





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      lowercase__    :







  def 	UpperCAmelCase__		(	self						):
       torch.manual_seed(0						)
       lowerCAmelCase_					:       int    =		TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

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

       torch.manual_seed(0						)
       lowerCAmelCase_					:       Tuple    =		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						)
       lowerCAmelCase_					:       Any    =		DDPMScheduler(
           num_train_timesteps=1_000    ,						beta_schedule="""squaredcos_cap_v2"""    ,						beta_start=0.0001    ,						beta_end=0.02    ,						thresholding=UpperCamelCase__    ,						dynamic_thresholding_ratio=0.95    ,						sample_max_value=1.0    ,						prediction_type="""epsilon"""    ,						variance_type="""learned_range"""    ,						)

       torch.manual_seed(0						)
       lowerCAmelCase_					:       Optional[int]    =		IFWatermarker()

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







  def 	UpperCAmelCase__		(	self						):
       torch.manual_seed(0						)
       lowerCAmelCase_					:       Dict    =		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=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						)
       lowerCAmelCase_					:       int    =		DDPMScheduler(
           num_train_timesteps=1_000    ,						beta_schedule="""squaredcos_cap_v2"""    ,						beta_start=0.0001    ,						beta_end=0.02    ,						thresholding=UpperCamelCase__    ,						dynamic_thresholding_ratio=0.95    ,						sample_max_value=1.0    ,						prediction_type="""epsilon"""    ,						variance_type="""learned_range"""    ,						)

       torch.manual_seed(0						)
       lowerCAmelCase_					:       List[Any]    =		DDPMScheduler(
           num_train_timesteps=1_000    ,						beta_schedule="""squaredcos_cap_v2"""    ,						beta_start=0.0001    ,						beta_end=0.02    ,						)

       torch.manual_seed(0						)
       lowerCAmelCase_					:       str    =		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						):
       lowerCAmelCase_					:       int    =		self.get_dummy_components()
       lowerCAmelCase_					:       Tuple    =		self.pipeline_class(**UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

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

       lowerCAmelCase_					:       Optional[int]    =		inputs['''prompt''']
       lowerCAmelCase_					:       Optional[int]    =		inputs['''generator''']
       lowerCAmelCase_					:       int    =		inputs['''num_inference_steps''']
       lowerCAmelCase_					:       Optional[Any]    =		inputs['''output_type''']

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

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

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

       lowerCAmelCase_					:       int    =		pipe.encode_prompt(UpperCamelCase__						)

       # inputs with prompt converted to embeddings
       lowerCAmelCase_					:       List[str]    =		{
           '''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_					:       str    =		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(UpperCamelCase__    ,						UpperCamelCase__    ,						UpperCamelCase__						)

       lowerCAmelCase_					:       int    =		pipe(**UpperCamelCase__						)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(UpperCamelCase__						)
            lowerCAmelCase_					:       Union[str, Any]    =		self.pipeline_class.from_pretrained(UpperCamelCase__						)
            pipe_loaded.to(UpperCamelCase__						)
            pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__						)

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

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

       lowerCAmelCase_					:       int    =		self.get_dummy_inputs(UpperCamelCase__						)

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

       # inputs with prompt converted to embeddings
       lowerCAmelCase_					:       List[str]    =		{
           '''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_					:       List[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

       lowerCAmelCase_					:       Any    =		pipe_loaded(**UpperCamelCase__						)[0]

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







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Optional[Any]    =		self.get_dummy_components()
       lowerCAmelCase_					:       List[Any]    =		self.pipeline_class(**UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       lowerCAmelCase_					:       int    =		self.get_dummy_inputs(UpperCamelCase__						)
       lowerCAmelCase_					:       str    =		pipe(**UpperCamelCase__						)[0]

       with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(UpperCamelCase__						)
            lowerCAmelCase_					:       Optional[int]    =		self.pipeline_class.from_pretrained(UpperCamelCase__						)
            pipe_loaded.to(UpperCamelCase__						)
            pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__						)

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

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

       lowerCAmelCase_					:       Optional[int]    =		np.abs(to_np(UpperCamelCase__						) - to_np(UpperCamelCase__						)						).max()
       self.assertLess(UpperCamelCase__    ,						1e-4						)







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Tuple 							=						{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Tuple 							=						["""VivitImageProcessor"""]


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Any 							=						[
          """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """VivitModel""",
          """VivitPreTrainedModel""",
          """VivitForVideoClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_vivit import VivitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_vivit import (
                VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VivitForVideoClassification,
                VivitModel,
                VivitPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Dict 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
