



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    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        UpperCAmelCase_			(			_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						KandinskyVaaControlnetPipeline
    lowercase   =						["image_embeds", "negative_image_embeds", "hint"]
    lowercase   =						["image_embeds", "negative_image_embeds", "hint"]
    lowercase   =						[
        "generator",
        "height",
        "width",
        "latents",
        "guidance_scale",
        "num_inference_steps",
        "return_dict",
        "guidance_scale",
        "num_images_per_prompt",
        "output_type",
        "return_dict",
    ]
    lowercase   =						False




    @property
    def lowerCamelCase			(       self   :	Optional[Any]		):
          return 32




    @property
    def lowerCamelCase			(       self   :	Optional[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   :	int		):
          return 100




    @property
    def lowerCamelCase			(       self   :	Any		):
          torch.manual_seed(0		)

          snake_case__    :       List[str]       		= {
              """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,
          }

          snake_case__    :       str       		= UNetaDConditionModel(**snake_case_		)
          return model




    @property
    def lowerCamelCase			(       self   :	Optional[int]		):
          return {
              "block_out_channels": [32, 32, 64, 64],
              "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": 12,
              "out_channels": 3,
              "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
              "vq_embed_dim": 4,
          }




    @property
    def lowerCamelCase			(       self   :	List[str]		):
          torch.manual_seed(0		)
          snake_case__    :       Tuple       		= VQModel(**self.dummy_movq_kwargs		)
          return model




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Union[str, Any]       		= self.dummy_unet
          snake_case__    :       Optional[Any]       		= self.dummy_movq

          snake_case__    :       Optional[int]       		= DDIMScheduler(
              num_train_timesteps=1_000					,	beta_schedule="""linear"""					,	beta_start=0.00085					,	beta_end=0.012					,	clip_sample=snake_case_					,	set_alpha_to_one=snake_case_					,	steps_offset=1					,	prediction_type="""epsilon"""					,	thresholding=snake_case_					,	)

          snake_case__    :       List[str]       		= {
              """unet""": unet,
              """scheduler""": scheduler,
              """movq""": movq,
          }
          return components




    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	Any					,	snake_case_   :	Optional[Any]=0		):
          snake_case__    :       str       		= floats_tensor((1, self.text_embedder_hidden_size)					,	rng=random.Random(snake_case_		)		).to(snake_case_		)
          snake_case__    :       Optional[int]       		= floats_tensor((1, self.text_embedder_hidden_size)					,	rng=random.Random(seed + 1		)		).to(
              snake_case_		)

          # create hint
          snake_case__    :       Tuple       		= floats_tensor((1, 3, 64, 64)					,	rng=random.Random(snake_case_		)		).to(snake_case_		)

          if str(snake_case_		).startswith("""mps"""		):
                snake_case__    :       List[str]       		= torch.manual_seed(snake_case_		)
          else:
                snake_case__    :       Optional[Any]       		= torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
          snake_case__    :       str       		= {
              """image_embeds""": image_embeds,
              """negative_image_embeds""": negative_image_embeds,
              """hint""": hint,
              """generator""": generator,
              """height""": 64,
              """width""": 64,
              """guidance_scale""": 4.0,
              """num_inference_steps""": 2,
              """output_type""": """np""",
          }
          return inputs




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       Optional[Any]       		= """cpu"""

          snake_case__    :       Tuple       		= self.get_dummy_components()

          snake_case__    :       Any       		= self.pipeline_class(**snake_case_		)
          snake_case__    :       str       		= pipe.to(snake_case_		)

          pipe.set_progress_bar_config(disable=snake_case_		)

          snake_case__    :       Optional[int]       		= pipe(**self.get_dummy_inputs(snake_case_		)		)
          snake_case__    :       str       		= output.images

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

          snake_case__    :       List[Any]       		= image[0, -3:, -3:, -1]
          snake_case__    :       Union[str, Any]       		= image_from_tuple[0, -3:, -3:, -1]

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

          snake_case__    :       Optional[Any]       		= np.array(
              [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]		)

          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      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	List[str]		):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Optional[int]       		= load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""		)

          snake_case__    :       str       		= load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinskyv22/hint_image_cat.png"""		)
          snake_case__    :       List[str]       		= torch.from_numpy(np.array(snake_case_		)		).float() / 255.0
          snake_case__    :       Optional[int]       		= hint.permute(2					,	0					,	1		).unsqueeze(0		)

          snake_case__    :       int       		= KandinskyVaaPriorPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-prior"""					,	torch_dtype=torch.floataa		)
          pipe_prior.to(snake_case_		)

          snake_case__    :       str       		= KandinskyVaaControlnetPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-2-controlnet-depth"""					,	torch_dtype=torch.floataa		)
          snake_case__    :       Optional[int]       		= pipeline.to(snake_case_		)
          pipeline.set_progress_bar_config(disable=snake_case_		)

          snake_case__    :       int       		= """A robot, 4k photo"""

          snake_case__    :       Optional[Any]       		= torch.Generator(device="""cuda"""		).manual_seed(0		)
          snake_case__		,							snake_case__    :       Dict       		= pipe_prior(
              snake_case_					,	generator=snake_case_					,	num_inference_steps=5					,	negative_prompt=""""""					,	).to_tuple()

          snake_case__    :       str       		= torch.Generator(device="""cuda"""		).manual_seed(0		)
          snake_case__    :       int       		= pipeline(
              image_embeds=snake_case_					,	negative_image_embeds=snake_case_					,	hint=snake_case_					,	generator=snake_case_					,	num_inference_steps=100					,	output_type="""np"""					,	)

          snake_case__    :       Union[str, Any]       		= output.images[0]

          assert image.shape == (512, 512, 3)

          assert_mean_pixel_difference(snake_case_					,	snake_case_		)





'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a  			=						logging.getLogger(__name__)







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						42







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						None
    lowercase   =						None







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"train"
    lowercase   =						"dev"
    lowercase   =						"test"







class        UpperCAmelCase_			:
    """simple docstring"""







    @staticmethod
    def lowerCamelCase			(       snake_case_   :	Optional[Any]					,	snake_case_   :	Union[Split, str]		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	str		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	List[InputExample]					,	snake_case_   :	List[str]					,	snake_case_   :	int					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	Optional[int]=False					,	snake_case_   :	Dict="[CLS]"					,	snake_case_   :	str=1					,	snake_case_   :	Dict="[SEP]"					,	snake_case_   :	List[str]=False					,	snake_case_   :	int=False					,	snake_case_   :	Tuple=0					,	snake_case_   :	Union[str, Any]=0					,	snake_case_   :	List[Any]=-100					,	snake_case_   :	Any=0					,	snake_case_   :	Union[str, Any]=True					,	):
          snake_case__    :       int       		= {label: i for i, label in enumerate(snake_case_		)}

          snake_case__    :       List[Any]       		= []
          for ex_index, example in enumerate(snake_case_		):
                if ex_index % 10_000 == 0:
                      logger.info("""Writing example %d of %d"""					,	snake_case_					,	len(snake_case_		)		)

                snake_case__    :       Tuple       		= []
                snake_case__    :       Dict       		= []
                for word, label in zip(example.words					,	example.labels		):
                      snake_case__    :       Any       		= tokenizer.tokenize(snake_case_		)

                      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                      if len(snake_case_		) > 0:
                            tokens.extend(snake_case_		)
                            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                snake_case__    :       Dict       		= tokenizer.num_special_tokens_to_add()
                if len(snake_case_		) > max_seq_length - special_tokens_count:
                      snake_case__    :       Tuple       		= tokens[: (max_seq_length - special_tokens_count)]
                      snake_case__    :       Tuple       		= label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                      # roberta uses an extra separator b/w pairs of sentences
                      tokens += [sep_token]
                      label_ids += [pad_token_label_id]
                snake_case__    :       Dict       		= [sequence_a_segment_id] * len(snake_case_		)

                if cls_token_at_end:
                      tokens += [cls_token]
                      label_ids += [pad_token_label_id]
                      segment_ids += [cls_token_segment_id]
                else:
                      snake_case__    :       str       		= [cls_token] + tokens
                      snake_case__    :       Union[str, Any]       		= [pad_token_label_id] + label_ids
                      snake_case__    :       Any       		= [cls_token_segment_id] + segment_ids

                snake_case__    :       Union[str, Any]       		= tokenizer.convert_tokens_to_ids(snake_case_		)

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                snake_case__    :       Tuple       		= [1 if mask_padding_with_zero else 0] * len(snake_case_		)

                # Zero-pad up to the sequence length.
                snake_case__    :       Dict       		= max_seq_length - len(snake_case_		)
                if pad_on_left:
                      snake_case__    :       Optional[Any]       		= ([pad_token] * padding_length) + input_ids
                      snake_case__    :       Union[str, Any]       		= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      snake_case__    :       Union[str, Any]       		= ([pad_token_segment_id] * padding_length) + segment_ids
                      snake_case__    :       Union[str, Any]       		= ([pad_token_label_id] * padding_length) + label_ids
                else:
                      input_ids += [pad_token] * padding_length
                      input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                      segment_ids += [pad_token_segment_id] * padding_length
                      label_ids += [pad_token_label_id] * padding_length

                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length

                if ex_index < 5:
                      logger.info("""*** Example ***"""		)
                      logger.info("""guid: %s"""					,	example.guid		)
                      logger.info("""tokens: %s"""					,	""" """.join([str(snake_case_		) for x in tokens]		)		)
                      logger.info("""input_ids: %s"""					,	""" """.join([str(snake_case_		) for x in input_ids]		)		)
                      logger.info("""input_mask: %s"""					,	""" """.join([str(snake_case_		) for x in input_mask]		)		)
                      logger.info("""segment_ids: %s"""					,	""" """.join([str(snake_case_		) for x in segment_ids]		)		)
                      logger.info("""label_ids: %s"""					,	""" """.join([str(snake_case_		) for x in label_ids]		)		)

                if "token_type_ids" not in tokenizer.model_input_names:
                      snake_case__    :       Tuple       		= None

                features.append(
                    InputFeatures(
                        input_ids=snake_case_					,	attention_mask=snake_case_					,	token_type_ids=snake_case_					,	label_ids=snake_case_		)		)
          return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset







       class        UpperCAmelCase_			(			_a      ):
           """simple docstring"""







           lowercase   =						42
           lowercase   =						nn.CrossEntropyLoss().ignore_index




           def __init__(       self   :	int					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Tuple=False					,	snake_case_   :	Split = Split.train					,	):
                 # Load data features from cache or dataset file
                 snake_case__    :       Tuple       		= os.path.join(
                     snake_case_					,	"""cached_{}_{}_{}""".format(mode.value					,	tokenizer.__class__.__name__					,	str(snake_case_		)		)					,	)

                 # Make sure only the first process in distributed training processes the dataset,
                 # and the others will use the cache.
                 snake_case__    :       Dict       		= cached_features_file + """.lock"""
                 with FileLock(snake_case_		):
                       if os.path.exists(snake_case_		) and not overwrite_cache:
                             logger.info(f"Loading features from cached file {cached_features_file}"		)
                             snake_case__    :       Tuple       		= torch.load(snake_case_		)
                       else:
                             logger.info(f"Creating features from dataset file at {data_dir}"		)
                             snake_case__    :       Any       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                             # TODO clean up all this to leverage built-in features of tokenizers
                             snake_case__    :       Any       		= token_classification_task.convert_examples_to_features(
                                 snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)
                             logger.info(f"Saving features into cached file {cached_features_file}"		)
                             torch.save(self.features					,	snake_case_		)




           def __len__(       self   :	str		):
                 return len(self.features		)




           def __getitem__(       self   :	int					,	snake_case_   :	Dict		):
                 return self.features[i]


if is_tf_available():
       import tensorflow as tf







       class        UpperCAmelCase_			:
           """simple docstring"""







           lowercase   =						42
           lowercase   =						-1_00




           def __init__(       self   :	List[str]					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Any=False					,	snake_case_   :	Split = Split.train					,	):
                 snake_case__    :       int       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case__    :       Optional[Any]       		= token_classification_task.convert_examples_to_features(
                     snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	cls_token_at_end=bool(model_type in ["""xlnet"""]		)					,	cls_token=tokenizer.cls_token					,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0					,	sep_token=tokenizer.sep_token					,	sep_token_extra=snake_case_					,	pad_on_left=bool(tokenizer.padding_side == """left"""		)					,	pad_token=tokenizer.pad_token_id					,	pad_token_segment_id=tokenizer.pad_token_type_id					,	pad_token_label_id=self.pad_token_label_id					,	)

                 def gen():
                       for ex in self.features:
                             if ex.token_type_ids is None:
                                   yield (
                                       {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                       ex.label_ids,
                                   )
                             else:
                                   yield (
                                       {
                                           "input_ids": ex.input_ids,
                                           "attention_mask": ex.attention_mask,
                                           "token_type_ids": ex.token_type_ids,
                                       },
                                       ex.label_ids,
                                   )

                 if "token_type_ids" not in tokenizer.model_input_names:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,	(
                               {"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)},
                               tf.TensorShape([None]		),
                           )					,	)
                 else:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)					,	(
                               {
                                   """input_ids""": tf.TensorShape([None]		),
                                   """attention_mask""": tf.TensorShape([None]		),
                                   """token_type_ids""": tf.TensorShape([None]		),
                               },
                               tf.TensorShape([None]		),
                           )					,	)




           def lowerCamelCase			(       self   :	Optional[Any]		):
                 snake_case__    :       Optional[Any]       		= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

                 return self.dataset




           def __len__(       self   :	Optional[Any]		):
                 return len(self.features		)




           def __getitem__(       self   :	str					,	snake_case_   :	List[str]		):
                 return self.features[i]
