



'''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,
)


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

lowercase_	 =						"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def lowerCAmelCase						(__A						,			__A						,			__A=8):



     """simple docstring"""



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






class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     A				,     A				,     A				,     )					->  List[str]:


      """simple docstring"""



      super().__init__()

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



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  Tuple:


      """simple docstring"""



      if latents is None:
           _a											=						randn_tensor(A				,     generator=A				,     device=A				,     dtype=A )
      else:
           if latents.shape != shape:
                raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
           _a											=						latents.to(A )

      _a											=						latents * scheduler.init_noise_sigma
      return latents



 def       a__						(self				,     A=0 )					->  Any:


      """simple docstring"""



      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError('''Please install accelerate via `pip install accelerate`''' )

      _a											=						torch.device(f'''cuda:{gpu_id}''' )

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



 def       a__						(self				,     A=0 )					->  Optional[int]:


      """simple docstring"""



      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.''' )

      _a											=						torch.device(f'''cuda:{gpu_id}''' )

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

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

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



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       a__						(self )					->  Any:


      """simple docstring"""



      if not hasattr(self.unet				,     '''_hf_hook''' ):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(A				,     '''_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(A )
 def __call__(self				,     A				,     A				,     A				,     A = 512				,     A = 512				,     A = 100				,     A = 4.0				,     A = 1				,     A = None				,     A = None				,     A = "pil"				,     A = True				,     )					->  List[str]:


      """simple docstring"""



      _a											=						self._execution_device

      _a											=						guidance_scale > 1.0

      if isinstance(A				,     A ):
           _a											=						torch.cat(A				,     dim=0 )
      if isinstance(A				,     A ):
           _a											=						torch.cat(A				,     dim=0 )
      if isinstance(A				,     A ):
           _a											=						torch.cat(A				,     dim=0 )

      _a											=						image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           _a											=						image_embeds.repeat_interleave(A				,     dim=0 )
           _a											=						negative_image_embeds.repeat_interleave(A				,     dim=0 )
           _a											=						hint.repeat_interleave(A				,     dim=0 )

      _a											=						torch.cat([negative_image_embeds, image_embeds]				,     dim=0 ).to(dtype=self.unet.dtype				,     device=A )
      _a											=						torch.cat([hint, hint]				,     dim=0 ).to(dtype=self.unet.dtype				,     device=A )
      self.scheduler.set_timesteps(A				,     device=A )
      _a											=						self.scheduler.timesteps

      _a											=						self.movq.config.latent_channels

      _a						,					_a											=						downscale_height_and_width(A				,     A				,     self.movq_scale_factor )

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

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

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

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

           # compute the previous noisy sample x_t -> x_t-1
           _a											=						self.scheduler.step(
               A				,     A				,     A				,     generator=A				,     )[0]
      # post-processing
      _a											=						self.movq.decode(A				,     force_not_quantize=A )['''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"]:
           _a											=						image * 0.5 + 0.5
           _a											=						image.clamp(0				,     1 )
           _a											=						image.cpu().permute(0				,     2				,     3				,     1 ).float().numpy()

      if output_type == "pil":
           _a											=						self.numpy_to_pil(A )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=A )








'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import numpy
  import tensorflow as tf

  from transformers.models.esm.modeling_tf_esm import (
      TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEsmForMaskedLM,
      TFEsmForSequenceClassification,
      TFEsmForTokenClassification,
      TFEsmModel,
  )






class __A    :



 '''simple docstring'''







 def __init__(self				,     A				,     )					->  Optional[int]:


      """simple docstring"""



      _a											=						parent
      _a											=						13
      _a											=						7
      _a											=						True
      _a											=						True
      _a											=						True
      _a											=						99
      _a											=						32
      _a											=						2
      _a											=						4
      _a											=						37
      _a											=						'''gelu'''
      _a											=						0.1
      _a											=						0.1
      _a											=						512
      _a											=						16
      _a											=						2
      _a											=						0.02
      _a											=						3
      _a											=						4
      _a											=						None



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size )

      _a											=						None
      if self.use_input_mask:
           _a											=						random_attention_mask([self.batch_size, self.seq_length] )

      _a											=						None
      _a											=						None
      _a											=						None
      if self.use_labels:
           _a											=						ids_tensor([self.batch_size]				,     self.type_sequence_label_size )
           _a											=						ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels )
           _a											=						ids_tensor([self.batch_size]				,     self.num_choices )

      _a											=						EsmConfig(
          vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     pad_token_id=1				,     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				,     initializer_range=self.initializer_range				,     )

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



 def       a__						(self )					->  str:


      """simple docstring"""



      (
          (
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					
      )											=						self.prepare_config_and_inputs()

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

      return (
          config,
          input_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  str:


      """simple docstring"""



      _a											=						TFEsmModel(config=A )
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _a											=						model(A )

      _a											=						[input_ids, input_mask]
      _a											=						model(A )

      _a											=						model(A )

      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A				,     A				,     A				,     )					->  Union[str, Any]:


      """simple docstring"""



      _a											=						True

      _a											=						TFEsmModel(config=A )
      _a											=						{
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
          '''encoder_hidden_states''': encoder_hidden_states,
          '''encoder_attention_mask''': encoder_attention_mask,
      }
      _a											=						model(A )

      _a											=						[input_ids, input_mask]
      _a											=						model(A				,     encoder_hidden_states=A )

      # Also check the case where encoder outputs are not passed
      _a											=						model(A				,     attention_mask=A )

      self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						TFEsmForMaskedLM(config=A )
      _a											=						model([input_ids, input_mask] )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )



 def       a__						(self				,     A				,     A				,     A				,     A				,     A				,     A )					->  Any:


      """simple docstring"""



      _a											=						self.num_labels
      _a											=						TFEsmForTokenClassification(config=A )
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _a											=						model(A )
      self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels) )



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						self.prepare_config_and_inputs()
      (
          (
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					(
          _a
      )						,					
      )											=						config_and_inputs
      _a											=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_tf
class __A    (						A     ,     A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: str    = (
     (
         TFEsmModel,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 __lowerCamelCase					: Union[str, Any]    = (
     {
         'feature-extraction': TFEsmModel,
         'fill-mask': TFEsmForMaskedLM,
         'text-classification': TFEsmForSequenceClassification,
         'token-classification': TFEsmForTokenClassification,
         'zero-shot': TFEsmForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowerCamelCase					: Dict    = False
 __lowerCamelCase					: Tuple    = False



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						TFEsmModelTester(self )
      _a											=						ConfigTester(self				,     config_class=A				,     hidden_size=37 )



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


      """simple docstring"""



      self.config_tester.run_common_tests()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*A )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A )



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


      """simple docstring"""



      _a											=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A )



 @slow
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a											=						TFEsmModel.from_pretrained(A )
           self.assertIsNotNone(A )



 @unittest.skip('''Protein models do not support embedding resizing.''' )
 def       a__						(self )					->  str:


      """simple docstring"""



      pass



 @unittest.skip('''Protein models do not support embedding resizing.''' )
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      pass



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a						,					_a											=						self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a											=						model_class(A )
           assert isinstance(model.get_input_embeddings()				,     tf.keras.layers.Layer )
           if model_class is TFEsmForMaskedLM:
                # Output embedding test differs from the main test because they're a matrix, not a layer
                _a											=						model.get_bias()
                assert isinstance(A				,     A )
                for k, v in name.items():
                     assert isinstance(A				,     tf.Variable )
           else:
                _a											=						model.get_output_embeddings()
                assert x is None
                _a											=						model.get_bias()
                assert name is None






@require_tf
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @slow
 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )

      _a											=						tf.constant([[0, 1, 2, 3, 4, 5]] )
      _a											=						model(A )[0]
      _a											=						[1, 6, 33]
      self.assertEqual(list(output.numpy().shape )				,     A )
      # compare the actual values for a slice.
      _a											=						tf.constant(
          [
              [
                  [8.921518, -10.589814, -6.4671307],
                  [-6.3967156, -13.911377, -1.1211915],
                  [-7.781247, -13.951557, -3.740592],
              ]
          ] )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-2 ) )



 @slow
 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D''' )

      _a											=						tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
      _a											=						model(A )[0]
      # compare the actual values for a slice.
      _a											=						tf.constant(
          [
              [
                  [0.14443092, 0.54125327, 0.3247739],
                  [0.30340484, 0.00526676, 0.31077722],
                  [0.32278043, -0.24987096, 0.3414628],
              ]
          ] )
      self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()				,     expected_slice.numpy()				,     atol=1E-4 ) )



