



"""simple docstring"""





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
					raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCAmelCase							:       Any          =       False
__lowerCAmelCase							:       Tuple          =       False
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Namespace     ):


						'''simple docstring'''






						return TrainCommand(__UpperCamelCase     )






class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@staticmethod
		def  UpperCAmelCase__				(       _lowercase )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      parser.add_parser("""train"""				,     help="""CLI tool to train a model on a task.""" )

								train_parser.add_argument(
								    """--train_data"""				,     type=_lowercase				,     required=_lowercase				,     help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""				,     )
								train_parser.add_argument(
								    """--column_label"""				,     type=_lowercase				,     default=0				,     help="""Column of the dataset csv file with example labels.""" )
								train_parser.add_argument(
								    """--column_text"""				,     type=_lowercase				,     default=1				,     help="""Column of the dataset csv file with example texts.""" )
								train_parser.add_argument(
								    """--column_id"""				,     type=_lowercase				,     default=2				,     help="""Column of the dataset csv file with example ids.""" )
								train_parser.add_argument(
								    """--skip_first_row"""				,     action="""store_true"""				,     help="""Skip the first row of the csv file (headers).""" )

								train_parser.add_argument("""--validation_data"""				,     type=_lowercase				,     default=""""""				,     help="""path to validation dataset.""" )
								train_parser.add_argument(
								    """--validation_split"""				,     type=_lowercase				,     default=0.1				,     help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""				,     )

								train_parser.add_argument("""--output"""				,     type=_lowercase				,     default="""./"""				,     help="""path to saved the trained model.""" )

								train_parser.add_argument(
								    """--task"""				,     type=_lowercase				,     default="""text_classification"""				,     help="""Task to train the model on.""" )
								train_parser.add_argument(
								    """--model"""				,     type=_lowercase				,     default="""bert-base-uncased"""				,     help="""Model's name or path to stored model.""" )
								train_parser.add_argument("""--train_batch_size"""				,     type=_lowercase				,     default=3_2				,     help="""Batch size for training.""" )
								train_parser.add_argument("""--valid_batch_size"""				,     type=_lowercase				,     default=6_4				,     help="""Batch size for validation.""" )
								train_parser.add_argument("""--learning_rate"""				,     type=_lowercase				,     default=3E-5				,     help="""Learning rate.""" )
								train_parser.add_argument("""--adam_epsilon"""				,     type=_lowercase				,     default=1E-08				,     help="""Epsilon for Adam optimizer.""" )
								train_parser.set_defaults(func=_lowercase )





		def __init__(       self				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      logging.get_logger("""transformers-cli/training""" )

								snake_case_ :			Dict            =      """tf""" if is_tf_available() else """torch"""

								os.makedirs(args.output				,     exist_ok=_lowercase )
								snake_case_ :			List[Any]            =      args.output

								snake_case_ :			Optional[int]            =      args.column_label
								snake_case_ :			List[str]            =      args.column_text
								snake_case_ :			int            =      args.column_id

								self.logger.info(f'Loading {args.task} pipeline for {args.model}' )
								if args.task == "text_classification":
														snake_case_ :			Any            =      TextClassificationPipeline.from_pretrained(args.model )
								elif args.task == "token_classification":
														raise NotImplementedError
								elif args.task == "question_answering":
														raise NotImplementedError

								self.logger.info(f'Loading dataset from {args.train_data}' )
								snake_case_ :			Tuple            =      Processor.create_from_csv(
								    args.train_data				,     column_label=args.column_label				,     column_text=args.column_text				,     column_id=args.column_id				,     skip_first_row=args.skip_first_row				,     )
								snake_case_ :			Tuple            =      None
								if args.validation_data:
														self.logger.info(f'Loading validation dataset from {args.validation_data}' )
														snake_case_ :			List[Any]            =      Processor.create_from_csv(
														    args.validation_data				,     column_label=args.column_label				,     column_text=args.column_text				,     column_id=args.column_id				,     skip_first_row=args.skip_first_row				,     )

								snake_case_ :			Dict            =      args.validation_split
								snake_case_ :			Tuple            =      args.train_batch_size
								snake_case_ :			List[Any]            =      args.valid_batch_size
								snake_case_ :			List[Any]            =      args.learning_rate
								snake_case_ :			str            =      args.adam_epsilon





		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								if self.framework == "tf":
														return self.run_tf()
								return self.run_torch()





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								raise NotImplementedError





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								self.pipeline.fit(
								    self.train_dataset				,     validation_data=self.valid_dataset				,     validation_split=self.validation_split				,     learning_rate=self.learning_rate				,     adam_epsilon=self.adam_epsilon				,     train_batch_size=self.train_batch_size				,     valid_batch_size=self.valid_batch_size				,     )

								# Save trained pipeline
								self.pipeline.save_pretrained(self.output )





"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					StableDiffusionSAGPipeline
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      torch.manual_seed(0)
      _lowerCamelCase	:					Dict   =		UNetaDConditionModel(
          block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,   cross_attention_dim=32      ,   )
      _lowerCamelCase	:					int   =		DDIMScheduler(
          beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   beta_schedule="""scaled_linear"""      ,   clip_sample=SCREAMING_SNAKE_CASE      ,   set_alpha_to_one=SCREAMING_SNAKE_CASE      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		AutoencoderKL(
          block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,   latent_channels=4      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		CLIPTextConfig(
          bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   )
      _lowerCamelCase	:					List[Any]   =		CLIPTextModel(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      _lowerCamelCase	:					List[Any]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  List[Any]:
      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[str]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					List[str]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		{
          """prompt""": """.""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 1.0,
          """sag_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)







@slow
@require_torch_gpu
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
      _lowerCamelCase	:					Union[str, Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		"""."""
      _lowerCamelCase	:					int   =		torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Dict   =		output.images

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

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Optional[Any]   =		np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[Any]   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Dict   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		"""."""
      _lowerCamelCase	:					List[str]   =		torch.manual_seed(0)
      _lowerCamelCase	:					int   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Any   =		output.images

      _lowerCamelCase	:					List[str]   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Any   =		np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					int   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Optional[Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		"""."""
      _lowerCamelCase	:					Union[str, Any]   =		torch.manual_seed(0)
      _lowerCamelCase	:					Optional[int]   =		sag_pipe(
          [prompt]      ,   width=768      ,   height=512      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		output.images

      assert image.shape == (1, 512, 768, 3)

