






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )






class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      def __init__(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						Dict ,					__UpperCAmelCase      :						Tuple=13 ,					__UpperCAmelCase      :						Optional[int]=7 ,					__UpperCAmelCase      :						Tuple=True ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						Optional[Any]=True ,					__UpperCAmelCase      :						Dict=True ,					__UpperCAmelCase      :						str=99 ,					__UpperCAmelCase      :						Optional[int]=32 ,					__UpperCAmelCase      :						Any=5 ,					__UpperCAmelCase      :						List[str]=4 ,					__UpperCAmelCase      :						List[Any]=37 ,					__UpperCAmelCase      :						str="gelu" ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						List[Any]=0.1 ,					__UpperCAmelCase      :						Dict=512 ,					__UpperCAmelCase      :						List[Any]=16 ,					__UpperCAmelCase      :						Tuple=2 ,					__UpperCAmelCase      :						List[Any]=0.02 ,					__UpperCAmelCase      :						Union[str, Any]=4 ,					)    ->str:




       """simple docstring"""


       a		  =      parent
       a		  =      batch_size
       a		  =      seq_length
       a		  =      is_training
       a		  =      use_attention_mask
       a		  =      use_token_type_ids
       a		  =      use_labels
       a		  =      vocab_size
       a		  =      hidden_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads
       a		  =      intermediate_size
       a		  =      hidden_act
       a		  =      hidden_dropout_prob
       a		  =      attention_probs_dropout_prob
       a		  =      max_position_embeddings
       a		  =      type_vocab_size
       a		  =      type_sequence_label_size
       a		  =      initializer_range
       a		  =      num_choices







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Optional[int]:




       """simple docstring"""


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

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

       a		  =      DistilBertConfig(
           vocab_size=self.vocab_size ,					dim=self.hidden_size ,					n_layers=self.num_hidden_layers ,					n_heads=self.num_attention_heads ,					hidden_dim=self.intermediate_size ,					hidden_act=self.hidden_act ,					dropout=self.hidden_dropout_prob ,					attention_dropout=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					tie_weights_=__UpperCAmelCase ,					)

       return config, input_ids, attention_mask







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




       """simple docstring"""


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






@require_flax
class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Tuple:




       """simple docstring"""


       a		  =      FlaxDistilBertModelTester(self  )







      @slow
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Any:




       """simple docstring"""


       for model_class_name in self.all_model_classes:
        a		  =      model_class_name.from_pretrained('''distilbert-base-uncased'''  )
        a		  =      model(np.ones((1, 1)  )  )
        self.assertIsNotNone(__UpperCAmelCase  )






@require_flax
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      @slow
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[Any]:




       """simple docstring"""


       a		  =      FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''  )
       a		  =      np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]  )
       a		  =      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
       a		  =      model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase  )[0]
       a		  =      (1, 11, 768)
       self.assertEqual(output.shape ,					__UpperCAmelCase  )
       a		  =      np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]  )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,					__UpperCAmelCase ,					atol=1e-4  )  )









"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

								if output_type == "pil":
									a      :		int	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


