



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

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 							lowerCamelCase    (     _lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     List[str]		=	UnCLIPPipeline
					_A						:     Optional[int]		=	TEXT_TO_IMAGE_PARAMS - {
					    """negative_prompt""",
					    """height""",
					    """width""",
					    """negative_prompt_embeds""",
					    """guidance_scale""",
					    """prompt_embeds""",
					    """cross_attention_kwargs""",
					}
					_A						:     Tuple		=	TEXT_TO_IMAGE_BATCH_PARAMS
					_A						:     str		=	[
					    """generator""",
					    """return_dict""",
					    """prior_num_inference_steps""",
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]
					_A						:     List[str]		=	False






					@property
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Union[str, Any]:
											return 32






					@property
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      str:
											return 32






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      str:
											return self.time_input_dim






					@property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      str:
											return self.time_input_dim * 4






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      Dict:
											return 100






					@property
					def   lowerCAmelCase_							(			self:				Any		)  ->      str:
											snake_case_					:int		=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
											return tokenizer






					@property
					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											torch.manual_seed(0		)
											snake_case_					:List[str]		=     CLIPTextConfig(
											    bos_token_id=0		,	eos_token_id=2		,	hidden_size=self.text_embedder_hidden_size		,	projection_dim=self.text_embedder_hidden_size		,	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 CLIPTextModelWithProjection(snake_case		)






					@property
					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											torch.manual_seed(0		)

											snake_case_					:List[str]		=     {
											    """num_attention_heads""": 2,
											    """attention_head_dim""": 12,
											    """embedding_dim""": self.text_embedder_hidden_size,
											    """num_layers""": 1,
											}

											snake_case_					:List[str]		=     PriorTransformer(**snake_case		)
											return model






					@property
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Dict:
											torch.manual_seed(0		)

											snake_case_					:str		=     {
											    """clip_embeddings_dim""": self.text_embedder_hidden_size,
											    """time_embed_dim""": self.time_embed_dim,
											    """cross_attention_dim""": self.cross_attention_dim,
											}

											snake_case_					:str		=     UnCLIPTextProjModel(**snake_case		)
											return model






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      Optional[Any]:
											torch.manual_seed(0		)

											snake_case_					:List[str]		=     {
											    """sample_size""": 32,
											    # RGB in channels
											    """in_channels""": 3,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 6,
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": """identity""",
											}

											snake_case_					:Union[str, Any]		=     UNetaDConditionModel(**snake_case		)
											return model






					@property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      int:
											return {
											    "sample_size": 64,
											    "layers_per_block": 1,
											    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
											    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
											    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    "in_channels": 6,
											    "out_channels": 3,
											}






					@property
					def   lowerCAmelCase_							(			self:				Any		)  ->      List[Any]:
											torch.manual_seed(0		)

											snake_case_					:Any		=     UNetaDModel(**self.dummy_super_res_kwargs		)
											return model






					@property
					def   lowerCAmelCase_							(			self:				Dict		)  ->      int:
											# seeded differently to get different unet than `self.dummy_super_res_first`
											torch.manual_seed(1		)

											snake_case_					:Dict		=     UNetaDModel(**self.dummy_super_res_kwargs		)
											return model






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      List[str]:
											snake_case_					:Union[str, Any]		=     self.dummy_prior
											snake_case_					:Any		=     self.dummy_decoder
											snake_case_					:List[Any]		=     self.dummy_text_proj
											snake_case_					:Union[str, Any]		=     self.dummy_text_encoder
											snake_case_					:Union[str, Any]		=     self.dummy_tokenizer
											snake_case_					:Optional[int]		=     self.dummy_super_res_first
											snake_case_					:int		=     self.dummy_super_res_last

											snake_case_					:str		=     UnCLIPScheduler(
											    variance_type="""fixed_small_log"""		,	prediction_type="""sample"""		,	num_train_timesteps=1_000		,	clip_sample_range=5.0		,	)

											snake_case_					:Optional[Any]		=     UnCLIPScheduler(
											    variance_type="""learned_range"""		,	prediction_type="""epsilon"""		,	num_train_timesteps=1_000		,	)

											snake_case_					:List[Any]		=     UnCLIPScheduler(
											    variance_type="""fixed_small_log"""		,	prediction_type="""epsilon"""		,	num_train_timesteps=1_000		,	)

											snake_case_					:str		=     {
											    """prior""": prior,
											    """decoder""": decoder,
											    """text_proj""": text_proj,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """super_res_first""": super_res_first,
											    """super_res_last""": super_res_last,
											    """prior_scheduler""": prior_scheduler,
											    """decoder_scheduler""": decoder_scheduler,
											    """super_res_scheduler""": super_res_scheduler,
											}

											return components






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	snake_case:				Optional[int]		,	snake_case:				Dict=0		)  ->      Optional[int]:
											if str(snake_case		).startswith("""mps"""		):
																	snake_case_					:Union[str, Any]		=     torch.manual_seed(snake_case		)
											else:
																	snake_case_					:Dict		=     torch.Generator(device=snake_case		).manual_seed(snake_case		)
											snake_case_					:Dict		=     {
											    """prompt""": """horse""",
											    """generator""": generator,
											    """prior_num_inference_steps""": 2,
											    """decoder_num_inference_steps""": 2,
											    """super_res_num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}
											return inputs






					def   lowerCAmelCase_							(			self:				Any		)  ->      Optional[int]:
											snake_case_					:Tuple		=     """cpu"""

											snake_case_					:Union[str, Any]		=     self.get_dummy_components()

											snake_case_					:List[str]		=     self.pipeline_class(**snake_case		)
											snake_case_					:List[Any]		=     pipe.to(snake_case		)

											pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_					:Union[str, Any]		=     pipe(**self.get_dummy_inputs(snake_case		)		)
											snake_case_					:Optional[int]		=     output.images

											snake_case_					:Union[str, Any]		=     pipe(
											    **self.get_dummy_inputs(snake_case		)		,	return_dict=snake_case		,	)[0]

											snake_case_					:str		=     image[0, -3:, -3:, -1]
											snake_case_					:int		=     image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											snake_case_					:List[Any]		=     np.array(
											    [
											        0.9_9_9_7,
											        0.9_9_8_8,
											        0.0_0_2_8,
											        0.9_9_9_7,
											        0.9_9_8_4,
											        0.9_9_6_5,
											        0.0_0_2_9,
											        0.9_9_8_6,
											        0.0_0_2_5,
											    ]		)

											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   lowerCAmelCase_							(			self:				List[str]		)  ->      str:
											snake_case_					:Optional[Any]		=     torch.device("""cpu"""		)
											class 							lowerCamelCase    :






																'''simple docstring'''





																_A						:     Any		=	1

											snake_case_					:List[str]		=     self.get_dummy_components()

											snake_case_					:Tuple		=     self.pipeline_class(**snake_case		)
											snake_case_					:int		=     pipe.to(snake_case		)

											snake_case_					:Optional[int]		=     components["""prior"""]
											snake_case_					:str		=     components["""decoder"""]
											snake_case_					:Any		=     components["""super_res_first"""]
											snake_case_					:Dict		=     components["""tokenizer"""]
											snake_case_					:str		=     components["""text_encoder"""]

											snake_case_					:Union[str, Any]		=     torch.Generator(device=snake_case		).manual_seed(0		)
											snake_case_					:List[Any]		=     prior.dtype
											snake_case_					:Dict		=     1

											snake_case_					:Optional[int]		=     (batch_size, prior.config.embedding_dim)
											snake_case_					:Union[str, Any]		=     pipe.prepare_latents(
											    snake_case		,	dtype=snake_case		,	device=snake_case		,	generator=snake_case		,	latents=snake_case		,	scheduler=DummyScheduler()		)
											snake_case_					:Any		=     (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
											snake_case_					:Optional[int]		=     pipe.prepare_latents(
											    snake_case		,	dtype=snake_case		,	device=snake_case		,	generator=snake_case		,	latents=snake_case		,	scheduler=DummyScheduler()		)

											snake_case_					:Any		=     (
											    batch_size,
											    super_res_first.config.in_channels // 2,
											    super_res_first.config.sample_size,
											    super_res_first.config.sample_size,
											)
											snake_case_					:Optional[int]		=     pipe.prepare_latents(
											    snake_case		,	dtype=snake_case		,	device=snake_case		,	generator=snake_case		,	latents=snake_case		,	scheduler=DummyScheduler()		)

											pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_					:Union[str, Any]		=     """this is a prompt example"""

											snake_case_					:Any		=     torch.Generator(device=snake_case		).manual_seed(0		)
											snake_case_					:str		=     pipe(
											    [prompt]		,	generator=snake_case		,	prior_num_inference_steps=2		,	decoder_num_inference_steps=2		,	super_res_num_inference_steps=2		,	prior_latents=snake_case		,	decoder_latents=snake_case		,	super_res_latents=snake_case		,	output_type="""np"""		,	)
											snake_case_					:List[str]		=     output.images

											snake_case_					:str		=     tokenizer(
											    snake_case		,	padding="""max_length"""		,	max_length=tokenizer.model_max_length		,	return_tensors="""pt"""		,	)
											snake_case_					:Optional[int]		=     text_encoder(text_inputs.input_ids		)
											snake_case_					:Optional[int]		=     text_inputs.attention_mask

											snake_case_					:Optional[int]		=     torch.Generator(device=snake_case		).manual_seed(0		)
											snake_case_					:Union[str, Any]		=     pipe(
											    generator=snake_case		,	prior_num_inference_steps=2		,	decoder_num_inference_steps=2		,	super_res_num_inference_steps=2		,	prior_latents=snake_case		,	decoder_latents=snake_case		,	super_res_latents=snake_case		,	text_model_output=snake_case		,	text_attention_mask=snake_case		,	output_type="""np"""		,	)[0]

											# make sure passing text embeddings manually is identical
											assert np.abs(image - image_from_text		).max() < 1E-4






					@skip_mps
					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Any:
											snake_case_					:Tuple		=     torch_device == """cpu"""

											self._test_attention_slicing_forward_pass(test_max_difference=snake_case		,	expected_max_diff=0.0_1		)






					@skip_mps
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Any		=     torch_device == """cpu"""
											snake_case_					:Union[str, Any]		=     True
											snake_case_					:Optional[int]		=     [
											    """prior_num_inference_steps""",
											    """decoder_num_inference_steps""",
											    """super_res_num_inference_steps""",
											]

											self._test_inference_batch_single_identical(
											    test_max_difference=snake_case		,	relax_max_difference=snake_case		,	additional_params_copy_to_batched_inputs=snake_case		,	)






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Dict:
											snake_case_					:Union[str, Any]		=     [
											    """prior_num_inference_steps""",
											    """decoder_num_inference_steps""",
											    """super_res_num_inference_steps""",
											]

											if torch_device == "mps":
																	# TODO: MPS errors with larger batch sizes
																	snake_case_					:Union[str, Any]		=     [2, 3]
																	self._test_inference_batch_consistent(
																	    batch_sizes=snake_case		,	additional_params_copy_to_batched_inputs=snake_case		,	)
											else:
																	self._test_inference_batch_consistent(
																	    additional_params_copy_to_batched_inputs=snake_case		)






					@skip_mps
					def   lowerCAmelCase_							(			self:				int		)  ->      Tuple:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Optional[Any]:
											return super().test_save_load_local()






					@skip_mps
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Optional[Any]:
											return super().test_save_load_optional_components()

@nightly
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				str		)  ->      Tuple:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											snake_case_					:Union[str, Any]		=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/unclip/karlo_v1_alpha_horse_cpu.npy"""		)

											snake_case_					:Optional[Any]		=     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""		)
											pipeline.set_progress_bar_config(disable=snake_case		)

											snake_case_					:str		=     torch.manual_seed(0		)
											snake_case_					:Optional[Any]		=     pipeline(
											    """horse"""		,	num_images_per_prompt=1		,	generator=snake_case		,	output_type="""np"""		,	)

											snake_case_					:str		=     output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image		).max() < 1E-1

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






					'''simple docstring'''





					def   lowerCAmelCase_							(			self:				Tuple		)  ->      str:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Tuple:
											snake_case_					:Union[str, Any]		=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/unclip/karlo_v1_alpha_horse_fp16.npy"""		)

											snake_case_					:Dict		=     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""		,	torch_dtype=torch.floataa		)
											snake_case_					:Union[str, Any]		=     pipeline.to(snake_case		)
											pipeline.set_progress_bar_config(disable=snake_case		)

											snake_case_					:Union[str, Any]		=     torch.Generator(device="""cpu"""		).manual_seed(0		)
											snake_case_					:Optional[Any]		=     pipeline(
											    """horse"""		,	generator=snake_case		,	output_type="""np"""		,	)

											snake_case_					:Any		=     output.images[0]

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

											assert_mean_pixel_difference(snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      int:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case_					:List[str]		=     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""		,	torch_dtype=torch.floataa		)
											snake_case_					:str		=     pipe.to(snake_case		)
											pipe.set_progress_bar_config(disable=snake_case		)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case_					:Union[str, Any]		=     pipe(
											    """horse"""		,	num_images_per_prompt=1		,	prior_num_inference_steps=2		,	decoder_num_inference_steps=2		,	super_res_num_inference_steps=2		,	output_type="""np"""		,	)

											snake_case_					:List[str]		=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


A__       =			logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

A__       =			{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
A__       =			{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
A__       =			sorted(arg_to_scheduler.keys())
A__       =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class    __lowerCAmelCase					(	pl.LightningModule			):




				def __init__(				self		,							_snake_case		,							_snake_case=None		,							_snake_case="base"		,							_snake_case=None		,							_snake_case=None		,							_snake_case=None		,							**_snake_case		,							):



									"""simple docstring"""





									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(_snake_case						)
									_lowerCAmelCase         =       0
									_lowerCAmelCase         =       Path(self.hparams.output_dir						)
									_lowerCAmelCase         =       self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
														_lowerCAmelCase         =       AutoConfig.from_pretrained(
														    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,							**({"""num_labels""": num_labels} if num_labels is not None else {})		,							cache_dir=_snake_case		,							**_snake_case		,							)
									else:
														_lowerCAmelCase         =       config

									_lowerCAmelCase         =       ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
									for p in extra_model_params:
														if getattr(self.hparams		,							_snake_case		,							_snake_case						):
																			assert hasattr(self.config		,							_snake_case						), F'model config doesn\'t have a `{p}` attribute'
																			setattr(self.config		,							_snake_case		,							getattr(self.hparams		,							_snake_case						)						)

									if tokenizer is None:
														_lowerCAmelCase         =       AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,							cache_dir=_snake_case		,							)
									else:
														_lowerCAmelCase         =       tokenizer
									_lowerCAmelCase         =       MODEL_MODES[mode]
									if model is None:
														_lowerCAmelCase         =       self.model_type.from_pretrained(
														    self.hparams.model_name_or_path		,							from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path						)		,							config=self.config		,							cache_dir=_snake_case		,							)
									else:
														_lowerCAmelCase         =       model




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model_type.from_pretrained(*_snake_case		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       arg_to_scheduler[self.hparams.lr_scheduler]
									_lowerCAmelCase         =       get_schedule_func(
									    self.opt		,							num_warmup_steps=self.hparams.warmup_steps		,							num_training_steps=self.total_steps()						)
									_lowerCAmelCase         =       {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
									return scheduler




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.model
									_lowerCAmelCase         =       ["""bias""", """LayerNorm.weight"""]
									_lowerCAmelCase         =       [
									    {
									        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)],
									        """weight_decay""": self.hparams.weight_decay,
									    },
									    {
									        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
									        """weight_decay""": 0.0,
									    },
									]
									if self.hparams.adafactor:
														_lowerCAmelCase         =       Adafactor(
														    _snake_case		,							lr=self.hparams.learning_rate		,							scale_parameter=_snake_case		,							relative_step=_snake_case						)

									else:
														_lowerCAmelCase         =       AdamW(
														    _snake_case		,							lr=self.hparams.learning_rate		,							eps=self.hparams.adam_epsilon						)
									_lowerCAmelCase         =       optimizer

									_lowerCAmelCase         =       self.get_lr_scheduler()

									return [optimizer], [scheduler]




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									return self.validation_step(_snake_case		,							_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return self.validation_end(_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       max(1		,							self.hparams.gpus						)  # TODO: consider num_tpu_cores
									_lowerCAmelCase         =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									if mode == "test":
														_lowerCAmelCase         =       len(self.test_dataloader().dataset						)
									else:
														_lowerCAmelCase         =       self.get_dataloader("""train"""		,							self.hparams.train_batch_size		,							shuffle=_snake_case						)
														_lowerCAmelCase         =       len(self.train_dataloader().dataset						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case = False						):



									"""simple docstring"""





									raise NotImplementedError("""You must implement this for your task"""						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.train_loader




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""dev"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									return self.get_dataloader("""test"""		,							self.hparams.eval_batch_size		,							shuffle=_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return os.path.join(
									    self.hparams.data_dir		,							"""cached_{}_{}_{}""".format(
									        _snake_case		,							list(filter(_snake_case		,							self.hparams.model_name_or_path.split("""/"""						)						)						).pop()		,							str(self.hparams.max_seq_length						)		,							)		,							)




				@pl.utilities.rank_zero_only
				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.output_dir.joinpath("""best_tfmr"""						)
									_lowerCAmelCase         =       self.step_count
									self.model.save_pretrained(_snake_case						)
									self.tokenizer.save_pretrained(_snake_case						)




				@staticmethod
				def 				snake_case  (				_snake_case		,							_snake_case						):



									"""simple docstring"""





									parser.add_argument(
									    """--model_name_or_path"""		,							default=_snake_case		,							type=_snake_case		,							required=_snake_case		,							help="""Path to pretrained model or model identifier from huggingface.co/models"""		,							)
									parser.add_argument(
									    """--config_name"""		,							default=""""""		,							type=_snake_case		,							help="""Pretrained config name or path if not the same as model_name"""						)
									parser.add_argument(
									    """--tokenizer_name"""		,							default=_snake_case		,							type=_snake_case		,							help="""Pretrained tokenizer name or path if not the same as model_name"""		,							)
									parser.add_argument(
									    """--cache_dir"""		,							default=""""""		,							type=_snake_case		,							help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,							)
									parser.add_argument(
									    """--encoder_layerdrop"""		,							type=_snake_case		,							help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--decoder_layerdrop"""		,							type=_snake_case		,							help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--dropout"""		,							type=_snake_case		,							help="""Dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument(
									    """--attention_dropout"""		,							type=_snake_case		,							help="""Attention dropout probability (Optional). Goes into model.config"""		,							)
									parser.add_argument("""--learning_rate"""		,							default=5e-5		,							type=_snake_case		,							help="""The initial learning rate for Adam."""						)
									parser.add_argument(
									    """--lr_scheduler"""		,							default="""linear"""		,							choices=_snake_case		,							metavar=_snake_case		,							type=_snake_case		,							help="""Learning rate scheduler"""		,							)
									parser.add_argument("""--weight_decay"""		,							default=0.0		,							type=_snake_case		,							help="""Weight decay if we apply some."""						)
									parser.add_argument("""--adam_epsilon"""		,							default=1e-8		,							type=_snake_case		,							help="""Epsilon for Adam optimizer."""						)
									parser.add_argument("""--warmup_steps"""		,							default=0		,							type=_snake_case		,							help="""Linear warmup over warmup_steps."""						)
									parser.add_argument("""--num_workers"""		,							default=4		,							type=_snake_case		,							help="""kwarg passed to DataLoader"""						)
									parser.add_argument("""--num_train_epochs"""		,							dest="""max_epochs"""		,							default=3		,							type=_snake_case						)
									parser.add_argument("""--train_batch_size"""		,							default=32		,							type=_snake_case						)
									parser.add_argument("""--eval_batch_size"""		,							default=32		,							type=_snake_case						)
									parser.add_argument("""--adafactor"""		,							action="""store_true"""						)





class    __lowerCAmelCase					(	pl.Callback			):




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       trainer.lr_schedulers[0]["""scheduler"""]
									_lowerCAmelCase         =       {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
									pl_module.logger.log_metrics(_snake_case						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									rank_zero_info("""***** Validation results *****"""						)
									_lowerCAmelCase         =       trainer.callback_metrics
									# Log results
									for key in sorted(_snake_case						):
														if key not in ["log", "progress_bar"]:
																			rank_zero_info("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									rank_zero_info("""***** Test results *****"""						)
									_lowerCAmelCase         =       trainer.callback_metrics
									# Log and save results to file
									_lowerCAmelCase         =       os.path.join(pl_module.hparams.output_dir		,							"""test_results.txt"""						)
									with open(_snake_case		,							"""w"""						) as writer:
														for key in sorted(_snake_case						):
																			if key not in ["log", "progress_bar"]:
																								rank_zero_info("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)
																								writer.write("""{} = {}\n""".format(_snake_case		,							str(metrics[key]						)						)						)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					parser.add_argument(
					    """--output_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		help="""The output directory where the model predictions and checkpoints will be written."""      ,		)
					parser.add_argument(
					    """--fp16"""      ,		action="""store_true"""      ,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,		)

					parser.add_argument(
					    """--fp16_opt_level"""      ,		type=snake_case      ,		default="""O2"""      ,		help=(
					        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
					        """See details at https://nvidia.github.io/apex/amp.html"""
					    )      ,		)
					parser.add_argument("""--n_tpu_cores"""      ,		dest="""tpu_cores"""      ,		type=snake_case     )
					parser.add_argument("""--max_grad_norm"""      ,		dest="""gradient_clip_val"""      ,		default=1.0      ,		type=snake_case      ,		help="""Max gradient norm"""     )
					parser.add_argument("""--do_train"""      ,		action="""store_true"""      ,		help="""Whether to run training."""     )
					parser.add_argument("""--do_predict"""      ,		action="""store_true"""      ,		help="""Whether to run predictions on the test set."""     )
					parser.add_argument(
					    """--gradient_accumulation_steps"""      ,		dest="""accumulate_grad_batches"""      ,		type=snake_case      ,		default=1      ,		help="""Number of updates steps to accumulate before performing a backward/update pass."""      ,		)
					parser.add_argument("""--seed"""      ,		type=snake_case      ,		default=42      ,		help="""random seed for initialization"""     )
					parser.add_argument(
					    """--data_dir"""      ,		default=snake_case      ,		type=snake_case      ,		required=snake_case      ,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""      ,		)


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case=None      ,		snake_case=True      ,		snake_case=[]      ,		snake_case=None      ,		snake_case=None      ,		**snake_case      ,		):





					"""simple docstring"""

					pl.seed_everything(args.seed     )

					# init model
					_lowerCAmelCase         =       Path(model.hparams.output_dir     )
					odir.mkdir(exist_ok=snake_case     )

					# add custom checkpoints
					if checkpoint_callback is None:
										_lowerCAmelCase         =       pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir      ,		prefix="""checkpoint"""      ,		monitor="""val_loss"""      ,		mode="""min"""      ,		save_top_k=1     )
					if early_stopping_callback:
										extra_callbacks.append(snake_case     )
					if logging_callback is None:
										_lowerCAmelCase         =       LoggingCallback()

					_lowerCAmelCase         =       {}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										_lowerCAmelCase         =       16
										_lowerCAmelCase         =       args.fpaa_opt_level

					if args.gpus > 1:
										_lowerCAmelCase         =       """ddp"""

					_lowerCAmelCase         =       args.accumulate_grad_batches
					_lowerCAmelCase         =       extra_train_kwargs.get("""accelerator"""      ,		snake_case     )
					_lowerCAmelCase         =       extra_train_kwargs.get("""profiler"""      ,		snake_case     )

					_lowerCAmelCase         =       pl.Trainer.from_argparse_args(
					    snake_case      ,		weights_summary=snake_case      ,		callbacks=[logging_callback] + extra_callbacks      ,		logger=snake_case      ,		checkpoint_callback=snake_case      ,		**snake_case      ,		)

					if args.do_train:
										trainer.fit(snake_case     )

					return trainer



