
import torch

from transformers import AutoModel



class 		UpperCamelCase__  (       torch.nn.Module    ):
   def __init__(self :					int					,				snake_case_ :					Any="sayef/fsner-bert-base-uncased"   ):
    super(snake_case_					,				self   ).__init__()

    __a :  Any						=  AutoModel.from_pretrained(snake_case_					,				return_dict=snake_case_   )
    __a :  Dict						=  torch.nn.CosineSimilarity(3					,				1E-08   )
    __a :  List[Any]						=  torch.nn.Softmax(dim=1   )
   def       lowerCAmelCase			(self :					int					,				**snake_case_ :					int   ):
    return self.bert(**snake_case_   ).last_hidden_state
   def       lowerCAmelCase			(self :					Optional[Any]					,				snake_case_ :					Optional[Any]   ):
    return token_embeddings.sum(2					,				keepdim=snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					List[str]					,				snake_case_ :					str					,				snake_case_ :					Dict=1   ):
    return self.softmax(T * self.cos(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					List[str]					,				snake_case_ :					Optional[int]   ):
    __a :  Tuple						=  W_supports['''sizes'''].tolist()
    __a :  Any						=  W_supports['''start_token_id'''].item()
    __a :  Dict						=  W_supports['''end_token_id'''].item()

    del W_supports["sizes"]
    del W_supports["start_token_id"]
    del W_supports["end_token_id"]

    __a :  Any						=  self.BERT(**snake_case_   )
    __a :  List[Any]						=  self.BERT(**snake_case_   )

    __a :  List[Any]						=  None
    __a :  Tuple						=  None

    __a :  Optional[Any]						=  W_supports['''input_ids'''] == start_token_id
    __a :  int						=  W_supports['''input_ids'''] == end_token_id

    for i, size in enumerate(snake_case_   ):
     if i == 0:
      __a :  str						=  0
     else:
      __a :  List[str]						=  support_sizes[i - 1]

     __a :  int						=  S[s : s + size][start_token_masks[s : s + size]]
     __a :  List[str]						=  S[s : s + size][end_token_masks[s : s + size]]

     __a :  Tuple						=  torch.matmul(q[i]					,				s_start.T   ).sum(1   ).softmax(0   )
     __a :  Tuple						=  torch.matmul(q[i]					,				s_end.T   ).sum(1   ).softmax(0   )

     if p_starts is not None:
      __a :  Any						=  torch.vstack((p_starts, p_start)   )
      __a :  str						=  torch.vstack((p_ends, p_end)   )
     else:
      __a :  int						=  p_start
      __a :  Optional[int]						=  p_end

    return p_starts, p_ends


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard



@slow
@require_flax
class 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
   def       lowerCAmelCase			(self :					int   ):
    __a				,       __a :  Optional[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        '''stabilityai/stable-diffusion-2'''					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)

    __a :  Any						=  '''A painting of a squirrel eating a burger'''
    __a :  Dict						=  jax.device_count()
    __a :  Optional[int]						=  num_samples * [prompt]
    __a :  Optional[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Optional[Any]						=  replicate(snake_case_   )
    __a :  Optional[int]						=  shard(snake_case_   )

    __a :  int						=  jax.random.PRNGKey(0   )
    __a :  str						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  int						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  Optional[int]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  List[Any]						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  Dict						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  str						=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[Any]						=  '''stabilityai/stable-diffusion-2'''
    __a				,       __a :  Dict						=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_					,				subfolder='''scheduler'''   )
    __a				,       __a :  List[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        snake_case_					,				scheduler=snake_case_					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)
    __a :  Union[str, Any]						=  scheduler_params

    __a :  List[Any]						=  '''A painting of a squirrel eating a burger'''
    __a :  Any						=  jax.device_count()
    __a :  Any						=  num_samples * [prompt]
    __a :  List[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Tuple						=  replicate(snake_case_   )
    __a :  Dict						=  shard(snake_case_   )

    __a :  Dict						=  jax.random.PRNGKey(0   )
    __a :  Dict						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  str						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  List[Any]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  Any						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  List[Any]						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  List[Any]						=  jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2
