'''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
lowercase_     		= False
lowercase_     		= False



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return TrainCommand(a__      )


class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@staticmethod
		def 		UpperCamelCase__       (   __A				) ->     Tuple:
								_lowerCAmelCase										=parser.add_parser('train'		,						help='CLI tool to train a model on a task.'				)

								train_parser.add_argument(
								    '--train_data'		,						type=__A		,						required=__A		,						help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,						)
								train_parser.add_argument(
								    '--column_label'		,						type=__A		,						default=0		,						help='Column of the dataset csv file with example labels.'				)
								train_parser.add_argument(
								    '--column_text'		,						type=__A		,						default=1		,						help='Column of the dataset csv file with example texts.'				)
								train_parser.add_argument(
								    '--column_id'		,						type=__A		,						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=__A		,						default=''		,						help='path to validation dataset.'				)
								train_parser.add_argument(
								    '--validation_split'		,						type=__A		,						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=__A		,						default='./'		,						help='path to saved the trained model.'				)

								train_parser.add_argument(
								    '--task'		,						type=__A		,						default='text_classification'		,						help='Task to train the model on.'				)
								train_parser.add_argument(
								    '--model'		,						type=__A		,						default='bert-base-uncased'		,						help='Model\'s name or path to stored model.'				)
								train_parser.add_argument('--train_batch_size'		,						type=__A		,						default=32		,						help='Batch size for training.'				)
								train_parser.add_argument('--valid_batch_size'		,						type=__A		,						default=64		,						help='Batch size for validation.'				)
								train_parser.add_argument('--learning_rate'		,						type=__A		,						default=3E-5		,						help='Learning rate.'				)
								train_parser.add_argument('--adam_epsilon'		,						type=__A		,						default=1E-08		,						help='Epsilon for Adam optimizer.'				)
								train_parser.set_defaults(func=__A				)






		def __init__(   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=logging.get_logger('transformers-cli/training'				)

								_lowerCAmelCase										='tf' if is_tf_available() else 'torch'

								os.makedirs(args.output		,						exist_ok=__A				)
								_lowerCAmelCase										=args.output

								_lowerCAmelCase										=args.column_label
								_lowerCAmelCase										=args.column_text
								_lowerCAmelCase										=args.column_id

								self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''				)
								if args.task == "text_classification":
														_lowerCAmelCase										=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}'''				)
								_lowerCAmelCase										=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		,						)
								_lowerCAmelCase										=None
								if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''				)
														_lowerCAmelCase										=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		,						)

								_lowerCAmelCase										=args.validation_split
								_lowerCAmelCase										=args.train_batch_size
								_lowerCAmelCase										=args.valid_batch_size
								_lowerCAmelCase										=args.learning_rate
								_lowerCAmelCase										=args.adam_epsilon






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								if self.framework == "tf":
														return self.run_tf()
								return self.run_torch()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								raise NotImplementedError






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								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 random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      List[Any]       =							IFPipeline
		lowercase   :      Tuple       =							TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
		lowercase   :      Union[str, Any]       =							TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase   :      int       =							PipelineTesterMixin.required_optional_params - {'latents'}






		def 		UpperCamelCase__       (   self				) ->     str:
								return self._get_dummy_components()






		def 		UpperCamelCase__       (   self		,						__A		,						__A=0				) ->     int:
								if str(__A				).startswith('mps'				):
														_lowerCAmelCase										=torch.manual_seed(__A				)
								else:
														_lowerCAmelCase										=torch.Generator(device=__A				).manual_seed(__A				)

								_lowerCAmelCase										={
								    'prompt': 'A painting of a squirrel eating a burger',
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								self._test_save_load_optional_components()






		@unittest.skipIf(torch_device != 'cuda'		,						reason='float16 requires CUDA'				)
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)






		def 		UpperCamelCase__       (   self				) ->     str:
								self._test_save_load_local()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2		,						)






		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()		,						reason='XFormers attention is only available with CUDA and `xformers` installed'		,						)
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

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







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								# if

								_lowerCAmelCase										=IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'		,						variant='fp16'		,						torch_dtype=torch.floataa				)

								_lowerCAmelCase										=IFSuperResolutionPipeline.from_pretrained(
								    'DeepFloyd/IF-II-L-v1.0'		,						variant='fp16'		,						torch_dtype=torch.floataa		,						text_encoder=__A		,						tokenizer=__A				)

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to('cuda'				)

								_lowerCAmelCase						,				_lowerCAmelCase										=pipe_a.encode_prompt('anime turtle'		,						device='cuda'				)

								del pipe_a.tokenizer
								del pipe_a.text_encoder
								gc.collect()

								_lowerCAmelCase										=None
								_lowerCAmelCase										=None

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if(__A		,						__A		,						__A		,						__A				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								_lowerCAmelCase										=IFImgaImgPipeline(**pipe_a.components				)
								_lowerCAmelCase										=IFImgaImgSuperResolutionPipeline(**pipe_a.components				)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if_imgaimg(__A		,						__A		,						__A		,						__A				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								_lowerCAmelCase										=IFInpaintingPipeline(**pipe_a.components				)
								_lowerCAmelCase										=IFInpaintingSuperResolutionPipeline(**pipe_a.components				)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if_inpainting(__A		,						__A		,						__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     str:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)
								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (64, 64, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (64, 64, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						original_image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     Dict:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(1				)				).to(__A				)

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)
								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						mask_image=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (64, 64, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(1				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						mask_image=__A		,						original_image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()





