





"""simple docstring"""






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


A	     =					logging.get_logger(__name__)

A	     =					{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 			__lowercase     (     _UpperCamelCase				, _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''nat'''

					__lowerCAmelCase					=					{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}







					def __init__(  self		,     _UpperCAmelCase=4		,     _UpperCAmelCase=3		,     _UpperCAmelCase=64		,     _UpperCAmelCase=[3, 4, 6, 5]		,     _UpperCAmelCase=[2, 4, 8, 16]		,     _UpperCAmelCase=7		,     _UpperCAmelCase=3.0		,     _UpperCAmelCase=True		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase="gelu"		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=1e-5		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase		,     ):
									super().__init__(**_UpperCAmelCase       )

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






"""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'''




					__lowerCAmelCase					=					0
					__lowerCAmelCase					=					1
					__lowerCAmelCase					=					2






@add_end_docstrings(_UpperCamelCase							)
class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




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







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )
									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.
													__a	:  List[Any]         =					None
													if self.model.config.prefix is not None:
																	__a	:  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.
																	__a	:  Tuple         =					self.XL_PREFIX
													if prefix is not None:
																	# Recalculate some generate_kwargs linked to prefix.
																	__a    ,  __a    ,  __a	:  Optional[int]         =					self._sanitize_parameters(prefix=_UpperCAmelCase		,     **self._forward_params       )
																	__a	:  Dict         =					{**self._preprocess_params, **preprocess_params}
																	__a	:  str         =					{**self._forward_params, **forward_params}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase		,     ):
									__a	:  str         =					{}
									if prefix is not None:
													__a	:  Tuple         =					prefix
									if prefix:
													__a	:  str         =					self.tokenizer(
													    _UpperCAmelCase		,     padding=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase		,     return_tensors=self.framework       )
													__a	:  List[str]         =					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\']'''       )
													__a	:  str         =					handle_long_generation

									preprocess_params.update(_UpperCAmelCase       )
									__a	:  int         =					generate_kwargs

									__a	:  List[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`'''       )
													__a	:  Optional[Any]         =					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`'''       )
													__a	:  List[Any]         =					ReturnType.TENSORS
									if return_type is not None:
													__a	:  Union[str, Any]         =					return_type
									if clean_up_tokenization_spaces is not None:
													__a	:  Optional[int]         =					clean_up_tokenization_spaces

									if stop_sequence is not None:
													__a	:  Any         =					self.tokenizer.encode(_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase       )
													if len(_UpperCAmelCase       ) > 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.'''       )
													__a	:  List[str]         =					stop_sequence_ids[0]

									return preprocess_params, forward_params, postprocess_params







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									# Parse arguments
									if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
													kwargs.update({'''add_space_before_punct_symbol''': True}       )

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







					def __call__(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									return super().__call__(_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=""		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Tuple         =					self.tokenizer(
									    prefix + prompt_text		,     padding=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase		,     return_tensors=self.framework       )
									__a	:  int         =					prompt_text

									if handle_long_generation == "hole":
													__a	:  str         =					inputs['''input_ids'''].shape[-1]
													if "max_new_tokens" in generate_kwargs:
																	__a	:  Tuple         =					generate_kwargs['''max_new_tokens''']
													else:
																	__a	:  List[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:
																	__a	:  Dict         =					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'''       )

																	__a	:  int         =					inputs['''input_ids'''][:, -keep_length:]
																	if "attention_mask" in inputs:
																					__a	:  List[str]         =					inputs['''attention_mask'''][:, -keep_length:]

									return inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									__a	:  str         =					model_inputs['''input_ids''']
									__a	:  Dict         =					model_inputs.get('''attention_mask'''		,     _UpperCAmelCase       )
									# Allow empty prompts
									if input_ids.shape[1] == 0:
													__a	:  str         =					None
													__a	:  List[Any]         =					None
													__a	:  Any         =					1
									else:
													__a	:  List[Any]         =					input_ids.shape[0]
									__a	:  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.
									__a	:  List[str]         =					generate_kwargs.pop('''prefix_length'''		,     0       )
									if prefix_length > 0:
													__a	:  str         =					'''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:
																	__a	:  Tuple         =					generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
																	generate_kwargs["max_length"] += prefix_length
													__a	:  Dict         =					'''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
									__a	:  List[str]         =					self.model.generate(input_ids=_UpperCAmelCase		,     attention_mask=_UpperCAmelCase		,     **_UpperCAmelCase       )
									__a	:  int         =					generated_sequence.shape[0]
									if self.framework == "pt":
													__a	:  Union[str, Any]         =					generated_sequence.reshape(_UpperCAmelCase		,     out_b // in_b		,     *generated_sequence.shape[1:]       )
									elif self.framework == "tf":
													__a	:  Dict         =					tf.reshape(_UpperCAmelCase		,     (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
									return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=ReturnType.FULL_TEXT		,     _UpperCAmelCase=True       ):
									__a	:  Optional[Any]         =					model_outputs['''generated_sequence'''][0]
									__a	:  List[str]         =					model_outputs['''input_ids''']
									__a	:  Optional[Any]         =					model_outputs['''prompt_text''']
									__a	:  str         =					generated_sequence.numpy().tolist()
									__a	:  List[Any]         =					[]
									for sequence in generated_sequence:
													if return_type == ReturnType.TENSORS:
																	__a	:  Union[str, Any]         =					{'''generated_token_ids''': sequence}
													elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																	# Decode text
																	__a	:  List[str]         =					self.tokenizer.decode(
																	    _UpperCAmelCase		,     skip_special_tokens=_UpperCAmelCase		,     clean_up_tokenization_spaces=_UpperCAmelCase		,     )

																	# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																	if input_ids is None:
																					__a	:  Dict         =					0
																	else:
																					__a	:  Any         =					len(
																					    self.tokenizer.decode(
																					        input_ids[0]		,     skip_special_tokens=_UpperCAmelCase		,     clean_up_tokenization_spaces=_UpperCAmelCase		,     )       )

																	if return_type == ReturnType.FULL_TEXT:
																					__a	:  Any         =					prompt_text + text[prompt_length:]
																	else:
																					__a	:  Any         =					text[prompt_length:]

																	__a	:  Dict         =					{'''generated_text''': all_text}
													records.append(_UpperCAmelCase       )

									return records