

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


_a									=  logging.get_logger(__name__)

_a									=  {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class 				A_					(						__lowerCamelCase   ):
     _lowercase     :	Optional[Any]        =      'blenderbot'
     _lowercase     :	List[Any]        =      ['past_key_values']
     _lowercase     :	Dict        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						Tuple     ,    UpperCAmelCase     :						int=8_0_0_8     ,    UpperCAmelCase     :						Tuple=1_2_8     ,    UpperCAmelCase     :						str=2     ,    UpperCAmelCase     :						int=1_0_2_4_0     ,    UpperCAmelCase     :						str=3_2     ,    UpperCAmelCase     :						List[Any]=2_4     ,    UpperCAmelCase     :						str=1_0_2_4_0     ,    UpperCAmelCase     :						Union[str, Any]=3_2     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Any=0.0     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Optional[Any]=True     ,    UpperCAmelCase     :						Optional[Any]="gelu"     ,    UpperCAmelCase     :						int=2_5_6_0     ,    UpperCAmelCase     :						List[str]=0.1     ,    UpperCAmelCase     :						Tuple=0.0     ,    UpperCAmelCase     :						Dict=0.0     ,    UpperCAmelCase     :						Optional[int]=0.02     ,    UpperCAmelCase     :						Tuple=1     ,    UpperCAmelCase     :						Tuple=False     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Dict=1     ,    UpperCAmelCase     :						int=2     ,    UpperCAmelCase     :						int=3     ,    UpperCAmelCase     :						Optional[int]=2     ,    **UpperCAmelCase     :						int     ,    )				->      str:
      __lowerCAmelCase:  List[Any]        = vocab_size
      __lowerCAmelCase:  List[str]        = max_position_embeddings
      __lowerCAmelCase:  str        = d_model
      __lowerCAmelCase:  Dict        = encoder_ffn_dim
      __lowerCAmelCase:  Tuple        = encoder_layers
      __lowerCAmelCase:  Dict        = encoder_attention_heads
      __lowerCAmelCase:  Dict        = decoder_ffn_dim
      __lowerCAmelCase:  int        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  List[Any]        = dropout
      __lowerCAmelCase:  Dict        = attention_dropout
      __lowerCAmelCase:  List[str]        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Tuple        = init_std
      __lowerCAmelCase:  str        = encoder_layerdrop
      __lowerCAmelCase:  List[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Any        = use_cache
      __lowerCAmelCase:  str        = encoder_layers
      __lowerCAmelCase:  Optional[int]        = 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     ,    encoder_no_repeat_ngram_size=_lowercase     ,    forced_eos_token_id=_lowercase     ,    **_lowercase     ,    )







class 				A_					(						__lowerCamelCase   ):


     @property
     def     UpperCAmelCase    (				self     :						Any   )				->      Any:
      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:  str        = OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
               ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
           ]   )
       if self.use_past:
        __lowerCAmelCase:  Optional[Any]        = {0: 'batch'}
        __lowerCAmelCase:  Any        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
       else:
        __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}
        __lowerCAmelCase:  int        = {0: 'batch', 1: 'decoder_sequence'}
       if self.use_past:
        self.fill_with_past_key_values_(_lowercase     ,    direction='inputs'   )
      elif self.task == "causal-lm":
       __lowerCAmelCase:  Optional[Any]        = OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
               ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
           ]   )
       if self.use_past:
        __lowerCAmelCase      ,				__lowerCAmelCase:  Union[str, Any]        = self.num_layers
        for i in range(_lowercase   ):
         __lowerCAmelCase:  Tuple        = {0: 'batch', 2: 'past_sequence + sequence'}
         __lowerCAmelCase:  Optional[int]        = {0: 'batch', 2: 'past_sequence + sequence'}
      else:
       __lowerCAmelCase:  int        = 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
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def     UpperCAmelCase    (				self     :						str   )				->      Optional[int]:
      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:  Optional[int]        = super().outputs
      else:
       __lowerCAmelCase:  Any        = super(_lowercase     ,    self   ).outputs
       if self.use_past:
        __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[int]        = self.num_layers
        for i in range(_lowercase   ):
         __lowerCAmelCase:  Optional[int]        = {0: 'batch', 2: 'past_sequence + sequence'}
         __lowerCAmelCase:  int        = {0: 'batch', 2: 'past_sequence + sequence'}
      return common_outputs


     def     UpperCAmelCase    (				self     :						Dict     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      int:
      __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase   )
      # Generate decoder inputs
      __lowerCAmelCase:  List[Any]        = seq_length if not self.use_past else 1
      __lowerCAmelCase:  Any        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase   )
      __lowerCAmelCase:  List[str]        = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase:  Dict        = 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
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  List[Any]        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  int        = self.num_attention_heads
       __lowerCAmelCase:  int        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  List[Any]        = decoder_seq_length
       __lowerCAmelCase:  Tuple        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )
       __lowerCAmelCase:  Union[str, Any]        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(_lowercase     ,    _lowercase   )]     ,    dim=1   )
       __lowerCAmelCase:  str        = []
       __lowerCAmelCase      ,				__lowerCAmelCase:  Any        = self.num_layers

       for _ in range(_lowercase   ):
        common_inputs["past_key_values"].append(
            (
                torch.zeros(_lowercase   ),
                torch.zeros(_lowercase   ),
                torch.zeros(_lowercase   ),
                torch.zeros(_lowercase   ),
            )   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Any:
      __lowerCAmelCase:  Optional[Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          _lowercase     ,    _lowercase     ,    _lowercase     ,    _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
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  List[str]        = seqlen
       __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[Any]        = self.num_layers
       __lowerCAmelCase      ,				__lowerCAmelCase:  int        = self.num_attention_heads
       __lowerCAmelCase:  Dict        = (
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = common_inputs['attention_mask'].dtype
       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['attention_mask'], torch.ones(_lowercase     ,    _lowercase     ,    dtype=_lowercase   )]     ,    dim=1   )
       __lowerCAmelCase:  List[Any]        = [
           (torch.zeros(_lowercase   ), torch.zeros(_lowercase   )) for _ in range(_lowercase   )
       ]
      return common_inputs


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Union[str, Any]:
      __lowerCAmelCase:  Any        = 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
      __lowerCAmelCase:  List[Any]        = tokenizer.num_special_tokens_to_add(_lowercase   )
      __lowerCAmelCase:  int        = 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
      __lowerCAmelCase:  List[Any]        = [' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
      __lowerCAmelCase:  Any        = dict(tokenizer(_lowercase     ,    return_tensors=_lowercase   )   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Union[str, Any]     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Dict:
      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:  str        = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           _lowercase     ,    batch_size=_lowercase     ,    seq_length=_lowercase     ,    is_pair=_lowercase     ,    framework=_lowercase   )

      elif self.task == "causal-lm":
       __lowerCAmelCase:  str        = self._generate_dummy_inputs_for_causal_lm(
           _lowercase     ,    batch_size=_lowercase     ,    seq_length=_lowercase     ,    is_pair=_lowercase     ,    framework=_lowercase   )
      else:
       __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowercase     ,    batch_size=_lowercase     ,    seq_length=_lowercase     ,    is_pair=_lowercase     ,    framework=_lowercase   )

      return common_inputs


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						List[Any]     ,    UpperCAmelCase     :						Union[str, Any]     ,    UpperCAmelCase     :						Dict   )				->      List[Any]:
      if self.task in ["default", "seq2seq-lm"]:
       __lowerCAmelCase:  str        = super()._flatten_past_key_values_(_lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase   )
      else:
       __lowerCAmelCase:  List[Any]        = super(_lowercase     ,    self   )._flatten_past_key_values_(
           _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase   )


     def     UpperCAmelCase    (				self     :						Any     ,    UpperCAmelCase     :						Mapping[str, Mapping[int, str]]     ,    UpperCAmelCase     :						str   )				->      str:
      if direction not in ["inputs", "outputs"]:
       raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''   )

      __lowerCAmelCase:  str        = 'past_key_values' if direction == 'inputs' else 'present'
      __lowerCAmelCase      ,				__lowerCAmelCase:  Optional[int]        = self.num_layers

      __lowerCAmelCase:  Dict        = 'past_encoder_sequence'
      __lowerCAmelCase:  Union[str, Any]        = 'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

      for i in range(_lowercase   ):
       __lowerCAmelCase:  Optional[int]        = {0: 'batch', 2: decoder_sequence}
       __lowerCAmelCase:  int        = {0: 'batch', 2: decoder_sequence}
       __lowerCAmelCase:  List[str]        = {0: 'batch', 2: encoder_sequence}
       __lowerCAmelCase:  List[str]        = {0: 'batch', 2: encoder_sequence}






"""simple docstring"""







import shutil

import requests


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      bool = False		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		"""https://api.nasa.gov/planetary/apod"""
						return requests.get(UpperCAmelCase__		,      params={"""api_key""": api_key}      ).json()


def 	a__   (		UpperCAmelCase__							:      str		,      UpperCAmelCase__							:      str = "."      )->						dict:
						UpperCAmelCase__       						=		get_apod_data(UpperCAmelCase__      )
						UpperCAmelCase__       						=		apod_data["""url"""]
						UpperCAmelCase__       						=		img_url.split("""/"""      )[-1]
						UpperCAmelCase__       						=		requests.get(UpperCAmelCase__		,      stream=UpperCAmelCase__      )

						with open(f'{path}/{img_name}'		,      """wb+"""      ) as img_file:
												shutil.copyfileobj(response.raw		,      UpperCAmelCase__      )
						del response
						return apod_data


def 	a__   (		UpperCAmelCase__							:      str      )->						dict:
						UpperCAmelCase__       						=		"""https://images-api.nasa.gov/search"""
						return requests.get(UpperCAmelCase__		,      params={"""q""": query}      ).json()


if __name__ == "__main__":
						print(save_apod("""YOUR API KEY"""))
						UpperCamelCase_							:							Optional[Any]				 =		get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
						print(apollo_aaaa_items[0]["""data"""][0]["""description"""])

