






"""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


UpperCamelCase_						:					List[str]      				=			logging.get_logger(__name__)


class   _lowercase			(  lowerCAmelCase    ):


      def __init__(     self					:						str				, a					:						Union["PreTrainedModel", nn.Module] = None				, a					:						"TrainingArguments" = None				, a					:						Optional["DataCollator"] = None				, a					:						Optional[Dataset] = None				, a					:						Optional[Union[Dataset, Dict[str, Dataset]]] = None				, a					:						Optional["PreTrainedTokenizerBase"] = None				, a					:						Optional[Callable[[], "PreTrainedModel"]] = None				, a					:						Optional[Callable[["EvalPrediction"], Dict]] = None				, a					:						Optional[List["TrainerCallback"]] = None				, a					:						Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)				, a					:						Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None				, ):





            """simple docstring"""







            super().__init__(
                model=UpperCamelCase_				, args=UpperCamelCase_				, data_collator=UpperCamelCase_				, train_dataset=UpperCamelCase_				, eval_dataset=UpperCamelCase_				, tokenizer=UpperCamelCase_				, model_init=UpperCamelCase_				, compute_metrics=UpperCamelCase_				, callbacks=UpperCamelCase_				, optimizers=UpperCamelCase_				, preprocess_logits_for_metrics=UpperCamelCase_				, )

            # 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   :     Optional[int]	      =self.load_generation_config(self.args.generation_config    )
                  __snake_case   :     Any	      =gen_config


      @staticmethod
      def _UpperCamelCase    (     a					:						Union[str, GenerationConfig]    ):





            """simple docstring"""







            if isinstance(UpperCamelCase_				, UpperCamelCase_    ):
                  return deepcopy(UpperCamelCase_    )

            # str or Path
            __snake_case   :     Any	      =Path(UpperCamelCase_    ) if isinstance(UpperCamelCase_				, UpperCamelCase_    ) else gen_config_arg
            __snake_case   :     Union[str, 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   :     Tuple	      =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   :     int	      =gen_config_arg

            __snake_case   :     Optional[Any]	      =GenerationConfig.from_pretrained(UpperCamelCase_				, UpperCamelCase_    )
            return gen_config


      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						Optional[Dataset] = None				, a					:						Optional[List[str]] = None				, a					:						str = "eval"				, **a					:						Optional[int]				, ):





            """simple docstring"""







            __snake_case   :     int	      =gen_kwargs.copy()
            if gen_kwargs.get('''max_length'''    ) is None and gen_kwargs.get('''max_new_tokens'''    ) is None:
                  __snake_case   :     List[str]	      =self.args.generation_max_length
            __snake_case   :     List[str]	      =(
                gen_kwargs['num_beams'] if gen_kwargs.get('''num_beams'''    ) is not None else self.args.generation_num_beams
            )
            __snake_case   :     Any	      =gen_kwargs

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


      def _UpperCamelCase    (     self					:						List[str]				, a					:						Dataset				, a					:						Optional[List[str]] = None				, a					:						str = "test"				, **a					:						Optional[int]				, ):





            """simple docstring"""







            __snake_case   :     str	      =gen_kwargs.copy()
            if gen_kwargs.get('''max_length'''    ) is None and gen_kwargs.get('''max_new_tokens'''    ) is None:
                  __snake_case   :     str	      =self.args.generation_max_length
            __snake_case   :     List[str]	      =(
                gen_kwargs['num_beams'] if gen_kwargs.get('''num_beams'''    ) is not None else self.args.generation_num_beams
            )
            __snake_case   :     Optional[int]	      =gen_kwargs

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


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





            """simple docstring"""







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

            __snake_case   :     int	      ='labels' in inputs
            __snake_case   :     List[str]	      =self._prepare_inputs(UpperCamelCase_    )

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

            if len(UpperCamelCase_    ) == 0 and hasattr(self				, '''_gen_kwargs'''    ):
                  __snake_case   :     Optional[Any]	      =self._gen_kwargs.copy()

            if gen_kwargs.get('''max_length'''    ) is None and gen_kwargs.get('''max_new_tokens'''    ) is None:
                  __snake_case   :     int	      =self.model.config.max_length
            __snake_case   :     Optional[Any]	      =(
                gen_kwargs['num_beams'] if gen_kwargs.get('''num_beams'''    ) is not None else self.model.config.num_beams
            )
            __snake_case   :     List[Any]	      =True if is_deepspeed_zeroa_enabled() else False
            __snake_case   :     Optional[int]	      =(
                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   :     str	      ={k: v for k, v in inputs.items() if k != 'decoder_input_ids'}
            __snake_case   :     Tuple	      =self.model.generate(**UpperCamelCase_				, **UpperCamelCase_    )

            # 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   :     Union[str, Any]	      =False

            # Retrieves GenerationConfig from model.generation_config
            __snake_case   :     Tuple	      =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   :     Dict	      =self._pad_tensors_to_max_len(UpperCamelCase_				, 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   :     str	      =self._pad_tensors_to_max_len(UpperCamelCase_				, gen_config.max_new_tokens + 1    )

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

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

            if has_labels:
                  __snake_case   :     str	      =inputs['labels']
                  if labels.shape[-1] < gen_config.max_length:
                        __snake_case   :     str	      =self._pad_tensors_to_max_len(UpperCamelCase_				, 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   :     Union[str, Any]	      =self._pad_tensors_to_max_len(UpperCamelCase_				, gen_config.max_new_tokens + 1    )
            else:
                  __snake_case   :     Optional[Any]	      =None

            return loss, generated_tokens, labels







      def _UpperCamelCase    (     self					:						List[str]				, a					:						Optional[int]				, a					:						Optional[int]    ):





            """simple docstring"""







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








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






   @property
   def 			_a    (						self :							Union[str, Any]  )->      List[str]:
     return self.time_input_dim






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






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			int      =     MCLIPConfig(
         numDims=self.cross_attention_dim    ,       transformerDimensions=self.text_embedder_hidden_size    ,       hidden_size=self.text_embedder_hidden_size    ,       intermediate_size=37    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       vocab_size=10_05    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






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

     _lowercase:			Optional[Any]      =     {
         'in_channels': 4,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'text_image',
         '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,
         'encoder_hid_dim': self.text_embedder_hidden_size,
         'encoder_hid_dim_type': 'text_image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     _lowercase:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Optional[Any]:
     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }






   @property
   def 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			List[str]      =     self.dummy_text_encoder
     _lowercase:			List[Any]      =     self.dummy_tokenizer
     _lowercase:			Optional[Any]      =     self.dummy_unet
     _lowercase:			List[str]      =     self.dummy_movq

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

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

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

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     assert (
         np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
     ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
     ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'

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






   def 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
