
'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__lowerCAmelCase					:			Optional[int]			      =		{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase					:			Optional[Any]			      =		[
     "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TrOCRForCausalLM",
     "TrOCRPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
 from .processing_trocr import TrOCRProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
 import sys

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




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 						__lowerCAmelCase	:



      """simple docstring"""







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

          torch.manual_seed(0				)
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''				)

          torch.manual_seed(0				)
          __lowercase		:			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				)
          __lowercase		:			List[str]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			str	    =       IFWatermarker()

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




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

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

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       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=1.4_14				,       time_embedding_act_fn='''gelu'''				,       time_embedding_dim=32				,       )
          unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

          torch.manual_seed(0				)
          __lowercase		:			Dict	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       thresholding=_snake_case				,       dynamic_thresholding_ratio=0.95				,       sample_max_value=1.0				,       prediction_type='''epsilon'''				,       variance_type='''learned_range'''				,       )

          torch.manual_seed(0				)
          __lowercase		:			Optional[int]	    =       DDPMScheduler(
              num_train_timesteps=1000				,       beta_schedule='''squaredcos_cap_v2'''				,       beta_start=0.00_01				,       beta_end=0.02				,       )

          torch.manual_seed(0				)
          __lowercase		:			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 		snake_case_			(      self		:       Any				):
          __lowercase		:			Tuple	    =       self.get_dummy_components()
          __lowercase		:			Any	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			List[str]	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			List[Any]	    =       inputs['''prompt''']
          __lowercase		:			int	    =       inputs['''generator''']
          __lowercase		:			Dict	    =       inputs['''num_inference_steps''']
          __lowercase		:			Optional[int]	    =       inputs['''output_type''']

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

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

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

          __lowercase    ,      __lowercase		:			Optional[int]	    =       pipe.encode_prompt(_snake_case				)

          # inputs with prompt converted to embeddings
          __lowercase		:			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:
              __lowercase		:			Tuple	    =       image

          if mask_image is not None:
              __lowercase		:			List[str]	    =       mask_image

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

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

          __lowercase		:			List[str]	    =       pipe(**_snake_case				)[0]

          with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(_snake_case				)
              __lowercase		:			List[Any]	    =       self.pipeline_class.from_pretrained(_snake_case				)
              pipe_loaded.to(_snake_case				)
              pipe_loaded.set_progress_bar_config(disable=_snake_case				)

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

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

          __lowercase		:			int	    =       self.get_dummy_inputs(_snake_case				)

          __lowercase		:			Union[str, Any]	    =       inputs['''generator''']
          __lowercase		:			Any	    =       inputs['''num_inference_steps''']
          __lowercase		:			Tuple	    =       inputs['''output_type''']

          # inputs with prompt converted to embeddings
          __lowercase		:			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:
              __lowercase		:			Dict	    =       image

          if mask_image is not None:
              __lowercase		:			Tuple	    =       mask_image

          if original_image is not None:
              __lowercase		:			Any	    =       original_image

          __lowercase		:			List[str]	    =       pipe_loaded(**_snake_case				)[0]

          __lowercase		:			Optional[int]	    =       np.abs(to_np(_snake_case				) - to_np(_snake_case				)				).max()
          self.assertLess(_snake_case				,       1E-4				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Union[str, Any]	    =       self.get_dummy_components()
          __lowercase		:			Dict	    =       self.pipeline_class(**_snake_case				)
          pipe.to(_snake_case				)
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Dict	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			Any	    =       pipe(**_snake_case				)[0]

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

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

          __lowercase		:			Optional[Any]	    =       self.get_dummy_inputs(_snake_case				)
          __lowercase		:			str	    =       pipe_loaded(**_snake_case				)[0]

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


