


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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   a__	(						lowerCAmelCase__						,       unittest.TestCase    ):
       lowerCamelCase__:     Union[str, Any]							    =     KandinskyVaaControlnetImgaImgPipeline
       lowerCamelCase__:     Optional[Any]							    =     ["image_embeds", "negative_image_embeds", "image", "hint"]
       lowerCamelCase__:     Optional[Any]							    =     ["image_embeds", "negative_image_embeds", "image", "hint"]
       lowerCamelCase__:     List[Any]							    =     [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       lowerCamelCase__:     Optional[int]							    =     False

       @property
       def 						UpperCAmelCase(   self      :     int    ):
              return 3_2

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
              return 3_2

       @property
       def 						UpperCAmelCase(   self      :     List[Any]    ):
              return self.time_input_dim

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

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
              return 1_0_0

       @property
       def 						UpperCAmelCase(   self      :     Dict    ):
              torch.manual_seed(0    )

              a_    :     Union[str, Any]										=    {
                  """in_channels""": 8,
                  # Out channels is double in channels because predicts mean and variance
                  """out_channels""": 8,
                  """addition_embed_type""": """image_hint""",
                  """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""": """image_proj""",
                  """cross_attention_dim""": self.cross_attention_dim,
                  """attention_head_dim""": 4,
                  """resnet_time_scale_shift""": """scale_shift""",
                  """class_embed_type""": None,
              }

              a_    :     Any										=    UNetaDConditionModel(**_lowerCamelCase    )
              return model

       @property
       def 						UpperCAmelCase(   self      :     Dict    ):
              return {
                  "block_out_channels": [3_2, 3_2, 6_4, 6_4],
                  "down_block_types": [
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "AttnDownEncoderBlock2D",
                  ],
                  "in_channels": 3,
                  "latent_channels": 4,
                  "layers_per_block": 1,
                  "norm_num_groups": 8,
                  "norm_type": "spatial",
                  "num_vq_embeddings": 1_2,
                  "out_channels": 3,
                  "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "vq_embed_dim": 4,
              }

       @property
       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
              torch.manual_seed(0    )
              a_    :     Optional[Any]										=    VQModel(**self.dummy_movq_kwargs    )
              return model

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
              a_    :     Optional[Any]										=    self.dummy_unet
              a_    :     Tuple										=    self.dummy_movq

              a_    :     Dict										=    {
                  """num_train_timesteps""": 1_0_0_0,
                  """beta_schedule""": """linear""",
                  """beta_start""": 0.0_0_0_8_5,
                  """beta_end""": 0.0_1_2,
                  """clip_sample""": False,
                  """set_alpha_to_one""": False,
                  """steps_offset""": 0,
                  """prediction_type""": """epsilon""",
                  """thresholding""": False,
              }

              a_    :     Optional[Any]										=    DDIMScheduler(**_lowerCamelCase    )

              a_    :     str										=    {
                  """unet""": unet,
                  """scheduler""": scheduler,
                  """movq""": movq,
              }

              return components

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     str=0    ):
              a_    :     str										=    floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
              a_    :     int										=    floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(seed + 1    )    ).to(
                  _lowerCamelCase    )
              # create init_image
              a_    :     Dict										=    floats_tensor((1, 3, 6_4, 6_4)      ,							rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
              a_    :     Union[str, Any]										=    image.cpu().permute(0      ,							2      ,							3      ,							1    )[0]
              a_    :     List[Any]										=    Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert("""RGB"""    ).resize((2_5_6, 2_5_6)    )
              # create hint
              a_    :     Dict										=    floats_tensor((1, 3, 6_4, 6_4)      ,							rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )

              if str(_lowerCamelCase    ).startswith("""mps"""    ):
                     a_    :     str										=    torch.manual_seed(_lowerCamelCase    )
              else:
                     a_    :     str										=    torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
              a_    :     Tuple										=    {
                  """image""": init_image,
                  """image_embeds""": image_embeds,
                  """negative_image_embeds""": negative_image_embeds,
                  """hint""": hint,
                  """generator""": generator,
                  """height""": 6_4,
                  """width""": 6_4,
                  """num_inference_steps""": 1_0,
                  """guidance_scale""": 7.0,
                  """strength""": 0.2,
                  """output_type""": """np""",
              }
              return inputs

       def 						UpperCAmelCase(   self      :     int    ):
              a_    :     Dict										=    """cpu"""

              a_    :     Any										=    self.get_dummy_components()

              a_    :     Optional[Any]										=    self.pipeline_class(**_lowerCamelCase    )
              a_    :     Union[str, Any]										=    pipe.to(_lowerCamelCase    )

              pipe.set_progress_bar_config(disable=_lowerCamelCase    )

              a_    :     Dict										=    pipe(**self.get_dummy_inputs(_lowerCamelCase    )    )
              a_    :     Union[str, Any]										=    output.images

              a_    :     Optional[Any]										=    pipe(
                  **self.get_dummy_inputs(_lowerCamelCase    )      ,							return_dict=_lowerCamelCase      ,							)[0]

              a_    :     Dict										=    image[0, -3:, -3:, -1]
              a_    :     List[str]										=    image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 6_4, 6_4, 3)

              a_    :     Union[str, Any]										=    np.array(
                  [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]    )
              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   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     List[str]    ):
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def 						UpperCAmelCase(   self      :     List[str]    ):
              a_    :     Any										=    load_numpy(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""    )

              a_    :     Any										=    load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""    )
              a_    :     Optional[int]										=    init_image.resize((5_1_2, 5_1_2)    )

              a_    :     Dict										=    load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/kandinskyv22/hint_image_cat.png"""    )
              a_    :     Optional[int]										=    torch.from_numpy(np.array(_lowerCamelCase    )    ).float() / 2_5_5.0
              a_    :     int										=    hint.permute(2      ,							0      ,							1    ).unsqueeze(0    )

              a_    :     Dict										=    """A robot, 4k photo"""

              a_    :     List[Any]										=    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
                  """kandinsky-community/kandinsky-2-2-prior"""      ,							torch_dtype=torch.floataa    )
              pipe_prior.to(_lowerCamelCase    )

              a_    :     Any										=    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                  """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,							torch_dtype=torch.floataa    )
              a_    :     Dict										=    pipeline.to(_lowerCamelCase    )

              pipeline.set_progress_bar_config(disable=_lowerCamelCase    )

              a_    :     Any										=    torch.Generator(device="""cpu"""    ).manual_seed(0    )

              a_			,			a_    :     Dict										=    pipe_prior(
                  _lowerCamelCase      ,							image=_lowerCamelCase      ,							strength=0.8_5      ,							generator=_lowerCamelCase      ,							negative_prompt=""""""      ,							).to_tuple()

              a_    :     Dict										=    pipeline(
                  image=_lowerCamelCase      ,							image_embeds=_lowerCamelCase      ,							negative_image_embeds=_lowerCamelCase      ,							hint=_lowerCamelCase      ,							generator=_lowerCamelCase      ,							num_inference_steps=1_0_0      ,							height=5_1_2      ,							width=5_1_2      ,							strength=0.5      ,							output_type="""np"""      ,							)

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

              assert image.shape == (5_1_2, 5_1_2, 3)

              assert_mean_pixel_difference(_lowerCamelCase      ,							_lowerCamelCase    )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



