



"""simple docstring"""







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       UpperCamelCase__   :




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        torch.manual_seed(0       )
        lowerCAmelCase_   :				Optional[int]								= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

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

        torch.manual_seed(0       )
        lowerCAmelCase_   :				int								= UNetaDConditionModel(
            sample_size=3_2    ,			layers_per_block=1    ,			block_out_channels=[3_2, 6_4]    ,			down_block_types=[
                'ResnetDownsampleBlock2D',
                'SimpleCrossAttnDownBlock2D',
            ]    ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'    ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']    ,			in_channels=3    ,			out_channels=6    ,			cross_attention_dim=3_2    ,			encoder_hid_dim=3_2    ,			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       )
        lowerCAmelCase_   :				int								= DDPMScheduler(
            num_train_timesteps=1_0_0_0    ,			beta_schedule='squaredcos_cap_v2'    ,			beta_start=0.00_01    ,			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       )
        lowerCAmelCase_   :				int								= IFWatermarker()

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        torch.manual_seed(0       )
        lowerCAmelCase_   :				Optional[int]								= TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'       )

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

        torch.manual_seed(0       )
        lowerCAmelCase_   :				str								= UNetaDConditionModel(
            sample_size=3_2    ,			layers_per_block=[1, 2]    ,			block_out_channels=[3_2, 6_4]    ,			down_block_types=[
                'ResnetDownsampleBlock2D',
                'SimpleCrossAttnDownBlock2D',
            ]    ,			mid_block_type='UNetMidBlock2DSimpleCrossAttn'    ,			up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']    ,			in_channels=6    ,			out_channels=6    ,			cross_attention_dim=3_2    ,			encoder_hid_dim=3_2    ,			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=1.4_14    ,			time_embedding_act_fn='gelu'    ,			time_embedding_dim=3_2    ,			)
        unet.set_attn_processor(AttnAddedKVProcessor()       )  # For reproducibility tests

        torch.manual_seed(0       )
        lowerCAmelCase_   :				str								= DDPMScheduler(
            num_train_timesteps=1_0_0_0    ,			beta_schedule='squaredcos_cap_v2'    ,			beta_start=0.00_01    ,			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       )
        lowerCAmelCase_   :				Dict								= DDPMScheduler(
            num_train_timesteps=1_0_0_0    ,			beta_schedule='squaredcos_cap_v2'    ,			beta_start=0.00_01    ,			beta_end=0.02    ,			)

        torch.manual_seed(0       )
        lowerCAmelCase_   :				Optional[Any]								= 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      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				int								= self.get_dummy_components()
        lowerCAmelCase_   :				Tuple								= self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
        pipe.to(SCREAMING_SNAKE_CASE_       )
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[str]								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= inputs['prompt']
        lowerCAmelCase_   :				Any								= inputs['generator']
        lowerCAmelCase_   :				Any								= inputs['num_inference_steps']
        lowerCAmelCase_   :				Union[str, Any]								= inputs['output_type']

        if "image" in inputs:
          lowerCAmelCase_   :				Optional[int]								= inputs['image']
        else:
          lowerCAmelCase_   :				Dict								= None

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

        if "original_image" in inputs:
          lowerCAmelCase_   :				Any								= inputs['original_image']
        else:
          lowerCAmelCase_   :				Optional[int]								= None

        lowerCAmelCase_						,lowerCAmelCase_   :				Union[str, Any]								= pipe.encode_prompt(SCREAMING_SNAKE_CASE_       )

        # inputs with prompt converted to embeddings
        lowerCAmelCase_   :				Optional[Any]								= {
            '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:
          lowerCAmelCase_   :				Optional[Any]								= image

        if mask_image is not None:
          lowerCAmelCase_   :				Union[str, Any]								= mask_image

        if original_image is not None:
          lowerCAmelCase_   :				List[str]								= 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_       )

        lowerCAmelCase_   :				List[str]								= pipe(**SCREAMING_SNAKE_CASE_       )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
          pipe.save_pretrained(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= 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."    ,			)

        lowerCAmelCase_   :				List[str]								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Tuple								= inputs['generator']
        lowerCAmelCase_   :				int								= inputs['num_inference_steps']
        lowerCAmelCase_   :				str								= inputs['output_type']

        # inputs with prompt converted to embeddings
        lowerCAmelCase_   :				int								= {
            '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:
          lowerCAmelCase_   :				Dict								= image

        if mask_image is not None:
          lowerCAmelCase_   :				int								= mask_image

        if original_image is not None:
          lowerCAmelCase_   :				List[Any]								= original_image

        lowerCAmelCase_   :				List[Any]								= pipe_loaded(**SCREAMING_SNAKE_CASE_       )[0]

        lowerCAmelCase_   :				Optional[Any]								= np.abs(to_np(SCREAMING_SNAKE_CASE_       ) - to_np(SCREAMING_SNAKE_CASE_       )       ).max()
        self.assertLess(SCREAMING_SNAKE_CASE_    ,			1E-4       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				Dict								= self.get_dummy_components()
        lowerCAmelCase_   :				Union[str, Any]								= self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
        pipe.to(SCREAMING_SNAKE_CASE_       )
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Dict								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Tuple								= pipe(**SCREAMING_SNAKE_CASE_       )[0]

        with tempfile.TemporaryDirectory() as tmpdir:
          pipe.save_pretrained(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= 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

        lowerCAmelCase_   :				Union[str, Any]								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= pipe_loaded(**SCREAMING_SNAKE_CASE_       )[0]

        lowerCAmelCase_   :				int								= np.abs(to_np(SCREAMING_SNAKE_CASE_       ) - to_np(SCREAMING_SNAKE_CASE_       )       ).max()
        self.assertLess(SCREAMING_SNAKE_CASE_    ,			1E-4       )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:				int          =	{"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Optional[int]          =	["""MBartTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Dict          =	["""MBartTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Any          =	[
         """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MBartForCausalLM""",
         """MBartForConditionalGeneration""",
         """MBartForQuestionAnswering""",
         """MBartForSequenceClassification""",
         """MBartModel""",
         """MBartPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """TFMBartForConditionalGeneration""",
         """TFMBartModel""",
         """TFMBartPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:				Tuple          =	[
         """FlaxMBartForConditionalGeneration""",
         """FlaxMBartForQuestionAnswering""",
         """FlaxMBartForSequenceClassification""",
         """FlaxMBartModel""",
         """FlaxMBartPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowercase__		:				int          =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





