

from argparse import ArgumentParser

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


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

def  lowerCamelCase__ (					_a):
	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(_a):
			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  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      str				     =       pipeline(
	    task=args.task							,			model=args.model if args.model else None							,			config=args.config							,			tokenizer=args.tokenizer							,			device=args.device							,			)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       try_infer_format_from_ext(args.input) if args.format == "infer" else args.format
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       PipelineDataFormat.from_str(
	    format=_a							,			output_path=args.output							,			input_path=args.input							,			column=args.column if args.column else nlp.default_input_names							,			overwrite=args.overwrite							,			)
	return RunCommand(_a							,			_a)






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: str      ,      a							: Pipeline      ,      a							: PipelineDataFormat	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nlp
				SCREAMING_SNAKE_CASE   :      List[str]				     =       reader
			@staticmethod
			def 							__UpperCamelCase (			a							: ArgumentParser	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       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 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self._nlp, []

				for entry in self._reader:
					SCREAMING_SNAKE_CASE   :      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:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       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	)


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =None
			lowerCamelCase__           =None
			lowerCamelCase__           =None  # sigma(t_i)
			@classmethod
			def 							__UpperCamelCase (			cls							: Tuple	)							->       int:

				"""simple docstring"""

				return cls()






@dataclass
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42






class _UpperCamelCase							(							__A							,	__A   ):
			'''simple docstring'''







			@property
			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				return True
			@register_to_config
			def __init__(			self							: List[str]      ,      a							: float = 0.02      ,      a							: float = 100      ,      a							: float = 1.007      ,      a							: float = 80      ,      a							: float = 0.05      ,      a							: float = 50      ,      )							->       Optional[Any]:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Dict	)							->       str:

				"""simple docstring"""

				return KarrasVeSchedulerState.create()
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: KarrasVeSchedulerState      ,      a							: int      ,      a							: Tuple = ()	)							->       KarrasVeSchedulerState:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       jnp.arange(0      ,      a	)[::-1].copy()
				SCREAMING_SNAKE_CASE   :      int				     =       [
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=a      ,      schedule=jnp.array(a      ,      dtype=jnp.floataa	)      ,      timesteps=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: random.KeyArray      ,      )							->       Tuple[jnp.ndarray, float]:

				"""simple docstring"""

				if self.config.s_min <= sigma <= self.config.s_max:
					SCREAMING_SNAKE_CASE   :      Dict				     =       min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1	)
				else:
					SCREAMING_SNAKE_CASE   :      Any				     =       0

				# sample eps ~ N(0, S_noise^2 * I)
				SCREAMING_SNAKE_CASE   :      Any				     =       random.split(a      ,      num=1	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.config.s_noise * random.normal(key=a      ,      shape=sample.shape	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sigma + gamma * sigma
				SCREAMING_SNAKE_CASE   :      str				     =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       sample_hat + sigma_hat * model_output
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       (sample_hat - pred_original_sample) / sigma_hat
				SCREAMING_SNAKE_CASE   :      str				     =       sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: KarrasVeSchedulerState      ,      a							: jnp.ndarray      ,      a							: float      ,      a							: float      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: jnp.ndarray      ,      a							: bool = True      ,      )							->       Union[FlaxKarrasVeOutput, Tuple]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       sample_prev + sigma_prev * model_output
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       (sample_prev - pred_original_sample) / sigma_prev
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
					return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=a      ,      derivative=a      ,      state=a	)






			def 							__UpperCamelCase (			self							: int      ,      a							: KarrasVeSchedulerState      ,      a							: Optional[Any]      ,      a							: int      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				raise NotImplementedError()