




"""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       __lowercase       (			snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


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

				@property
				def       _lowerCamelCase					(   self   :     List[str]      ):
										return 3_2

				@property
				def       _lowerCamelCase					(   self   :     List[str]      ):
										return 3_2

				@property
				def       _lowerCamelCase					(   self   :     int      ):
										return self.time_input_dim

				@property
				def       _lowerCamelCase					(   self   :     int      ):
										return self.time_input_dim * 4

				@property
				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										return 1_0_0

				@property
				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					List[str]										=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
										return tokenizer

				@property
				def       _lowerCamelCase					(   self   :     Dict      ):
										torch.manual_seed(0      )
										snake_case__       :					Optional[int]										=					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=3_7      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1_0_0_0      ,)
										return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_      )

				@property
				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										torch.manual_seed(0      )

										snake_case__       :					Dict										=					{
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 1_2,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										snake_case__       :					str										=					PriorTransformer(**SCREAMING_SNAKE_CASE_      )
										# 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
										snake_case__       :					str										=					nn.Parameter(torch.ones(model.clip_std.shape      )      )
										return model

				@property
				def       _lowerCamelCase					(   self   :     Tuple      ):
										torch.manual_seed(0      )
										snake_case__       :					Dict										=					CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size      ,image_size=2_2_4      ,projection_dim=self.text_embedder_hidden_size      ,intermediate_size=3_7      ,num_attention_heads=4      ,num_channels=3      ,num_hidden_layers=5      ,patch_size=1_4      ,)

										snake_case__       :					Union[str, Any]										=					CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE_      )
										return model

				@property
				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										snake_case__       :					Tuple										=					CLIPImageProcessor(
										    crop_size=2_2_4      ,do_center_crop=SCREAMING_SNAKE_CASE_      ,do_normalize=SCREAMING_SNAKE_CASE_      ,do_resize=SCREAMING_SNAKE_CASE_      ,image_mean=[0.48145466, 0.4578275, 0.40821073]      ,image_std=[0.26862954, 0.26130258, 0.27577711]      ,resample=3      ,size=2_2_4      ,)

										return image_processor

				def       _lowerCamelCase					(   self   :     Dict      ):
										snake_case__       :					Optional[Any]										=					self.dummy_prior
										snake_case__       :					int										=					self.dummy_image_encoder
										snake_case__       :					int										=					self.dummy_text_encoder
										snake_case__       :					List[str]										=					self.dummy_tokenizer
										snake_case__       :					List[Any]										=					self.dummy_image_processor

										snake_case__       :					Dict										=					UnCLIPScheduler(
										    variance_type='''fixed_small_log'''      ,prediction_type='''sample'''      ,num_train_timesteps=1_0_0_0      ,clip_sample=SCREAMING_SNAKE_CASE_      ,clip_sample_range=10.0      ,)

										snake_case__       :					Union[str, Any]										=					{
										    '''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]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=0      ):
										if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																snake_case__       :					List[Any]										=					torch.manual_seed(SCREAMING_SNAKE_CASE_      )
										else:
																snake_case__       :					Union[str, Any]										=					torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Dict										=					floats_tensor((1, 3, 6_4, 6_4)      ,rng=random.Random(SCREAMING_SNAKE_CASE_      )      ).to(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Tuple										=					image.cpu().permute(0      ,2      ,3      ,1      )[0]
										snake_case__       :					Any										=					Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )

										snake_case__       :					Tuple										=					{
										    '''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   :     Optional[Any]      ):
										snake_case__       :					Tuple										=					'''cpu'''

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

										snake_case__       :					Union[str, Any]										=					self.pipeline_class(**SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Tuple										=					pipe.to(SCREAMING_SNAKE_CASE_      )

										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

										snake_case__       :					str										=					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )      )
										snake_case__       :					Any										=					output.image_embeds

										snake_case__       :					str										=					pipe(
										    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )      ,return_dict=SCREAMING_SNAKE_CASE_      ,)[0]

										snake_case__       :					str										=					image[0, -1_0:]
										snake_case__       :					str										=					image_from_tuple[0, -1_0:]

										assert image.shape == (1, 3_2)

										snake_case__       :					str										=					np.array(
										    [
										        0.1071284,
										        1.3330271,
										        0.61260223,
										        -0.6691065,
										        -0.3846852,
										        -1.0303661,
										        0.22716111,
										        0.03348901,
										        0.30040675,
										        -0.24805029,
										    ]      )

										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   :     List[Any]      ):
										snake_case__       :					List[str]										=					torch_device == '''cpu'''
										snake_case__       :					int										=					True
										snake_case__       :					Optional[Any]										=					False

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





				@skip_mps
				def       _lowerCamelCase					(   self   :     List[str]      ):
										snake_case__       :					Optional[Any]										=					torch_device == '''cpu'''
										snake_case__       :					List[Any]										=					False

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








"""simple docstring"""





from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
							from .data.data_collator import DataCollator
							from .modeling_utils import PreTrainedModel
							from .tokenization_utils_base import PreTrainedTokenizerBase
							from .trainer_callback import TrainerCallback
							from .trainer_utils import EvalPrediction, PredictionOutput
							from .training_args import TrainingArguments


_lowercase	      =    logging.get_logger(__name__)

class       __lowercase       (			snake_case_	):


				'''simple docstring'''


				def __init__(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Union["PreTrainedModel", nn.Module] = None      ,SCREAMING_SNAKE_CASE_   :     "TrainingArguments" = None      ,SCREAMING_SNAKE_CASE_   :     Optional["DataCollator"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Union[Dataset, Dict[str, Dataset]]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional["PreTrainedTokenizerBase"] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[], "PreTrainedModel"]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[["EvalPrediction"], Dict]] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List["TrainerCallback"]] = None      ,SCREAMING_SNAKE_CASE_   :     Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)      ,SCREAMING_SNAKE_CASE_   :     Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None      ,):
										super().__init__(
										    model=SCREAMING_SNAKE_CASE_      ,args=SCREAMING_SNAKE_CASE_      ,data_collator=SCREAMING_SNAKE_CASE_      ,train_dataset=SCREAMING_SNAKE_CASE_      ,eval_dataset=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,model_init=SCREAMING_SNAKE_CASE_      ,compute_metrics=SCREAMING_SNAKE_CASE_      ,callbacks=SCREAMING_SNAKE_CASE_      ,optimizers=SCREAMING_SNAKE_CASE_      ,preprocess_logits_for_metrics=SCREAMING_SNAKE_CASE_      ,)

										# Override self.model.generation_config if a GenerationConfig is specified in args.
										# Priority: args.generation_config > model.generation_config > default GenerationConfig.
										if self.args.generation_config is not None:
																snake_case__       :					List[Any]										=					self.load_generation_config(self.args.generation_config      )
																snake_case__       :					Optional[int]										=					gen_config

				@staticmethod
				def       _lowerCamelCase					(   SCREAMING_SNAKE_CASE_   :     Union[str, GenerationConfig]      ):

										# GenerationConfig provided, nothing to do
										if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ):
																return deepcopy(SCREAMING_SNAKE_CASE_      )

										# str or Path
										snake_case__       :					Optional[int]										=					Path(SCREAMING_SNAKE_CASE_      ) if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else gen_config_arg
										snake_case__       :					Optional[Any]										=					None

										# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
										# This step is required in order to determine config_file_name
										if pretrained_model_name.is_file():
																snake_case__       :					List[Any]										=					pretrained_model_name.name
																snake_case__       :					Any										=					pretrained_model_name.parent
										# dir path
										elif pretrained_model_name.is_dir():
																pass
										# model id or URL
										else:
																snake_case__       :					Optional[Any]										=					gen_config_arg

										snake_case__       :					Optional[Any]										=					GenerationConfig.from_pretrained(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )
										return gen_config

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[Dataset] = None      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "eval"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					List[str]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Optional[int]										=					self.args.generation_max_length
										snake_case__       :					Optional[Any]										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
										)
										snake_case__       :					Optional[Any]										=					gen_kwargs

										return super().evaluate(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Dataset      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,SCREAMING_SNAKE_CASE_   :     str = "test"      ,**SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					Union[str, Any]										=					gen_kwargs.copy()
										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.args.generation_max_length
										snake_case__       :					str										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.args.generation_num_beams
										)
										snake_case__       :					List[str]										=					gen_kwargs

										return super().predict(SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      ,metric_key_prefix=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     nn.Module      ,SCREAMING_SNAKE_CASE_   :     Dict[str, Union[torch.Tensor, Any]]      ,SCREAMING_SNAKE_CASE_   :     bool      ,SCREAMING_SNAKE_CASE_   :     Optional[List[str]] = None      ,**SCREAMING_SNAKE_CASE_   :     List[Any]      ,):

										if not self.args.predict_with_generate or prediction_loss_only:
																return super().prediction_step(
																    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,prediction_loss_only=SCREAMING_SNAKE_CASE_      ,ignore_keys=SCREAMING_SNAKE_CASE_      )

										snake_case__       :					List[Any]										=					'''labels''' in inputs
										snake_case__       :					Tuple										=					self._prepare_inputs(SCREAMING_SNAKE_CASE_      )

										# XXX: adapt synced_gpus for fairscale as well
										# Priority (handled in generate):
										# gen_kwargs > model.generation_config > default GenerationConfig()

										if len(SCREAMING_SNAKE_CASE_      ) == 0 and hasattr(self      ,'''_gen_kwargs'''      ):
																snake_case__       :					Any										=					self._gen_kwargs.copy()

										if gen_kwargs.get('''max_length'''      ) is None and gen_kwargs.get('''max_new_tokens'''      ) is None:
																snake_case__       :					Any										=					self.model.config.max_length
										snake_case__       :					int										=					(
										    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''      ) is not None else self.model.config.num_beams
										)
										snake_case__       :					Optional[int]										=					True if is_deepspeed_zeroa_enabled() else False
										snake_case__       :					Any										=					(
										    gen_kwargs['''synced_gpus'''] if gen_kwargs.get('''synced_gpus'''      ) is not None else default_synced_gpus
										)

										# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
										# (otherwise, it would continue generating from the padded `decoder_input_ids`)
										if (
										    "labels" in inputs
										    and "decoder_input_ids" in inputs
										    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
										):
																snake_case__       :					List[str]										=					{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
										snake_case__       :					List[str]										=					self.model.generate(**SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )

										# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
										# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
										# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
										if self.model.generation_config._from_model_config:
																snake_case__       :					int										=					False

										# Retrieves GenerationConfig from model.generation_config
										snake_case__       :					Dict										=					self.model.generation_config
										# in case the batch is shorter than max length, the output should be padded
										if generated_tokens.shape[-1] < gen_config.max_length:
																snake_case__       :					Tuple										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
										elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
																snake_case__       :					Any										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )

										with torch.no_grad():
																if has_labels:
																						with self.compute_loss_context_manager():
																												snake_case__       :					Union[str, Any]										=					model(**SCREAMING_SNAKE_CASE_      )
																						if self.label_smoother is not None:
																												snake_case__       :					str										=					self.label_smoother(SCREAMING_SNAKE_CASE_      ,inputs['''labels''']      ).mean().detach()
																						else:
																												snake_case__       :					Optional[Any]										=					(outputs['''loss'''] if isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ) else outputs[0]).mean().detach()
																else:
																						snake_case__       :					Optional[Any]										=					None

										if self.args.prediction_loss_only:
																return loss, None, None

										if has_labels:
																snake_case__       :					Tuple										=					inputs['''labels''']
																if labels.shape[-1] < gen_config.max_length:
																						snake_case__       :					List[str]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_length      )
																elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																						snake_case__       :					Optional[int]										=					self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,gen_config.max_new_tokens + 1      )
										else:
																snake_case__       :					List[str]										=					None

										return loss, generated_tokens, labels





				def       _lowerCamelCase					(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Dict      ):
										if self.tokenizer is not None and hasattr(self.tokenizer      ,'''pad_token_id'''      ):
																# If PAD token is not defined at least EOS token has to be defined
																snake_case__       :					List[str]										=					(
																    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
																)
										else:
																if self.model.config.pad_token_id is not None:
																						snake_case__       :					Any										=					self.model.config.pad_token_id
																else:
																						raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''      )

										snake_case__       :					str										=					pad_token_id * torch.ones(
										    (tensor.shape[0], max_length)      ,dtype=tensor.dtype      ,device=tensor.device      )
										snake_case__       :					str										=					tensor
										return padded_tensor


