       encoder_layers
       _lowercase							     =       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   UpperCAmelCase_	(				lowercase__   ):
    @property
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Mapping[str, Mapping[int, str]]:
       _lowercase							     =       OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]		)

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

       if self.use_past:
          self.fill_with_past_key_values_(_lowercase   ,						direction="inputs"		)
       return common_inputs
    def 		_lowerCamelCase      (	self			:       Optional[Any]   ,						_lowercase			:       PreTrainedTokenizer   ,						_lowercase			:       int = -1   ,						_lowercase			:       int = -1   ,						_lowercase			:       bool = False   ,						_lowercase			:       Optional[TensorType] = None   ,						)						->    Mapping[str, Any]:
       # 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
       _lowercase							     =       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
       _lowercase							     =       tokenizer.num_special_tokens_to_add(_lowercase		)
       _lowercase							     =       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
       _lowercase							     =       [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
       _lowercase							     =       dict(tokenizer(_lowercase   ,						return_tensors=_lowercase		)		)
       return common_inputs
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       PreTrainedTokenizer   ,						_lowercase			:       int = -1   ,						_lowercase			:       int = -1   ,						_lowercase			:       bool = False   ,						_lowercase			:       Optional[TensorType] = None   ,						)						->    Mapping[str, Any]:
       _lowercase							     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase		)

       # Generate decoder inputs
       _lowercase							     =       seq_length if not self.use_past else 1
       _lowercase							     =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase		)
       _lowercase							     =       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       _lowercase							     =       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
          _lowercase     ,  _lowercase							     =       common_inputs["input_ids"].shape
          _lowercase							     =       common_inputs["decoder_input_ids"].shape[1]
          _lowercase     ,  _lowercase							     =       self.num_attention_heads
          _lowercase							     =       (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          _lowercase							     =       decoder_seq_length + 3
          _lowercase							     =       (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

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

          _lowercase							     =       []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          _lowercase     ,  _lowercase							     =       self.num_layers
          _lowercase							     =       min(_lowercase   ,						_lowercase		)
          _lowercase							     =       max(_lowercase   ,						_lowercase		) - min_num_layers
          _lowercase							     =       "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.
          _lowercase							     =       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




    snake_case_											=      _generate_dummy_inputs_for_default_and_seqaseq_lm
"""simple docstring"""


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]