

'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def   a__			(    a__		):




     """simple docstring"""


     return DownloadCommand(args.model ,       args.cache_dir ,       args.force ,       args.trust_remote_code		)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @staticmethod
 def  UpperCAmelCase__						(						__SCREAMING_SNAKE_CASE   :			ArgumentParser     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parser.add_parser("""download"""     )
      download_parser.add_argument(
          """--cache-dir"""   ,				type=__SCREAMING_SNAKE_CASE   ,				default=__SCREAMING_SNAKE_CASE   ,				help="""Path to location to store the models"""     )
      download_parser.add_argument(
          """--force"""   ,				action="""store_true"""   ,				help="""Force the model to be download even if already in cache-dir"""     )
      download_parser.add_argument(
          """--trust-remote-code"""   ,				action="""store_true"""   ,				help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,				)
      download_parser.add_argument("""model"""   ,				type=__SCREAMING_SNAKE_CASE   ,				help="""Name of the model to download"""     )
      download_parser.set_defaults(func=__SCREAMING_SNAKE_CASE     )
 def __init__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			bool   ,				__SCREAMING_SNAKE_CASE   :			bool     )    ->					Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = model
      __SCREAMING_SNAKE_CASE      = cache
      __SCREAMING_SNAKE_CASE      = force
      __SCREAMING_SNAKE_CASE      = trust_remote_code


 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      from ..models.auto import AutoModel, AutoTokenizer

      AutoModel.from_pretrained(
          self._model   ,				cache_dir=self._cache   ,				force_download=self._force   ,				trust_remote_code=self._trust_remote_code     )
      AutoTokenizer.from_pretrained(
          self._model   ,				cache_dir=self._cache   ,				force_download=self._force   ,				trust_remote_code=self._trust_remote_code     )









'''simple docstring'''



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

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


UpperCAmelCase					:			str       =	logging.get_logger(__name__)

UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "blenderbot-small"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=50_265   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			Tuple=8   ,				__SCREAMING_SNAKE_CASE   :			str=2_048   ,				__SCREAMING_SNAKE_CASE   :			Tuple=16   ,				__SCREAMING_SNAKE_CASE   :			Dict=8   ,				__SCREAMING_SNAKE_CASE   :			Dict=2_048   ,				__SCREAMING_SNAKE_CASE   :			str=16   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Tuple=512   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.0   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Any=1   ,				__SCREAMING_SNAKE_CASE   :			Any=False   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=2   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					Union[str, Any]:


      """simple docstring"""




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

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )

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

           if self.use_past:
                self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE   ,				direction="""inputs"""     )
      elif self.task == "causal-lm":
           # TODO: figure this case out.
           __SCREAMING_SNAKE_CASE      = OrderedDict(
               [
                   ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                   ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
               ]     )
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE      = 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  UpperCAmelCase__						(						self   :			Dict     )    ->					Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super().outputs
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     ).outputs
           if self.use_past:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.num_layers
                for i in range(__SCREAMING_SNAKE_CASE     ):
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
                     __SCREAMING_SNAKE_CASE      = {0: """batch""", 2: """past_sequence + sequence"""}
      return common_outputs
 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

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

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

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

           for _ in range(__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                        torch.zeros(__SCREAMING_SNAKE_CASE     ),
                    )     )
           # TODO: test this.
           __SCREAMING_SNAKE_CASE      = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
           for _ in range(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                common_inputs["past_key_values"].append((torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     ))     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

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

           __SCREAMING_SNAKE_CASE      = common_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE      = torch.cat(
               [common_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )]   ,				dim=1     )
           __SCREAMING_SNAKE_CASE      = [
               (torch.zeros(__SCREAMING_SNAKE_CASE     ), torch.zeros(__SCREAMING_SNAKE_CASE     )) for _ in range(__SCREAMING_SNAKE_CASE     )
           ]
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				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
      __SCREAMING_SNAKE_CASE      = tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = compute_effective_axis_dimension(
          __SCREAMING_SNAKE_CASE   ,				fixed_dimension=OnnxConfig.default_fixed_sequence   ,				num_token_to_add=__SCREAMING_SNAKE_CASE     )

      # Generate dummy inputs according to compute batch and sequence
      __SCREAMING_SNAKE_CASE      = [""" """.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      __SCREAMING_SNAKE_CASE      = dict(tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )     )
      return common_inputs
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			PreTrainedTokenizer   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			int = -1   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			Optional[TensorType] = None   ,				)    ->					Mapping[str, Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      elif self.task == "causal-lm":
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_causal_lm(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               __SCREAMING_SNAKE_CASE   ,				batch_size=__SCREAMING_SNAKE_CASE   ,				seq_length=__SCREAMING_SNAKE_CASE   ,				is_pair=__SCREAMING_SNAKE_CASE   ,				framework=__SCREAMING_SNAKE_CASE     )

      return common_inputs


 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if self.task in ["default", "seq2seq-lm"]:
           __SCREAMING_SNAKE_CASE      = super()._flatten_past_key_values_(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = super(__SCREAMING_SNAKE_CASE   ,				self     )._flatten_past_key_values_(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






