


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 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  Optional[int]    )			-> Tuple:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					np.full((len(UpperCamelCase__    ), sequence_length, 2)				,       UpperCamelCase__    )
	else:
		A             =					np.full((len(UpperCamelCase__    ), sequence_length)				,       UpperCamelCase__    )

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

	return out_tensor.tolist()





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	A             =					ord(UpperCamelCase__    )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
		return True
	A             =					unicodedata.category(UpperCamelCase__    )
	if cat.startswith("""P"""    ):
		return True
	return False






@dataclass
class    _UpperCamelCase     (       lowerCAmelCase__					):

				"""simple docstring"""







				lowerCAmelCase					=			4_2
				lowerCAmelCase					=			True
				lowerCAmelCase					=			None
				lowerCAmelCase					=			None
				lowerCAmelCase					=			-1_0_0
				lowerCAmelCase					=			'pt'

				def   _UpperCAmelCase       (    self					, a__ )						->					List[Any]:
					import torch

					A             =					"""label""" if """label""" in features[0].keys() else """labels"""
					A             =					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
					A             =					self.tokenizer.pad(
					    _lowerCamelCase					, 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

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

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

					return batch






"""simple docstring"""
# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)

