

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__A	:   str	=					logging.getLogger(__name__)


@dataclass
class 			_SCREAMING_SNAKE_CASE      :
						_UpperCamelCase:int	=		field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
						_UpperCamelCase:List[Any]	=		field(
						    default=__UpperCAmelCase ,    metadata={"help": "Pretrained config name or path if not the same as model_name"})
						_UpperCamelCase:int	=		field(
						    default=__UpperCAmelCase ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
						_UpperCamelCase:Any	=		field(default=__UpperCAmelCase ,    metadata={"help": "Set this flag to use fast tokenization."})
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						_UpperCamelCase:Any	=		field(
						    default=__UpperCAmelCase ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,    )




def        __UpperCamelCase     (      )				->Optional[int]:



							"""simple docstring"""
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							lowerCamelCase_								=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

							if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
							else:
														lowerCamelCase_	,   lowerCamelCase_	,   lowerCamelCase_								=parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir		)
							    and os.listdir(training_args.output_dir		)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
														    """ --overwrite_output_dir to overcome."""		)

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,  datefmt="""%m/%d/%Y %H:%M:%S"""      ,  level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,  )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""      ,  training_args.local_rank      ,  training_args.device      ,  training_args.n_gpu      ,  bool(training_args.local_rank != -1		)      ,  training_args.fpaa      ,  )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank		):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""      ,  _A		)

							# Prepare Question-Answering task
							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							lowerCamelCase_								=AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,  cache_dir=model_args.cache_dir      ,  )
							lowerCamelCase_								=AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,  cache_dir=model_args.cache_dir      ,  use_fast=_A      ,  )
							lowerCamelCase_								=AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path      ,  from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)      ,  config=_A      ,  cache_dir=model_args.cache_dir      ,  )

							# Get datasets
							lowerCamelCase_								=hasattr(model.config      ,  """lang2id"""		)
							lowerCamelCase_								=(
							    SquadDataset(
							        _A      ,  tokenizer=_A      ,  is_language_sensitive=_A      ,  cache_dir=model_args.cache_dir		)
							    if training_args.do_train
							    else None
							)
							lowerCamelCase_								=(
							    SquadDataset(
							        _A      ,  tokenizer=_A      ,  mode="""dev"""      ,  is_language_sensitive=_A      ,  cache_dir=model_args.cache_dir      ,  )
							    if training_args.do_eval
							    else None
							)

							# Data collator
							lowerCamelCase_								=DataCollatorWithPadding(_A      ,  pad_to_multiple_of=8		) if training_args.fpaa else None

							# Initialize our Trainer
							lowerCamelCase_								=Trainer(
							    model=_A      ,  args=_A      ,  train_dataset=_A      ,  eval_dataset=_A      ,  data_collator=_A      ,  )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir		)




def        __UpperCamelCase     (      _A					:							int		)				->List[str]:



							"""simple docstring"""
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
	main()

"""simple docstring"""






import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 							snake_case			(					__UpperCAmelCase ,       unittest.TestCase							):




					"""simple docstring"""

					snake_case__							       =					KandinskyVaaPriorEmbaEmbPipeline
					snake_case__							       =					["prompt", "image"]
					snake_case__							       =					["prompt", "image"]
					snake_case__							       =					[
					    "num_images_per_prompt",
					    "strength",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__							       =					False







					@property
					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									return 32







					@property
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									return 32







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return self.time_input_dim







					@property
					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									return self.time_input_dim * 4







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[Any]     ):
									return 100







					@property
					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
									return tokenizer







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									torch.manual_seed(0     )
									UpperCAmelCase__			 =				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(lowerCamelCase__     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									torch.manual_seed(0     )

									UpperCAmelCase__			 =				{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__			 =				PriorTransformer(**lowerCamelCase__     )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__			 =				nn.Parameter(torch.ones(model.clip_std.shape     )     )
									return model







					@property
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									torch.manual_seed(0     )
									UpperCAmelCase__			 =				CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size       ,image_size=224       ,projection_dim=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_channels=3       ,num_hidden_layers=5       ,patch_size=14       ,)

									UpperCAmelCase__			 =				CLIPVisionModelWithProjection(lowerCamelCase__     )
									return model







					@property
					def 						__lowerCAmelCase		(							self  :      List[str]     ):
									UpperCAmelCase__			 =				CLIPImageProcessor(
									    crop_size=224       ,do_center_crop=lowerCamelCase__       ,do_normalize=lowerCamelCase__       ,do_resize=lowerCamelCase__       ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,resample=3       ,size=224       ,)

									return image_processor







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				self.dummy_prior
									UpperCAmelCase__			 =				self.dummy_image_encoder
									UpperCAmelCase__			 =				self.dummy_text_encoder
									UpperCAmelCase__			 =				self.dummy_tokenizer
									UpperCAmelCase__			 =				self.dummy_image_processor

									UpperCAmelCase__			 =				UnCLIPScheduler(
									    variance_type='fixed_small_log'       ,prediction_type='sample'       ,num_train_timesteps=1_000       ,clip_sample=lowerCamelCase__       ,clip_sample_range=1_0.0       ,)

									UpperCAmelCase__			 =				{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      Any       ,lowerCamelCase__  :      str=0     ):
									if str(lowerCamelCase__     ).startswith('mps'     ):
													UpperCAmelCase__			 =				torch.manual_seed(lowerCamelCase__     )
									else:
													UpperCAmelCase__			 =				torch.Generator(device=lowerCamelCase__     ).manual_seed(lowerCamelCase__     )

									UpperCAmelCase__			 =				floats_tensor((1, 3, 64, 64)       ,rng=random.Random(lowerCamelCase__     )     ).to(lowerCamelCase__     )
									UpperCAmelCase__			 =				image.cpu().permute(0       ,2       ,3       ,1     )[0]
									UpperCAmelCase__			 =				Image.fromarray(np.uinta(lowerCamelCase__     )     ).convert('RGB'     ).resize((256, 256)     )

									UpperCAmelCase__			 =				{
									    'prompt': 'horse',
									    'image': init_image,
									    'strength': 0.5,
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 						__lowerCAmelCase		(							self  :      List[Any]     ):
									UpperCAmelCase__			 =				'cpu'

									UpperCAmelCase__			 =				self.get_dummy_components()

									UpperCAmelCase__			 =				self.pipeline_class(**lowerCamelCase__     )
									UpperCAmelCase__			 =				pipe.to(lowerCamelCase__     )

									pipe.set_progress_bar_config(disable=lowerCamelCase__     )

									UpperCAmelCase__			 =				pipe(**self.get_dummy_inputs(lowerCamelCase__     )     )
									UpperCAmelCase__			 =				output.image_embeds

									UpperCAmelCase__			 =				pipe(
									    **self.get_dummy_inputs(lowerCamelCase__     )       ,return_dict=lowerCamelCase__       ,)[0]

									UpperCAmelCase__			 =				image[0, -10:]
									UpperCAmelCase__			 =				image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__			 =				np.array(
									    [
									        0.1_0_7_1_2_8_4,
									        1.3_3_3_0_2_7_1,
									        0.6_1_2_6_0_2_2_3,
									        -0.6_6_9_1_0_6_5,
									        -0.3_8_4_6_8_5_2,
									        -1.0_3_0_3_6_6_1,
									        0.2_2_7_1_6_1_1_1,
									        0.0_3_3_4_8_9_0_1,
									        0.3_0_0_4_0_6_7_5,
									        -0.2_4_8_0_5_0_2_9,
									    ]     )

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







					@skip_mps
					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									UpperCAmelCase__			 =				torch_device == 'cpu'
									UpperCAmelCase__			 =				True
									UpperCAmelCase__			 =				False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowerCamelCase__       ,relax_max_difference=lowerCamelCase__       ,test_mean_pixel_difference=lowerCamelCase__       ,)







					@skip_mps
					def 						__lowerCAmelCase		(							self  :      int     ):
									UpperCAmelCase__			 =				torch_device == 'cpu'
									UpperCAmelCase__			 =				False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowerCamelCase__       ,test_mean_pixel_difference=lowerCamelCase__       ,)



