



from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_snake_case		=  logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if not path:
														return "pipe"

							for ext in PipelineDataFormat.SUPPORTED_FORMATS:
														if path.endswith(_lowerCamelCase  ):
																					return ext

							raise Exception(
							    F"Unable to determine file format from file extension {path}. "
							    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"  )






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    pipeline(
							    task=args.task				, model=args.model if args.model else None				, config=args.config				, tokenizer=args.tokenizer				, device=args.device				, )
							_lowerCAmelCase     :							int    =    try_infer_format_from_ext(args.input  ) if args.format == "infer" else args.format
							_lowerCAmelCase     :							Any    =    PipelineDataFormat.from_str(
							    format=_lowerCamelCase				, output_path=args.output				, input_path=args.input				, column=args.column if args.column else nlp.default_input_names				, overwrite=args.overwrite				, )
							return RunCommand(_lowerCamelCase				, _lowerCamelCase  )



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    nlp
														_lowerCAmelCase     :							Tuple    =    reader







							@staticmethod
							def    snake_case__      (   __a):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    parser.add_parser("run",			help="Run a pipeline through the CLI")
														run_parser.add_argument("--task",			choices=get_supported_tasks(),			help="Task to run")
														run_parser.add_argument("--input",			type=__a,			help="Path to the file to use for inference")
														run_parser.add_argument("--output",			type=__a,			help="Path to the file that will be used post to write results.")
														run_parser.add_argument("--model",			type=__a,			help="Name or path to the model to instantiate.")
														run_parser.add_argument("--config",			type=__a,			help="Name or path to the model's config to instantiate.")
														run_parser.add_argument(
														    "--tokenizer",			type=__a,			help="Name of the tokenizer to use. (default: same as the model name)")
														run_parser.add_argument(
														    "--column",			type=__a,			help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)",			)
														run_parser.add_argument(
														    "--format",			type=__a,			default="infer",			choices=PipelineDataFormat.SUPPORTED_FORMATS,			help="Input format to read from",			)
														run_parser.add_argument(
														    "--device",			type=__a,			default=-1,			help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)",			)
														run_parser.add_argument("--overwrite",			action="store_true",			help="Allow overwriting the output file.")
														run_parser.set_defaults(func=__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    self._nlp, []

														for entry in self._reader:
																					_lowerCAmelCase     :							Any    =    nlp(**__a) if self._reader.is_multi_columns else nlp(__a)
																					if isinstance(__a,			__a):
																												outputs.append(__a)
																					else:
																												outputs += output

        # Saving data
														if self._nlp.binary_output:
																					_lowerCAmelCase     :							Tuple    =    self._reader.save_binary(__a)
																					logger.warning(f"Current pipeline requires output to be in binary format, saving at {binary_path}")
														else:
																					self._reader.save(__a)





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
