





from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
				import tensorflow as tf

				from ..tf_utils import stable_softmax


if is_torch_available():
				import torch


UpperCamelCase__   : Optional[Any]					  = logging.get_logger(__name__)







@add_end_docstrings(
    A_						,       r'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '						,       )
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  GenericTensor		):






						'''simple docstring'''







						if self.framework == "tf":
							UpperCAmelCase_								=     tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
						elif self.framework == "pt":
							UpperCAmelCase_								=     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,					as_tuple=__lowercase		)
						else:
							raise ValueError("""Unsupported framework"""		)
						return masked_index





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  GenericTensor		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_masked_index(__lowercase		)
						UpperCAmelCase_								=     np.prod(masked_index.shape		)
						if numel < 1:
							raise PipelineException(
							    """fill-mask""" ,					self.model.base_model_prefix ,					F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" ,					)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  GenericTensor		):






						'''simple docstring'''







						if isinstance(__lowercase ,					__lowercase		):
							for model_input in model_inputs:
								self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0]		)
						else:
							for input_ids in model_inputs["input_ids"]:
								self._ensure_exactly_one_mask_token(__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Optional[int] ,					__lowercase    :  List[Any]=None ,					**__lowercase    :  Any		):






						'''simple docstring'''







						if return_tensors is None:
							UpperCAmelCase_								=     self.framework
						UpperCAmelCase_								=     self.tokenizer(__lowercase ,					return_tensors=__lowercase		)
						self.ensure_exactly_one_mask_token(__lowercase		)
						return model_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.model(**__lowercase		)
						UpperCAmelCase_								=     model_inputs["""input_ids"""]
						return model_outputs





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  str ,					__lowercase    :  List[Any]=5 ,					__lowercase    :  int=None		):






						'''simple docstring'''







						if target_ids is not None and target_ids.shape[0] < top_k:
							UpperCAmelCase_								=     target_ids.shape[0]
						UpperCAmelCase_								=     model_outputs["""input_ids"""][0]
						UpperCAmelCase_								=     model_outputs["""logits"""]

						if self.framework == "tf":
							UpperCAmelCase_								=     tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()[:, 0]

							UpperCAmelCase_								=     outputs.numpy()

							UpperCAmelCase_								=     outputs[0, masked_index, :]
							UpperCAmelCase_								=     stable_softmax(__lowercase ,					axis=-1		)
							if target_ids is not None:
								UpperCAmelCase_								=     tf.gather_nd(tf.squeeze(__lowercase ,					0		) ,					target_ids.reshape(-1 ,					1		)		)
								UpperCAmelCase_								=     tf.expand_dims(__lowercase ,					0		)

							UpperCAmelCase_								=     tf.math.top_k(__lowercase ,					k=__lowercase		)
							UpperCAmelCase_						,	UpperCAmelCase_								=     topk.values.numpy(), topk.indices.numpy()
						else:
							UpperCAmelCase_								=     torch.nonzero(input_ids == self.tokenizer.mask_token_id ,					as_tuple=__lowercase		).squeeze(-1		)
							# Fill mask pipeline supports only one ${mask_token} per sample

							UpperCAmelCase_								=     outputs[0, masked_index, :]
							UpperCAmelCase_								=     logits.softmax(dim=-1		)
							if target_ids is not None:
								UpperCAmelCase_								=     probs[..., target_ids]

							UpperCAmelCase_						,	UpperCAmelCase_								=     probs.topk(__lowercase		)

						UpperCAmelCase_								=     []
						UpperCAmelCase_								=     values.shape[0] == 1
						for i, (_values, _predictions) in enumerate(zip(values.tolist() ,					predictions.tolist()		)		):
							UpperCAmelCase_								=     []
							for v, p in zip(_values ,					_predictions		):
								# Copy is important since we're going to modify this array in place
								UpperCAmelCase_								=     input_ids.numpy().copy()
								if target_ids is not None:
									UpperCAmelCase_								=     target_ids[p].tolist()

								UpperCAmelCase_								=     p
								# Filter padding out:
								UpperCAmelCase_								=     tokens[np.where(tokens != self.tokenizer.pad_token_id		)]
								# Originally we skip special tokens to give readable output.
								# For multi masks though, the other [MASK] would be removed otherwise
								# making the output look odd, so we add them back
								UpperCAmelCase_								=     self.tokenizer.decode(__lowercase ,					skip_special_tokens=__lowercase		)
								UpperCAmelCase_								=     {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]		), """sequence""": sequence}
								row.append(__lowercase		)
							result.append(__lowercase		)
						if single_mask:
							return result[0]
						return result





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Any=None		):






						'''simple docstring'''







						if isinstance(__lowercase ,					__lowercase		):
							UpperCAmelCase_								=     [targets]
						try:
							UpperCAmelCase_								=     self.tokenizer.get_vocab()
						except Exception:
							UpperCAmelCase_								=     {}
						UpperCAmelCase_								=     []
						for target in targets:
							UpperCAmelCase_								=     vocab.get(__lowercase ,					__lowercase		)
							if id_ is None:
								UpperCAmelCase_								=     self.tokenizer(
								    __lowercase ,					add_special_tokens=__lowercase ,					return_attention_mask=__lowercase ,					return_token_type_ids=__lowercase ,					max_length=1 ,					truncation=__lowercase ,					)["""input_ids"""]
								if len(__lowercase		) == 0:
									logger.warning(
									    F"""The specified target token `{target}` does not exist in the model vocabulary. """
									    """We cannot replace it with anything meaningful, ignoring it"""		)
									continue
								UpperCAmelCase_								=     input_ids[0]
								# XXX: If users encounter this pass
								# it becomes pretty slow, so let's make sure
								# The warning enables them to fix the input to
								# get faster performance.
								logger.warning(
								    F"""The specified target token `{target}` does not exist in the model vocabulary. """
								    F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_		)}`."""		)
							target_ids.append(id_		)
						UpperCAmelCase_								=     list(set(__lowercase		)		)
						if len(__lowercase		) == 0:
							raise ValueError("""At least one target must be provided when passed."""		)
						UpperCAmelCase_								=     np.array(__lowercase		)
						return target_ids





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Optional[int]=None ,					__lowercase    :  Optional[Any]=None		):






						'''simple docstring'''







						UpperCAmelCase_								=     {}

						if targets is not None:
							UpperCAmelCase_								=     self.get_target_ids(__lowercase ,					__lowercase		)
							UpperCAmelCase_								=     target_ids

						if top_k is not None:
							UpperCAmelCase_								=     top_k

						if self.tokenizer.mask_token_id is None:
							raise PipelineException(
							    """fill-mask""" ,					self.model.base_model_prefix ,					"""The tokenizer does not define a `mask_token`."""		)
						return {}, {}, postprocess_params





					def __call__(						self    :  Union[str, Any] ,					__lowercase    :  Any ,					*__lowercase    :  Union[str, Any] ,					**__lowercase    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     super().__call__(__lowercase ,					**__lowercase		)
						if isinstance(__lowercase ,					__lowercase		) and len(__lowercase		) == 1:
							return outputs[0]
						return outputs









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     tempfile.mkdtemp()

						UpperCAmelCase_								=     [
						    """[UNK]""",
						    """[CLS]""",
						    """[SEP]""",
						    """[PAD]""",
						    """[MASK]""",
						    """的""",
						    """价""",
						    """格""",
						    """是""",
						    """15""",
						    """便""",
						    """alex""",
						    """##andra""",
						    """，""",
						    """。""",
						    """-""",
						    """t""",
						    """shirt""",
						]
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]		)
						with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""		) as vocab_writer:
							vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

						UpperCAmelCase_								=     {
						    """do_resize""": True,
						    """size""": {"""height""": 2_24, """width""": 2_24},
						    """do_center_crop""": True,
						    """crop_size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
						    """image_std""": [0.26862954, 0.26130258, 0.27577711],
						    """do_convert_rgb""": True,
						}
						UpperCAmelCase_								=     os.path.join(self.tmpdirname ,					__lowercase		)
						with open(self.image_processor_file ,					"""w""" ,					encoding="""utf-8"""		) as fp:
							json.dump(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizer.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					**__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						return BertTokenizerFast.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					**__lowercase    :  int		):






						'''simple docstring'''







						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						shutil.rmtree(self.tmpdirname		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     [np.random.randint(2_55 ,					size=(3, 30, 4_00) ,					dtype=np.uinta		)]

						UpperCAmelCase_								=     [Image.fromarray(np.moveaxis(__lowercase ,					0 ,					-1		)		) for x in image_inputs]

						return image_inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_tokenizer()
						UpperCAmelCase_								=     self.get_rust_tokenizer()
						UpperCAmelCase_								=     self.get_image_processor()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_slow.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname ,					use_fast=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)
						processor_fast.save_pretrained(self.tmpdirname		)
						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor_slow.tokenizer.get_vocab() ,					tokenizer_slow.get_vocab()		)
						self.assertEqual(processor_fast.tokenizer.get_vocab() ,					tokenizer_fast.get_vocab()		)
						self.assertEqual(tokenizer_slow.get_vocab() ,					tokenizer_fast.get_vocab()		)
						self.assertIsInstance(processor_slow.tokenizer ,					__lowercase		)
						self.assertIsInstance(processor_fast.tokenizer ,					__lowercase		)

						self.assertEqual(processor_slow.image_processor.to_json_string() ,					image_processor.to_json_string()		)
						self.assertEqual(processor_fast.image_processor.to_json_string() ,					image_processor.to_json_string()		)
						self.assertIsInstance(processor_slow.image_processor ,					__lowercase		)
						self.assertIsInstance(processor_fast.image_processor ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=self.get_tokenizer() ,					image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						UpperCAmelCase_								=     self.get_tokenizer(cls_token="""(CLS)""" ,					sep_token="""(SEP)"""		)
						UpperCAmelCase_								=     self.get_image_processor(do_normalize=__lowercase		)

						UpperCAmelCase_								=     ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname ,					cls_token="""(CLS)""" ,					sep_token="""(SEP)""" ,					do_normalize=__lowercase		)

						self.assertEqual(processor.tokenizer.get_vocab() ,					tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer ,					__lowercase		)

						self.assertEqual(processor.image_processor.to_json_string() ,					image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     image_processor(__lowercase ,					return_tensors="""np"""		)
						UpperCAmelCase_								=     processor(images=__lowercase ,					return_tensors="""np"""		)

						for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum() ,					input_processor[key].sum() ,					delta=1e-2		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""

						UpperCAmelCase_								=     processor(text=__lowercase		)

						UpperCAmelCase_								=     tokenizer(__lowercase		)

						for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key] ,					encoded_processor[key]		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

						self.assertListEqual(list(inputs.keys()		) ,					["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]		)

						# test if it raises when no input is passed
						with pytest.raises(__lowercase		):
							processor()





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase_								=     processor.batch_decode(__lowercase		)
						UpperCAmelCase_								=     tokenizer.batch_decode(__lowercase		)

						self.assertListEqual(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.get_image_processor()
						UpperCAmelCase_								=     self.get_tokenizer()

						UpperCAmelCase_								=     ChineseCLIPProcessor(tokenizer=__lowercase ,					image_processor=__lowercase		)

						UpperCAmelCase_								=     """Alexandra，T-shirt的价格是15便士。"""
						UpperCAmelCase_								=     self.prepare_image_inputs()

						UpperCAmelCase_								=     processor(text=__lowercase ,					images=__lowercase		)

						self.assertListEqual(list(inputs.keys()		) ,					processor.model_input_names		)


