

"""simple docstring"""


import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf






class  lowercase(					enum.Enum			):







 '''simple docstring'''





 lowercase__			 = 0
 lowercase__			 = 1
 lowercase__			 = 2







@add_end_docstrings(__a			)
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = "\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "


 def __init__(    self:		str,				*a_:		str,				**a_:		Optional[int]		):




   '''simple docstring'''



   super().__init__(*a_,				**a_		)
   self.check_model_type(
       TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING		)
   if "prefix" not in self._preprocess_params:
     # This is very specific. The logic is quite complex and needs to be done
     # as a "default".
     # It also defines both some preprocess_kwargs and generate_kwargs
     # which is why we cannot put them in their respective methods.
     _snake_case							:		Any								=  None
     if self.model.config.prefix is not None:
       _snake_case							:		Tuple								=  self.model.config.prefix
     if prefix is None and self.model.__class__.__name__ in [
         "XLNetLMHeadModel",
         "TransfoXLLMHeadModel",
         "TFXLNetLMHeadModel",
         "TFTransfoXLLMHeadModel",
     ]:
       # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
       _snake_case							:		Dict								=  self.XL_PREFIX
     if prefix is not None:
       # Recalculate some generate_kwargs linked to prefix.
       _snake_case							:		Union[str, Any]								=  self._sanitize_parameters(prefix=a_,				**self._forward_params		)
       _snake_case							:		Optional[Any]								=  {**self._preprocess_params, **preprocess_params}
       _snake_case							:		List[str]								=  {**self._forward_params, **forward_params}


 def 			UpperCamelCase_       (    self:		List[str],				a_:		List[str]=None,				a_:		Union[str, Any]=None,				a_:		Optional[Any]=None,				a_:		List[Any]=None,				a_:		Tuple=None,				a_:		List[str]=None,				a_:		List[Any]=None,				a_:		Union[str, Any]=None,				**a_:		Optional[Any],				):




   '''simple docstring'''



   _snake_case							:		str								=  {}
   if prefix is not None:
     _snake_case							:		int								=  prefix
   if prefix:
     _snake_case							:		int								=  self.tokenizer(
         a_,				padding=a_,				add_special_tokens=a_,				return_tensors=self.framework		)
     _snake_case							:		int								=  prefix_inputs["""input_ids"""].shape[-1]

   if handle_long_generation is not None:
     if handle_long_generation not in {"hole"}:
       raise ValueError(
           f"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
           """ [None, 'hole']"""		)
     _snake_case							:		Union[str, Any]								=  handle_long_generation

   preprocess_params.update(a_		)
   _snake_case							:		Optional[Any]								=  generate_kwargs

   _snake_case							:		Optional[Any]								=  {}
   if return_full_text is not None and return_type is None:
     if return_text is not None:
       raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""		)
     if return_tensors is not None:
       raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""		)
     _snake_case							:		List[str]								=  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
   if return_tensors is not None and return_type is None:
     if return_text is not None:
       raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""		)
     _snake_case							:		Optional[Any]								=  ReturnType.TENSORS
   if return_type is not None:
     _snake_case							:		str								=  return_type
   if clean_up_tokenization_spaces is not None:
     _snake_case							:		List[str]								=  clean_up_tokenization_spaces

   if stop_sequence is not None:
     _snake_case							:		Tuple								=  self.tokenizer.encode(a_,				add_special_tokens=a_		)
     if len(a_		) > 1:
       warnings.warn(
           """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
           """ the stop sequence will be used as the stop sequence string in the interim."""		)
     _snake_case							:		Optional[int]								=  stop_sequence_ids[0]

   return preprocess_params, forward_params, postprocess_params


 def 			UpperCamelCase_       (    self:		Tuple,				*a_:		List[str],				**a_:		str		):




   '''simple docstring'''



   if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
     kwargs.update({"""add_space_before_punct_symbol""": True}		)

   return super()._parse_and_tokenize(*a_,				**a_		)


 def __call__(    self:		Any,				a_:		Optional[Any],				**a_:		Optional[Any]		):




   '''simple docstring'''



   return super().__call__(a_,				**a_		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		Optional[Any],				a_:		List[Any]="",				a_:		Union[str, Any]=None,				**a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.tokenizer(
       prefix + prompt_text,				padding=a_,				add_special_tokens=a_,				return_tensors=self.framework		)
   _snake_case							:		List[str]								=  prompt_text

   if handle_long_generation == "hole":
     _snake_case							:		Union[str, Any]								=  inputs["""input_ids"""].shape[-1]
     if "max_new_tokens" in generate_kwargs:
       _snake_case							:		Dict								=  generate_kwargs["""max_new_tokens"""]
     else:
       _snake_case							:		Any								=  generate_kwargs.get("""max_length""",				self.model.config.max_length		) - cur_len
       if new_tokens < 0:
         raise ValueError("""We cannot infer how many new tokens are expected"""		)
     if cur_len + new_tokens > self.tokenizer.model_max_length:
       _snake_case							:		List[Any]								=  self.tokenizer.model_max_length - new_tokens
       if keep_length <= 0:
         raise ValueError(
             """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
             """ models max length"""		)

       _snake_case							:		List[str]								=  inputs["""input_ids"""][:, -keep_length:]
       if "attention_mask" in inputs:
         _snake_case							:		Union[str, Any]								=  inputs["""attention_mask"""][:, -keep_length:]

   return inputs


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Dict,				**a_:		int		):




   '''simple docstring'''



   _snake_case							:		str								=  model_inputs["""input_ids"""]
   _snake_case							:		Tuple								=  model_inputs.get("""attention_mask""",				a_		)
   # Allow empty prompts
   if input_ids.shape[1] == 0:
     _snake_case							:		Any								=  None
     _snake_case							:		List[str]								=  None
     _snake_case							:		List[str]								=  1
   else:
     _snake_case							:		Union[str, Any]								=  input_ids.shape[0]
   _snake_case							:		List[str]								=  model_inputs.pop("""prompt_text"""		)

   # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
   # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
   _snake_case							:		Dict								=  generate_kwargs.pop("""prefix_length""",				0		)
   if prefix_length > 0:
     _snake_case							:		Optional[Any]								=  """max_new_tokens""" in generate_kwargs or (
         """generation_config""" in generate_kwargs
         and generate_kwargs["""generation_config"""].max_new_tokens is not None
     )
     if not has_max_new_tokens:
       _snake_case							:		Tuple								=  generate_kwargs.get("""max_length"""		) or self.model.config.max_length
       generate_kwargs["max_length"] += prefix_length
     _snake_case							:		List[Any]								=  """min_new_tokens""" in generate_kwargs or (
         """generation_config""" in generate_kwargs
         and generate_kwargs["""generation_config"""].min_new_tokens is not None
     )
     if not has_min_new_tokens and "min_length" in generate_kwargs:
       generate_kwargs["min_length"] += prefix_length

        # BS x SL
   _snake_case							:		str								=  self.model.generate(input_ids=a_,				attention_mask=a_,				**a_		)
   _snake_case							:		Union[str, Any]								=  generated_sequence.shape[0]
   if self.framework == "pt":
     _snake_case							:		List[Any]								=  generated_sequence.reshape(a_,				out_b // in_b,				*generated_sequence.shape[1:]		)
   elif self.framework == "tf":
     _snake_case							:		Any								=  tf.reshape(a_,				(in_b, out_b // in_b, *generated_sequence.shape[1:])		)
   return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}


 def 			UpperCamelCase_       (    self:		Any,				a_:		str,				a_:		Tuple=ReturnType.FULL_TEXT,				a_:		str=True		):




   '''simple docstring'''



   _snake_case							:		int								=  model_outputs["""generated_sequence"""][0]
   _snake_case							:		Optional[Any]								=  model_outputs["""input_ids"""]
   _snake_case							:		Optional[int]								=  model_outputs["""prompt_text"""]
   _snake_case							:		int								=  generated_sequence.numpy().tolist()
   _snake_case							:		str								=  []
   for sequence in generated_sequence:
     if return_type == ReturnType.TENSORS:
       _snake_case							:		Optional[int]								=  {"""generated_token_ids""": sequence}
     elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
       # Decode text
       _snake_case							:		str								=  self.tokenizer.decode(
           a_,				skip_special_tokens=a_,				clean_up_tokenization_spaces=a_,				)

       # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
       if input_ids is None:
         _snake_case							:		Optional[int]								=  0
       else:
         _snake_case							:		List[str]								=  len(
             self.tokenizer.decode(
                 input_ids[0],				skip_special_tokens=a_,				clean_up_tokenization_spaces=a_,				)		)

       if return_type == ReturnType.FULL_TEXT:
         _snake_case							:		List[str]								=  prompt_text + text[prompt_length:]
       else:
         _snake_case							:		Optional[Any]								=  text[prompt_length:]

       _snake_case							:		Any								=  {"""generated_text""": all_text}
     records.append(a_		)

   return records








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = "swin"

 lowercase__			 = {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(    self:		Any,				a_:		List[str]=224,				a_:		List[Any]=4,				a_:		List[Any]=3,				a_:		Dict=96,				a_:		List[str]=[2, 2, 6, 2],				a_:		int=[3, 6, 12, 24],				a_:		int=7,				a_:		str=4.0,				a_:		Optional[Any]=True,				a_:		Dict=0.0,				a_:		List[Any]=0.0,				a_:		List[str]=0.1,				a_:		Union[str, Any]="gelu",				a_:		Dict=False,				a_:		Union[str, Any]=0.02,				a_:		Optional[int]=1E-5,				a_:		Optional[int]=32,				a_:		Tuple=None,				a_:		Union[str, Any]=None,				**a_:		Any,				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Any								=  image_size
   _snake_case							:		List[Any]								=  patch_size
   _snake_case							:		Tuple								=  num_channels
   _snake_case							:		str								=  embed_dim
   _snake_case							:		Union[str, Any]								=  depths
   _snake_case							:		int								=  len(a_		)
   _snake_case							:		Union[str, Any]								=  num_heads
   _snake_case							:		List[str]								=  window_size
   _snake_case							:		str								=  mlp_ratio
   _snake_case							:		Union[str, Any]								=  qkv_bias
   _snake_case							:		Dict								=  hidden_dropout_prob
   _snake_case							:		str								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  drop_path_rate
   _snake_case							:		Optional[int]								=  hidden_act
   _snake_case							:		str								=  use_absolute_embeddings
   _snake_case							:		Tuple								=  layer_norm_eps
   _snake_case							:		List[Any]								=  initializer_range
   _snake_case							:		Optional[Any]								=  encoder_stride
   # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   _snake_case							:		Any								=  int(embed_dim * 2 ** (len(a_		) - 1)		)
   _snake_case							:		Any								=  ["""stem"""] + [f"stage{idx}" for idx in range(1,				len(a_		) + 1		)]
   _snake_case		, _snake_case							:		List[str]								=  get_aligned_output_features_output_indices(
       out_features=a_,				out_indices=a_,				stage_names=self.stage_names		)







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = version.parse("1.11"			)


 @property
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]		)


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return 1E-4


