




'''simple docstring'''

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										snake_case_									=				np.full((len(__UpperCAmelCase	), sequence_length, 2),      __UpperCAmelCase	)
					else:
										snake_case_									=				np.full((len(__UpperCAmelCase	), sequence_length),      __UpperCAmelCase	)

					for i, tensor in enumerate(__UpperCAmelCase	):
										if padding_side == "right":
															if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
																				snake_case_									=				tensor[:sequence_length]
															else:
																				snake_case_									=				tensor[:sequence_length]
										else:
															if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
																				snake_case_									=				tensor[:sequence_length]
															else:
																				snake_case_									=				tensor[:sequence_length]

					return out_tensor.tolist()


def 		__magic_name__    (	__UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				ord(__UpperCAmelCase	)
					if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
										return True
					snake_case_									=				unicodedata.category(__UpperCAmelCase	)
					if cat.startswith('''P'''	):
										return True
					return False






@dataclass
class 						a				(   _lowerCamelCase     ):
				snake_case_      =		42
				snake_case_      =		True
				snake_case_      =		None
				snake_case_      =		None
				snake_case_      =		-100
				snake_case_      =		"pt"


				def   A_ (		self    :					int       ,				lowercase_    :					List[str]					):
									import torch

									snake_case_									=				'''label''' if '''label''' in features[0].keys() else '''labels'''
									snake_case_									=				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
									snake_case_									=				self.tokenizer.pad(
									    lowercase_       ,				padding=self.padding       ,				max_length=self.max_length       ,				pad_to_multiple_of=self.pad_to_multiple_of       ,				return_tensors='''pt''' if labels is None else None       ,				)

									if labels is None:
														return batch

									snake_case_									=				torch.tensor(batch['''entity_ids''']					).shape[1]
									snake_case_									=				self.tokenizer.padding_side
									if padding_side == "right":
														snake_case_									=				[
														    list(lowercase_					) + [self.label_pad_token_id] * (sequence_length - len(lowercase_					)) for label in labels
														]
									else:
														snake_case_									=				[
														    [self.label_pad_token_id] * (sequence_length - len(lowercase_					)) + list(lowercase_					) for label in labels
														]

									snake_case_									=				[feature['''ner_tags'''] for feature in features]
									snake_case_									=				padding_tensor(lowercase_       ,				-1       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				[feature['''original_entity_spans'''] for feature in features]
									snake_case_									=				padding_tensor(lowercase_       ,				(-1, -1)       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				{k: torch.tensor(lowercase_       ,				dtype=torch.intaa					) for k, v in batch.items()}

									return batch




'''simple docstring'''



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
  import jax







@require_flax
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      FlaxAutoencoderKL
 @property
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      4
      __SCREAMING_SNAKE_CASE     =      3
      __SCREAMING_SNAKE_CASE     =      (32, 32)

      __SCREAMING_SNAKE_CASE     =      jax.random.PRNGKey(0			)
      __SCREAMING_SNAKE_CASE     =      jax.random.uniform(__SCREAMING_SNAKE_CASE					,							((batch_size, num_channels) + sizes)			)

      return {"sample": image, "prng_key": prng_key}


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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 4,
      }
      __SCREAMING_SNAKE_CASE     =      self.dummy_input
      return init_dict, inputs_dict






