





'''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
_SCREAMING_SNAKE_CASE					=    False
_SCREAMING_SNAKE_CASE					=    False




def    _lowerCAmelCase						(				lowerCamelCase_			:  Namespace				):
			return TrainCommand(lowerCamelCase_				)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	@staticmethod
	def 						_UpperCAmelCase     (_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							parser.add_parser('''train'''     ,help='''CLI tool to train a model on a task.''' )

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

				train_parser.add_argument(
				    '''--task'''     ,type=_lowerCamelCase     ,default='''text_classification'''     ,help='''Task to train the model on.''' )
				train_parser.add_argument(
				    '''--model'''     ,type=_lowerCamelCase     ,default='''bert-base-uncased'''     ,help='''Model\'s name or path to stored model.''' )
				train_parser.add_argument('''--train_batch_size'''     ,type=_lowerCamelCase     ,default=32     ,help='''Batch size for training.''' )
				train_parser.add_argument('''--valid_batch_size'''     ,type=_lowerCamelCase     ,default=64     ,help='''Batch size for validation.''' )
				train_parser.add_argument('''--learning_rate'''     ,type=_lowerCamelCase     ,default=3E-5     ,help='''Learning rate.''' )
				train_parser.add_argument('''--adam_epsilon'''     ,type=_lowerCamelCase     ,default=1E-0_8     ,help='''Epsilon for Adam optimizer.''' )
				train_parser.set_defaults(func=_lowerCamelCase )


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







				'''simple docstring'''





				__lowercase										=							logging.get_logger('''transformers-cli/training''' )

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

				os.makedirs(args.output     ,exist_ok=_lowerCamelCase )
				__lowercase										=							args.output

				__lowercase										=							args.column_label
				__lowercase										=							args.column_text
				__lowercase										=							args.column_id

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

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


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self )							->							Optional[int]:







				'''simple docstring'''





				raise NotImplementedError


	def 						_UpperCAmelCase     (self )							->							str:







				'''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 TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_SCREAMING_SNAKE_CASE					=    False






class 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 12


	@property
	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				return 32


	@property
	def 						_UpperCAmelCase     (self )							->							List[str]:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							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     ,num_vq_embeddings=self.num_embed     ,vq_embed_dim=3     ,)
				return model


	@property
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
				return tokenizer


	@property
	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				torch.manual_seed(0 )
				__lowercase										=							CLIPTextConfig(
				    bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1E-0_5     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
				return CLIPTextModel(_lowerCamelCase )


	@property
	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0 )

				__lowercase										=							12
				__lowercase										=							12

				__lowercase										=							{
				    '''attention_bias''': True,
				    '''cross_attention_dim''': 32,
				    '''attention_head_dim''': height * width,
				    '''num_attention_heads''': 1,
				    '''num_vector_embeds''': self.num_embed,
				    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
				    '''norm_num_groups''': 32,
				    '''sample_size''': width,
				    '''activation_fn''': '''geglu-approximate''',
				}

				__lowercase										=							TransformeraDModel(**_lowerCamelCase )
				return model


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )

				assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							'''cpu'''

				__lowercase										=							self.dummy_vqvae
				__lowercase										=							self.dummy_text_encoder
				__lowercase										=							self.dummy_tokenizer
				__lowercase										=							self.dummy_transformer
				__lowercase										=							VQDiffusionScheduler(self.num_embed )
				__lowercase										=							LearnedClassifierFreeSamplingEmbeddings(
				    learnable=_lowerCamelCase     ,hidden_size=self.text_embedder_hidden_size     ,length=tokenizer.model_max_length )

				__lowercase										=							VQDiffusionPipeline(
				    vqvae=_lowerCamelCase     ,text_encoder=_lowerCamelCase     ,tokenizer=_lowerCamelCase     ,transformer=_lowerCamelCase     ,scheduler=_lowerCamelCase     ,learned_classifier_free_sampling_embeddings=_lowerCamelCase     ,)
				__lowercase										=							pipe.to(_lowerCamelCase )
				pipe.set_progress_bar_config(disable=_lowerCamelCase )

				__lowercase										=							'''teddy bear playing in the pool'''

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe([prompt]     ,generator=_lowerCamelCase     ,num_inference_steps=2     ,output_type='''np''' )
				__lowercase										=							output.images

				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipe(
				    [prompt]     ,generator=_lowerCamelCase     ,output_type='''np'''     ,return_dict=_lowerCamelCase     ,num_inference_steps=2 )[0]

				__lowercase										=							image[0, -3:, -3:, -1]
				__lowercase										=							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 24, 24, 3)

				__lowercase										=							np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )

				assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






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







	'''simple docstring'''

	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				__lowercase										=							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

				__lowercase										=							VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
				__lowercase										=							pipeline.to(_lowerCamelCase )
				pipeline.set_progress_bar_config(disable=_lowerCamelCase )

				# requires GPU generator for gumbel softmax
				# don't use GPU generator in tests though
				__lowercase										=							torch.Generator(device=_lowerCamelCase ).manual_seed(0 )
				__lowercase										=							pipeline(
				    '''teddy bear playing in the pool'''     ,num_images_per_prompt=1     ,generator=_lowerCamelCase     ,output_type='''np'''     ,)

				__lowercase										=							output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image ).max() < 2.0





