



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class  __snake_case      :







							'''simple docstring'''






							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

											torch.manual_seed(0		)
											a__				      =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

											torch.manual_seed(0		)
											a__				      =		UNetaDConditionModel(
											    sample_size=32  ,  layers_per_block=1  ,  block_out_channels=[32, 64]  ,  down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]  ,  mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""  ,  up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]  ,  in_channels=3  ,  out_channels=6  ,  cross_attention_dim=32  ,  encoder_hid_dim=32  ,  attention_head_dim=8  ,  addition_embed_type="""text"""  ,  addition_embed_type_num_heads=2  ,  cross_attention_norm="""group_norm"""  ,  resnet_time_scale_shift="""scale_shift"""  ,  act_fn="""gelu"""  ,  )
											unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

											torch.manual_seed(0		)
											a__				      =		DDPMScheduler(
											    num_train_timesteps=1_000  ,  beta_schedule="""squaredcos_cap_v2"""  ,  beta_start=0.0_001  ,  beta_end=0.02  ,  thresholding=a_  ,  dynamic_thresholding_ratio=0.95  ,  sample_max_value=1.0  ,  prediction_type="""epsilon"""  ,  variance_type="""learned_range"""  ,  )

											torch.manual_seed(0		)
											a__				      =		IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}


							def _a      (							self		):
											torch.manual_seed(0		)
											a__				      =		TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

											torch.manual_seed(0		)
											a__				      =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

											torch.manual_seed(0		)
											a__				      =		UNetaDConditionModel(
											    sample_size=32  ,  layers_per_block=[1, 2]  ,  block_out_channels=[32, 64]  ,  down_block_types=[
											        """ResnetDownsampleBlock2D""",
											        """SimpleCrossAttnDownBlock2D""",
											    ]  ,  mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""  ,  up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]  ,  in_channels=6  ,  out_channels=6  ,  cross_attention_dim=32  ,  encoder_hid_dim=32  ,  attention_head_dim=8  ,  addition_embed_type="""text"""  ,  addition_embed_type_num_heads=2  ,  cross_attention_norm="""group_norm"""  ,  resnet_time_scale_shift="""scale_shift"""  ,  act_fn="""gelu"""  ,  class_embed_type="""timestep"""  ,  mid_block_scale_factor=1.414  ,  time_embedding_act_fn="""gelu"""  ,  time_embedding_dim=32  ,  )
											unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

											torch.manual_seed(0		)
											a__				      =		DDPMScheduler(
											    num_train_timesteps=1_000  ,  beta_schedule="""squaredcos_cap_v2"""  ,  beta_start=0.0_001  ,  beta_end=0.02  ,  thresholding=a_  ,  dynamic_thresholding_ratio=0.95  ,  sample_max_value=1.0  ,  prediction_type="""epsilon"""  ,  variance_type="""learned_range"""  ,  )

											torch.manual_seed(0		)
											a__				      =		DDPMScheduler(
											    num_train_timesteps=1_000  ,  beta_schedule="""squaredcos_cap_v2"""  ,  beta_start=0.0_001  ,  beta_end=0.02  ,  )

											torch.manual_seed(0		)
											a__				      =		IFWatermarker()

											return {
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "unet": unet,
											    "scheduler": scheduler,
											    "image_noising_scheduler": image_noising_scheduler,
											    "watermarker": watermarker,
											    "safety_checker": None,
											    "feature_extractor": None,
											}


							def _a      (							self		):
											a__				      =		self.get_dummy_components()
											a__				      =		self.pipeline_class(**a_		)
											pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		self.get_dummy_inputs(a_		)

											a__				      =		inputs["""prompt"""]
											a__				      =		inputs["""generator"""]
											a__				      =		inputs["""num_inference_steps"""]
											a__				      =		inputs["""output_type"""]

											if "image" in inputs:
															a__				      =		inputs["""image"""]
											else:
															a__				      =		None

											if "mask_image" in inputs:
															a__				      =		inputs["""mask_image"""]
											else:
															a__				      =		None

											if "original_image" in inputs:
															a__				      =		inputs["""original_image"""]
											else:
															a__				      =		None

											a__      ,			a__				      =		pipe.encode_prompt(a_		)

											# inputs with prompt converted to embeddings
											a__				      =		{
											    """prompt_embeds""": prompt_embeds,
											    """negative_prompt_embeds""": negative_prompt_embeds,
											    """generator""": generator,
											    """num_inference_steps""": num_inference_steps,
											    """output_type""": output_type,
											}

											if image is not None:
															a__				      =		image

											if mask_image is not None:
															a__				      =		mask_image

											if original_image is not None:
															a__				      =		original_image

											# set all optional components to None
											for optional_component in pipe._optional_components:
															setattr(a_  ,  a_  ,  a_		)

											a__				      =		pipe(**a_		)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(a_		)
															a__				      =		self.pipeline_class.from_pretrained(a_		)
															pipe_loaded.to(a_		)
															pipe_loaded.set_progress_bar_config(disable=a_		)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

											for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(a_  ,  a_		) is None  ,  F'''`{optional_component}` did not stay set to None after loading.'''  ,  )

											a__				      =		self.get_dummy_inputs(a_		)

											a__				      =		inputs["""generator"""]
											a__				      =		inputs["""num_inference_steps"""]
											a__				      =		inputs["""output_type"""]

											# inputs with prompt converted to embeddings
											a__				      =		{
											    """prompt_embeds""": prompt_embeds,
											    """negative_prompt_embeds""": negative_prompt_embeds,
											    """generator""": generator,
											    """num_inference_steps""": num_inference_steps,
											    """output_type""": output_type,
											}

											if image is not None:
															a__				      =		image

											if mask_image is not None:
															a__				      =		mask_image

											if original_image is not None:
															a__				      =		original_image

											a__				      =		pipe_loaded(**a_		)[0]

											a__				      =		np.abs(to_np(a_		) - to_np(a_		)		).max()
											self.assertLess(a_  ,  1E-4		)







							def _a      (							self		):
											a__				      =		self.get_dummy_components()
											a__				      =		self.pipeline_class(**a_		)
											pipe.to(a_		)
											pipe.set_progress_bar_config(disable=a_		)

											a__				      =		self.get_dummy_inputs(a_		)
											a__				      =		pipe(**a_		)[0]

											with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(a_		)
															a__				      =		self.pipeline_class.from_pretrained(a_		)
															pipe_loaded.to(a_		)
															pipe_loaded.set_progress_bar_config(disable=a_		)

											pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

											a__				      =		self.get_dummy_inputs(a_		)
											a__				      =		pipe_loaded(**a_		)[0]

											a__				      =		np.abs(to_np(a_		) - to_np(a_		)		).max()
											self.assertLess(a_  ,  1E-4		)






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
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		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-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

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

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


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


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							self		):
											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		)

