





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np

class 		snake_case_   :







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Union[str, Any]:

			'''simple docstring'''




			torch.manual_seed(0   )
			__lowercase											=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

			torch.manual_seed(0   )
			__lowercase											=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

			torch.manual_seed(0   )
			__lowercase											=     UNetaDConditionModel(
			    sample_size=32					,      layers_per_block=1					,      block_out_channels=[32, 64]					,      down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]					,      mid_block_type='UNetMidBlock2DSimpleCrossAttn'					,      up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']					,      in_channels=3					,      out_channels=6					,      cross_attention_dim=32					,      encoder_hid_dim=32					,      attention_head_dim=8					,      addition_embed_type='text'					,      addition_embed_type_num_heads=2					,      cross_attention_norm='group_norm'					,      resnet_time_scale_shift='scale_shift'					,      act_fn='gelu'					,      )
			unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

			torch.manual_seed(0   )
			__lowercase											=     DDPMScheduler(
			    num_train_timesteps=1_000					,      beta_schedule='squaredcos_cap_v2'					,      beta_start=0.0001					,      beta_end=0.02					,      thresholding=_SCREAMING_SNAKE_CASE					,      dynamic_thresholding_ratio=0.95					,      sample_max_value=1.0					,      prediction_type='epsilon'					,      variance_type='learned_range'					,      )

			torch.manual_seed(0   )
			__lowercase											=     IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Dict:

			'''simple docstring'''




			torch.manual_seed(0   )
			__lowercase											=     TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

			torch.manual_seed(0   )
			__lowercase											=     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

			torch.manual_seed(0   )
			__lowercase											=     UNetaDConditionModel(
			    sample_size=32					,      layers_per_block=[1, 2]					,      block_out_channels=[32, 64]					,      down_block_types=[
			        'ResnetDownsampleBlock2D',
			        'SimpleCrossAttnDownBlock2D',
			    ]					,      mid_block_type='UNetMidBlock2DSimpleCrossAttn'					,      up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']					,      in_channels=6					,      out_channels=6					,      cross_attention_dim=32					,      encoder_hid_dim=32					,      attention_head_dim=8					,      addition_embed_type='text'					,      addition_embed_type_num_heads=2					,      cross_attention_norm='group_norm'					,      resnet_time_scale_shift='scale_shift'					,      act_fn='gelu'					,      class_embed_type='timestep'					,      mid_block_scale_factor=1.414					,      time_embedding_act_fn='gelu'					,      time_embedding_dim=32					,      )
			unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

			torch.manual_seed(0   )
			__lowercase											=     DDPMScheduler(
			    num_train_timesteps=1_000					,      beta_schedule='squaredcos_cap_v2'					,      beta_start=0.0001					,      beta_end=0.02					,      thresholding=_SCREAMING_SNAKE_CASE					,      dynamic_thresholding_ratio=0.95					,      sample_max_value=1.0					,      prediction_type='epsilon'					,      variance_type='learned_range'					,      )

			torch.manual_seed(0   )
			__lowercase											=     DDPMScheduler(
			    num_train_timesteps=1_000					,      beta_schedule='squaredcos_cap_v2'					,      beta_start=0.0001					,      beta_end=0.02					,      )

			torch.manual_seed(0   )
			__lowercase											=     IFWatermarker()

			return {
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "unet": unet,
			    "scheduler": scheduler,
			    "image_noising_scheduler": image_noising_scheduler,
			    "watermarker": watermarker,
			    "safety_checker": None,
			    "feature_extractor": None,
			}




	def    UpperCAmelCase			(						self							:     int   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     self.get_dummy_components()
			__lowercase											=     self.pipeline_class(**_SCREAMING_SNAKE_CASE   )
			pipe.to(_SCREAMING_SNAKE_CASE   )
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

			__lowercase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )

			__lowercase											=     inputs['prompt']
			__lowercase											=     inputs['generator']
			__lowercase											=     inputs['num_inference_steps']
			__lowercase											=     inputs['output_type']

			if "image" in inputs:
					__lowercase											=     inputs['image']
			else:
					__lowercase											=     None

			if "mask_image" in inputs:
					__lowercase											=     inputs['mask_image']
			else:
					__lowercase											=     None

			if "original_image" in inputs:
					__lowercase											=     inputs['original_image']
			else:
					__lowercase											=     None

			__lowercase						,      __lowercase											=     pipe.encode_prompt(_SCREAMING_SNAKE_CASE   )

			# inputs with prompt converted to embeddings
			__lowercase											=     {
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					__lowercase											=     image

			if mask_image is not None:
					__lowercase											=     mask_image

			if original_image is not None:
					__lowercase											=     original_image

			# set all optional components to None
			for optional_component in pipe._optional_components:
					setattr(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE   )

			__lowercase											=     pipe(**_SCREAMING_SNAKE_CASE   )[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(_SCREAMING_SNAKE_CASE   )
					__lowercase											=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE   )
					pipe_loaded.to(_SCREAMING_SNAKE_CASE   )
					pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

			for optional_component in pipe._optional_components:
					self.assertTrue(
					    getattr(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE   ) is None					,      F"`{optional_component}` did not stay set to None after loading."					,      )

			__lowercase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )

			__lowercase											=     inputs['generator']
			__lowercase											=     inputs['num_inference_steps']
			__lowercase											=     inputs['output_type']

			# inputs with prompt converted to embeddings
			__lowercase											=     {
			    'prompt_embeds': prompt_embeds,
			    'negative_prompt_embeds': negative_prompt_embeds,
			    'generator': generator,
			    'num_inference_steps': num_inference_steps,
			    'output_type': output_type,
			}

			if image is not None:
					__lowercase											=     image

			if mask_image is not None:
					__lowercase											=     mask_image

			if original_image is not None:
					__lowercase											=     original_image

			__lowercase											=     pipe_loaded(**_SCREAMING_SNAKE_CASE   )[0]

			__lowercase											=     np.abs(to_np(_SCREAMING_SNAKE_CASE   ) - to_np(_SCREAMING_SNAKE_CASE   )   ).max()
			self.assertLess(_SCREAMING_SNAKE_CASE					,      1E-4   )






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     self.get_dummy_components()
			__lowercase											=     self.pipeline_class(**_SCREAMING_SNAKE_CASE   )
			pipe.to(_SCREAMING_SNAKE_CASE   )
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

			__lowercase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )
			__lowercase											=     pipe(**_SCREAMING_SNAKE_CASE   )[0]

			with tempfile.TemporaryDirectory() as tmpdir:
					pipe.save_pretrained(_SCREAMING_SNAKE_CASE   )
					__lowercase											=     self.pipeline_class.from_pretrained(_SCREAMING_SNAKE_CASE   )
					pipe_loaded.to(_SCREAMING_SNAKE_CASE   )
					pipe_loaded.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE   )

			pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

			__lowercase											=     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE   )
			__lowercase											=     pipe_loaded(**_SCREAMING_SNAKE_CASE   )[0]

			__lowercase											=     np.abs(to_np(_SCREAMING_SNAKE_CASE   ) - to_np(_SCREAMING_SNAKE_CASE   )   ).max()
			self.assertLess(_SCREAMING_SNAKE_CASE					,      1E-4   )







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:  Optional[int]							       =				logging.get_logger(__name__)

_UpperCamelCase						:  Tuple							       =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''canine'''

  def __init__(				self      , _SCREAMING_SNAKE_CASE=7_68      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=12      , _SCREAMING_SNAKE_CASE=30_72      , _SCREAMING_SNAKE_CASE="gelu"      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=0.1      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=16      , _SCREAMING_SNAKE_CASE=0.02      , _SCREAMING_SNAKE_CASE=1e-12      , _SCREAMING_SNAKE_CASE=0      , _SCREAMING_SNAKE_CASE=0Xe0_00      , _SCREAMING_SNAKE_CASE=0Xe0_01      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=4      , _SCREAMING_SNAKE_CASE=8      , _SCREAMING_SNAKE_CASE=1_63_84      , _SCREAMING_SNAKE_CASE=1_28      , **_SCREAMING_SNAKE_CASE      , ):





      '''simple docstring'''

      super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      , bos_token_id=_SCREAMING_SNAKE_CASE      , eos_token_id=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							max_position_embeddings
      lowerCAmelCase 						=							hidden_size
      lowerCAmelCase 						=							num_hidden_layers
      lowerCAmelCase 						=							num_attention_heads
      lowerCAmelCase 						=							intermediate_size
      lowerCAmelCase 						=							hidden_act
      lowerCAmelCase 						=							hidden_dropout_prob
      lowerCAmelCase 						=							attention_probs_dropout_prob
      lowerCAmelCase 						=							initializer_range
      lowerCAmelCase 						=							type_vocab_size
      lowerCAmelCase 						=							layer_norm_eps

      # Character config:
      lowerCAmelCase 						=							downsampling_rate
      lowerCAmelCase 						=							upsampling_kernel_size
      lowerCAmelCase 						=							num_hash_functions
      lowerCAmelCase 						=							num_hash_buckets
      lowerCAmelCase 						=							local_transformer_stride





