





from argparse import ArgumentParser

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


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

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Any:
   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   __snake_case    (       _lowerCAmelCase			:			Optional[Any]						)			->    str:
   A_       :					Dict        =      pipeline(
       task=args.task					,					model=args.model if args.model else None					,					config=args.config					,					tokenizer=args.tokenizer					,					device=args.device					,					)
   A_       :					Dict        =      try_infer_format_from_ext(args.input						) if args.format == "infer" else args.format
   A_       :					List[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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def __init__(				self							:Optional[Any]      ,   snake_case							:Pipeline      ,   snake_case							:PipelineDataFormat	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      nlp
        A_       :					Tuple        =      reader



     @staticmethod
     def     SCREAMING_SNAKE_CASE		(				snake_case							:ArgumentParser	):





        '''simple docstring'''




        A_       :					Tuple        =      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=snake_case      ,   help="Path to the file to use for inference"	)
        run_parser.add_argument("--output"      ,   type=snake_case      ,   help="Path to the file that will be used post to write results."	)
        run_parser.add_argument("--model"      ,   type=snake_case      ,   help="Name or path to the model to instantiate."	)
        run_parser.add_argument("--config"      ,   type=snake_case      ,   help="Name or path to the model's config to instantiate."	)
        run_parser.add_argument(
            "--tokenizer"      ,   type=snake_case      ,   help="Name of the tokenizer to use. (default: same as the model name)"	)
        run_parser.add_argument(
            "--column"      ,   type=snake_case      ,   help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,   )
        run_parser.add_argument(
            "--format"      ,   type=snake_case      ,   default="infer"      ,   choices=PipelineDataFormat.SUPPORTED_FORMATS      ,   help="Input format to read from"      ,   )
        run_parser.add_argument(
            "--device"      ,   type=snake_case      ,   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=snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_   ,    A_       :					str        =      self._nlp, []

        for entry in self._reader:
           A_       :					Optional[int]        =      nlp(**snake_case	) if self._reader.is_multi_columns else nlp(snake_case	)
           if isinstance(snake_case      ,   snake_case	):
              outputs.append(snake_case	)
           else:
              outputs += output

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







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



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





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
