
"""simple docstring"""

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE_    					=						'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Union[str, Any]=8 ) ->      Dict:






    """simple docstring"""




    UpperCAmelCase									=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCAmelCase									=					width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor







class      _UpperCAmelCase							(		UpperCamelCase__							):




      def __init__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Union[str, Any]:
          super().__init__()

          self.register_modules(
              unet=lowercase_   ,       scheduler=lowercase_   ,       movq=lowercase_   ,       )
          UpperCAmelCase									=					2 ** (len(self.movq.config.block_out_channels							) - 1)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Tuple:
          if latents is None:
              UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=lowercase_   ,       dtype=lowercase_							)
          else:
              if latents.shape != shape:
                  raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"							)
              UpperCAmelCase									=					latents.to(lowercase_							)

          UpperCAmelCase									=					latents * scheduler.init_noise_sigma
          return latents




      def     a_      (							self   ,       lowercase_=0							)						->					Any:
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('Please install accelerate via `pip install accelerate`'							)

          UpperCAmelCase									=					torch.device(F"cuda:{gpu_id}"							)

          UpperCAmelCase									=					[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(lowercase_   ,       lowercase_							)




      def     a_      (							self   ,       lowercase_=0							)						->					Union[str, Any]:
          if is_accelerate_available() and is_accelerate_version('>='   ,       '0.17.0.dev0'							):
              from accelerate import cpu_offload_with_hook
          else:
              raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)

          UpperCAmelCase									=					torch.device(F"cuda:{gpu_id}"							)

          if self.device.type != "cpu":
              self.to('cpu'   ,       silence_dtype_warnings=lowercase_							)
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCAmelCase									=					None
          for cpu_offloaded_model in [self.unet, self.movq]:
              UpperCAmelCase			,							UpperCAmelCase									=					cpu_offload_with_hook(lowercase_   ,       lowercase_   ,       prev_module_hook=lowercase_							)

          # We'll offload the last model manually.
          UpperCAmelCase									=					hook




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def     a_      (							self							)						->					Union[str, Any]:
          if not hasattr(self.unet   ,       '_hf_hook'							):
              return self.device
          for module in self.unet.modules():
              if (
                  hasattr(lowercase_   ,       '_hf_hook'							)
                  and hasattr(module._hf_hook   ,       'execution_device'							)
                  and module._hf_hook.execution_device is not None
              ):
                  return torch.device(module._hf_hook.execution_device							)
          return self.device




      @torch.no_grad()
      @replace_example_docstring(lowercase_							)
      def __call__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = 5_1_2   ,       lowercase_ = 5_1_2   ,       lowercase_ = 1_0_0   ,       lowercase_ = 4.0   ,       lowercase_ = 1   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					int:
          UpperCAmelCase									=					self._execution_device

          UpperCAmelCase									=					guidance_scale > 1.0

          if isinstance(lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					torch.cat(lowercase_   ,       dim=0							)
          UpperCAmelCase									=					image_embeds.shape[0] * num_images_per_prompt
          if isinstance(lowercase_   ,       lowercase_							):
              UpperCAmelCase									=					torch.cat(lowercase_   ,       dim=0							)

          if do_classifier_free_guidance:
              UpperCAmelCase									=					image_embeds.repeat_interleave(lowercase_   ,       dim=0							)
              UpperCAmelCase									=					negative_image_embeds.repeat_interleave(lowercase_   ,       dim=0							)

          UpperCAmelCase									=					torch.cat([negative_image_embeds, image_embeds]   ,       dim=0							).to(dtype=self.unet.dtype   ,       device=lowercase_							)

          self.scheduler.set_timesteps(lowercase_   ,       device=lowercase_							)
          UpperCAmelCase									=					self.scheduler.timesteps

          UpperCAmelCase									=					self.unet.config.in_channels

          UpperCAmelCase			,							UpperCAmelCase									=					downscale_height_and_width(lowercase_   ,       lowercase_   ,       self.movq_scale_factor							)

          # create initial latent
          UpperCAmelCase									=					self.prepare_latents(
              (batch_size, num_channels_latents, height, width)   ,       image_embeds.dtype   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       self.scheduler   ,       )

          for i, t in enumerate(self.progress_bar(lowercase_							)							):
              # expand the latents if we are doing classifier free guidance
              UpperCAmelCase									=					torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

              UpperCAmelCase									=					{'image_embeds': image_embeds}
              UpperCAmelCase									=					self.unet(
                  sample=lowercase_   ,       timestep=lowercase_   ,       encoder_hidden_states=lowercase_   ,       added_cond_kwargs=lowercase_   ,       return_dict=lowercase_   ,       )[0]

              if do_classifier_free_guidance:
                  UpperCAmelCase			,							UpperCAmelCase									=					noise_pred.split(latents.shape[1]   ,       dim=1							)
                  UpperCAmelCase			,							UpperCAmelCase									=					noise_pred.chunk(2							)
                  UpperCAmelCase			,							UpperCAmelCase									=					variance_pred.chunk(2							)
                  UpperCAmelCase									=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  UpperCAmelCase									=					torch.cat([noise_pred, variance_pred_text]   ,       dim=1							)

              if not (
                  hasattr(self.scheduler.config   ,       'variance_type'							)
                  and self.scheduler.config.variance_type in ["learned", "learned_range"]
              ):
                  UpperCAmelCase			,							UpperCAmelCase									=					noise_pred.split(latents.shape[1]   ,       dim=1							)

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_   ,       )[0]
          # post-processing
          UpperCAmelCase									=					self.movq.decode(lowercase_   ,       force_not_quantize=lowercase_							)['sample']

          if output_type not in ["pt", "np", "pil"]:
              raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"							)

          if output_type in ["np", "pil"]:
              UpperCAmelCase									=					image * 0.5 + 0.5
              UpperCAmelCase									=					image.clamp(0   ,       1							)
              UpperCAmelCase									=					image.cpu().permute(0   ,       2   ,       3   ,       1							).float().numpy()

          if output_type == "pil":
              UpperCAmelCase									=					self.numpy_to_pil(lowercase_							)

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_							)





"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=4   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_token_type_ids
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					type_vocab_size
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					num_choices




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

          UpperCAmelCase									=					None
          if self.use_attention_mask:
              UpperCAmelCase									=					random_attention_mask([self.batch_size, self.seq_length]							)

          UpperCAmelCase									=					None
          if self.use_token_type_ids:
              UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size							)

          UpperCAmelCase									=					RobertaPreLayerNormConfig(
              vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       is_decoder=lowercase_   ,       initializer_range=self.initializer_range   ,       )

          return config, input_ids, token_type_ids, attention_mask




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs

          UpperCAmelCase									=					True
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       vocab_size=2							)

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	True

      __SCREAMING_SNAKE_CASE   :							int							      =	(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




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




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)
              UpperCAmelCase									=					model(np.ones((1, 1)							)							)
              self.assertIsNotNone(lowercase_							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          UpperCAmelCase									=					[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape							)   ,       lowercase_							)
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)




      @slow
      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)



