

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

_lowerCAmelCase  :      Any			 =logging.get_logger(__name__)

class __UpperCamelCase     (				enum.Enum		):






					'''simple docstring'''



					__magic_name__        =   0
					__magic_name__        =   1

@add_end_docstrings(A__		)
class __UpperCamelCase     (				A__		):






					'''simple docstring'''



					__magic_name__        =   "generated"





					def __init__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						super().__init__(*snake_case_				,      **snake_case_						)

						self.check_model_type(
						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Dict									= {}
						if truncation is not None:
							UpperCAmelCase__:	Optional[int]									= truncation

						UpperCAmelCase__:	Tuple									= generate_kwargs

						UpperCAmelCase__:	Optional[Any]									= {}
						if return_tensors is not None and return_type is None:
							UpperCAmelCase__:	int									= ReturnType.TENSORS if return_tensors else ReturnType.TEXT
						if return_type is not None:
							UpperCAmelCase__:	Any									= return_type

						if clean_up_tokenization_spaces is not None:
							UpperCAmelCase__:	Optional[Any]									= clean_up_tokenization_spaces

						if stop_sequence is not None:
							UpperCAmelCase__:	Dict									= self.tokenizer.encode(snake_case_				,      add_special_tokens=snake_case_						)
							if len(snake_case_						) > 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."						)
							UpperCAmelCase__:	str									= stop_sequence_ids[0]

						return preprocess_params, forward_params, postprocess_params





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						return True





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= self.model.config.prefix if self.model.config.prefix is not None else ""
						if isinstance(args[0]				,      snake_case_						):
							if self.tokenizer.pad_token_id is None:
								raise ValueError("Please make sure that the tokenizer has a pad_token_id when using a batch input"						)
							UpperCAmelCase__:	Optional[int]									= ([prefix + arg for arg in args[0]],)
							UpperCAmelCase__:	int									= True

						elif isinstance(args[0]				,      snake_case_						):
							UpperCAmelCase__:	int									= (prefix + args[0],)
							UpperCAmelCase__:	Optional[int]									= False
						else:
							raise ValueError(
							    F" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"						)
						UpperCAmelCase__:	Union[str, Any]									= self.tokenizer(*snake_case_				,      padding=snake_case_				,      truncation=snake_case_				,      return_tensors=self.framework						)
						# This is produced by tokenizers but is an invalid generate kwargs
						if "token_type_ids" in inputs:
							del inputs["token_type_ids"]
						return inputs





					def __call__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						UpperCAmelCase__:	List[str]									= super().__call__(*snake_case_				,      **snake_case_						)
						if (
						    isinstance(args[0]				,      snake_case_						)
						    and all(isinstance(snake_case_				,      snake_case_						) for el in args[0]						)
						    and all(len(snake_case_						) == 1 for res in result						)
						):
							return [res[0] for res in result]
						return result





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=TruncationStrategy.DO_NOT_TRUNCATE				,      **lowerCamelCase__						):
						UpperCAmelCase__:	int									= self._parse_and_tokenize(snake_case_				,      truncation=snake_case_				,      **snake_case_						)
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      **lowerCamelCase__						):
						if self.framework == "pt":
							UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= model_inputs["input_ids"].shape
						elif self.framework == "tf":
							UpperCAmelCase__							,						UpperCAmelCase__:	Optional[Any]									= tf.shape(model_inputs["input_ids"]						).numpy()

						UpperCAmelCase__:	Tuple									= generate_kwargs.get("min_length"				,      self.model.config.min_length						)
						UpperCAmelCase__:	Tuple									= generate_kwargs.get("max_length"				,      self.model.config.max_length						)
						self.check_inputs(snake_case_				,      generate_kwargs["min_length"]				,      generate_kwargs["max_length"]						)
						UpperCAmelCase__:	int									= self.model.generate(**snake_case_				,      **snake_case_						)
						UpperCAmelCase__:	Optional[int]									= output_ids.shape[0]
						if self.framework == "pt":
							UpperCAmelCase__:	Optional[int]									= output_ids.reshape(snake_case_				,      out_b // in_b				,      *output_ids.shape[1:]						)
						elif self.framework == "tf":
							UpperCAmelCase__:	int									= tf.reshape(snake_case_				,      (in_b, out_b // in_b, *output_ids.shape[1:])						)
						return {"output_ids": output_ids}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__=ReturnType.TEXT				,      lowerCamelCase__=False						):
						UpperCAmelCase__:	Optional[int]									= []
						for output_ids in model_outputs["output_ids"][0]:
							if return_type == ReturnType.TENSORS:
								UpperCAmelCase__:	int									= {F"{self.return_name}_token_ids": output_ids}
							elif return_type == ReturnType.TEXT:
								UpperCAmelCase__:	Optional[int]									= {
								    F"{self.return_name}_text": self.tokenizer.decode(
								        snake_case_				,      skip_special_tokens=snake_case_				,      clean_up_tokenization_spaces=snake_case_				,      )
								}
							records.append(snake_case_						)
						return records

@add_end_docstrings(A__		)
class __UpperCamelCase     (				A__		):






					'''simple docstring'''



					__magic_name__        =   "summary"





					def __call__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						return super().__call__(*snake_case_				,      **snake_case_						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if max_length < min_length:
							logger.warning(F"Your min_length={min_length} must be inferior than your max_length={max_length}."						)

						if input_length < max_length:
							logger.warning(
							    F"Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is "
							    "a summarization task, where outputs shorter than the input are typically wanted, you might "
							    F"consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"						)

@add_end_docstrings(A__		)
class __UpperCamelCase     (				A__		):






					'''simple docstring'''



					__magic_name__        =   "translation"





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if input_length > 0.9 * max_length:
							logger.warning(
							    F"Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider "
							    "increasing your max_length manually, e.g. translator('...', max_length=400)"						)
						return True





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__				,      lowerCamelCase__=TruncationStrategy.DO_NOT_TRUNCATE				,      lowerCamelCase__=None				,      lowerCamelCase__=None						):
						if getattr(self.tokenizer				,      "_build_translation_inputs"				,      snake_case_						):
							return self.tokenizer._build_translation_inputs(
							    *snake_case_				,      return_tensors=self.framework				,      truncation=snake_case_				,      src_lang=snake_case_				,      tgt_lang=snake_case_						)
						else:
							return super()._parse_and_tokenize(*snake_case_				,      truncation=snake_case_						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      **lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[int]									= super()._sanitize_parameters(**snake_case_						)
						if src_lang is not None:
							UpperCAmelCase__:	Optional[int]									= src_lang
						if tgt_lang is not None:
							UpperCAmelCase__:	int									= tgt_lang
						if src_lang is None and tgt_lang is None:
							# Backward compatibility, direct arguments use is preferred.
							UpperCAmelCase__:	List[str]									= kwargs.get("task"				,      self.task						)
							UpperCAmelCase__:	Any									= task.split("_"						)
							if task and len(snake_case_						) == 4:
								# translation, XX, to YY
								UpperCAmelCase__:	List[Any]									= items[1]
								UpperCAmelCase__:	Optional[Any]									= items[3]
						return preprocess_params, forward_params, postprocess_params





					def __call__(				self				,      *lowerCamelCase__				,      **lowerCamelCase__						):
						return super().__call__(*snake_case_				,      **snake_case_						)

"""simple docstring"""
from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientformer'''




							def __init__( self    ,				snake_case_ = [3, 2, 6, 4]    ,				snake_case_ = [48, 96, 224, 448]    ,				snake_case_ = [True, True, True, True]    ,				snake_case_ = 448    ,				snake_case_ = 32    ,				snake_case_ = 4    ,				snake_case_ = 7    ,				snake_case_ = 5    ,				snake_case_ = 8    ,				snake_case_ = 4    ,				snake_case_ = 0.0    ,				snake_case_ = 16    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 3    ,				snake_case_ = 2    ,				snake_case_ = 1    ,				snake_case_ = 0.0    ,				snake_case_ = 1    ,				snake_case_ = True    ,				snake_case_ = True    ,				snake_case_ = 1e-5    ,				snake_case_ = "gelu"    ,				snake_case_ = 0.02    ,				snake_case_ = 1e-1_2    ,				snake_case_ = 224    ,				snake_case_ = 1e-0_5    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  hidden_sizes
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  depths
													__lowerCAmelCase     =  mlp_expansion_ratio
													__lowerCAmelCase     =  downsamples
													__lowerCAmelCase     =  dim
													__lowerCAmelCase     =  key_dim
													__lowerCAmelCase     =  attention_ratio
													__lowerCAmelCase     =  resolution
													__lowerCAmelCase     =  pool_size
													__lowerCAmelCase     =  downsample_patch_size
													__lowerCAmelCase     =  downsample_stride
													__lowerCAmelCase     =  downsample_pad
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  num_metaad_blocks
													__lowerCAmelCase     =  distillation
													__lowerCAmelCase     =  use_layer_scale
													__lowerCAmelCase     =  layer_scale_init_value
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  batch_norm_eps
