


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 				__SCREAMING_SNAKE_CASE   (			A__  ,						unittest.TestCase   ):
      A				:							Union[str, Any]    							=     KandinskyVaaPriorEmbaEmbPipeline
      A				:							Optional[Any]    							=     ['prompt', 'image']
      A				:							List[Any]    							=     ['prompt', 'image']
      A				:							int    							=     [
          'num_images_per_prompt',
          'strength',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      A				:							Optional[int]    							=     False


      @property
      def        __lowerCamelCase			(							self		):
            return 32


      @property
      def        __lowerCamelCase			(							self		):
            return 32


      @property
      def        __lowerCamelCase			(							self		):
            return self.time_input_dim


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


      @property
      def        __lowerCamelCase			(							self		):
            return 100


      @property
      def        __lowerCamelCase			(							self		):
            lowercase			:   Tuple					   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
            return tokenizer


      @property
      def        __lowerCamelCase			(							self		):
            torch.manual_seed(0		)
            lowercase			:   List[str]					   =		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=1000							,						)
            return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE__		)


      @property
      def        __lowerCamelCase			(							self		):
            torch.manual_seed(0		)

            lowercase			:   Optional[Any]					   =		{
                '''num_attention_heads''': 2,
                '''attention_head_dim''': 12,
                '''embedding_dim''': self.text_embedder_hidden_size,
                '''num_layers''': 1,
            }

            lowercase			:   Tuple					   =		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
            lowercase			:   Tuple					   =		nn.Parameter(torch.ones(model.clip_std.shape		)		)
            return model


      @property
      def        __lowerCamelCase			(							self		):
            torch.manual_seed(0		)
            lowercase			:   List[Any]					   =		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							,						)

            lowercase			:   Optional[int]					   =		CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE__		)
            return model


      @property
      def        __lowerCamelCase			(							self		):
            lowercase			:   Tuple					   =		CLIPImageProcessor(
                crop_size=224							,						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=224							,						)

            return image_processor


      def        __lowerCamelCase			(							self		):
            lowercase			:   List[Any]					   =		self.dummy_prior
            lowercase			:   Any					   =		self.dummy_image_encoder
            lowercase			:   Optional[int]					   =		self.dummy_text_encoder
            lowercase			:   int					   =		self.dummy_tokenizer
            lowercase			:   int					   =		self.dummy_image_processor

            lowercase			:   Union[str, Any]					   =		UnCLIPScheduler(
                variance_type='''fixed_small_log'''							,						prediction_type='''sample'''							,						num_train_timesteps=1000							,						clip_sample=SCREAMING_SNAKE_CASE__							,						clip_sample_range=10.0							,						)

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

            return components


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=0		):
            if str(SCREAMING_SNAKE_CASE__		).startswith('''mps'''		):
                  lowercase			:   Tuple					   =		torch.manual_seed(SCREAMING_SNAKE_CASE__		)
            else:
                  lowercase			:   Optional[int]					   =		torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(SCREAMING_SNAKE_CASE__		)

            lowercase			:   Optional[Any]					   =		floats_tensor((1, 3, 64, 64)							,						rng=random.Random(SCREAMING_SNAKE_CASE__		)		).to(SCREAMING_SNAKE_CASE__		)
            lowercase			:   Union[str, Any]					   =		image.cpu().permute(0							,						2							,						3							,						1		)[0]
            lowercase			:   str					   =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__		)		).convert('''RGB'''		).resize((256, 256)		)

            lowercase			:   List[Any]					   =		{
                '''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		):
            lowercase			:   Any					   =		'''cpu'''

            lowercase			:   Dict					   =		self.get_dummy_components()

            lowercase			:   Optional[Any]					   =		self.pipeline_class(**SCREAMING_SNAKE_CASE__		)
            lowercase			:   Any					   =		pipe.to(SCREAMING_SNAKE_CASE__		)

            pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__		)

            lowercase			:   List[Any]					   =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__		)		)
            lowercase			:   List[str]					   =		output.image_embeds

            lowercase			:   str					   =		pipe(
                **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__		)							,						return_dict=SCREAMING_SNAKE_CASE__							,						)[0]

            lowercase			:   Union[str, Any]					   =		image[0, -10:]
            lowercase			:   Dict					   =		image_from_tuple[0, -10:]

            assert image.shape == (1, 32)

            lowercase			:   List[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		):
            lowercase			:   str					   =		torch_device == '''cpu'''
            lowercase			:   str					   =		True
            lowercase			:   Dict					   =		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		):
            lowercase			:   Union[str, Any]					   =		torch_device == '''cpu'''
            lowercase			:   List[str]					   =		False

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   				=      {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   				=      [
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

