






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


snake_case__      :	List[str]       	=      logging.get_logger(__name__)


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

    """						,       )
class 						_a       (  UpperCAmelCase__    ):






		"""simple docstring"""





		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> np.ndarray:
					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=_UpperCAmelCase )
					else:
								raise ValueError('Unsupported framework' )
					return masked_index




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> np.ndarray:
					UpperCamelCase_									=     self.get_masked_index(_UpperCAmelCase )
					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    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
					if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
								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(_UpperCAmelCase )




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		**_UpperCAmelCase )			-> Dict[str, GenericTensor]:
					if return_tensors is None:
								UpperCamelCase_									=     self.framework
					UpperCamelCase_									=     self.tokenizer(_UpperCAmelCase		,		return_tensors=_UpperCAmelCase )
					self.ensure_exactly_one_mask_token(_UpperCAmelCase )
					return model_inputs




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[str]:
					UpperCamelCase_									=     self.model(**_UpperCAmelCase )
					UpperCamelCase_									=     model_inputs['input_ids']
					return model_outputs




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=5		,		_UpperCAmelCase=None )			-> str:
					# Cap top_k if there are targets
					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(_UpperCAmelCase		,		axis=-1 )
								if target_ids is not None:
											UpperCamelCase_									=     tf.gather_nd(tf.squeeze(_UpperCAmelCase		,		0 )		,		target_ids.reshape(-1		,		1 ) )
											UpperCamelCase_									=     tf.expand_dims(_UpperCAmelCase		,		0 )

								UpperCamelCase_									=     tf.math.top_k(_UpperCAmelCase		,		k=_UpperCAmelCase )
								UpperCamelCase_		,      UpperCamelCase_									=     topk.values.numpy(), topk.indices.numpy()
					else:
								UpperCamelCase_									=     torch.nonzero(input_ids == self.tokenizer.mask_token_id		,		as_tuple=_UpperCAmelCase ).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(_UpperCAmelCase )

					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(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase )
											UpperCamelCase_									=     {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
											row.append(_UpperCAmelCase )
								result.append(_UpperCAmelCase )
					if single_mask:
								return result[0]
					return result




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> Dict:
					if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
								UpperCamelCase_									=     [targets]
					try:
								UpperCamelCase_									=     self.tokenizer.get_vocab()
					except Exception:
								UpperCamelCase_									=     {}
					UpperCamelCase_									=     []
					for target in targets:
								UpperCamelCase_									=     vocab.get(_UpperCAmelCase		,		_UpperCAmelCase )
								if id_ is None:
											UpperCamelCase_									=     self.tokenizer(
											    _UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_attention_mask=_UpperCAmelCase		,		return_token_type_ids=_UpperCAmelCase		,		max_length=1		,		truncation=_UpperCAmelCase		,		)['input_ids']
											if len(_UpperCAmelCase ) == 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(_UpperCAmelCase ) )
					if len(_UpperCAmelCase ) == 0:
								raise ValueError('At least one target must be provided when passed.' )
					UpperCamelCase_									=     np.array(_UpperCAmelCase )
					return target_ids




		def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None )			-> Optional[int]:
					UpperCamelCase_									=     {}

					if targets is not None:
								UpperCamelCase_									=     self.get_target_ids(_UpperCAmelCase		,		_UpperCAmelCase )
								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		,		_UpperCAmelCase		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> Optional[int]:
					UpperCamelCase_									=     super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase )
					if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ) and len(_UpperCAmelCase ) == 1:
								return outputs[0]
					return outputs













import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      :	Optional[int]       	=      logging.get_logger(__name__)

snake_case__      :	List[Any]       	=      {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git_vision_model"""




  def __init__(     self		,		_UpperCAmelCase=768		,		_UpperCAmelCase=3072		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3		,		_UpperCAmelCase=224		,		_UpperCAmelCase=16		,		_UpperCAmelCase="quick_gelu"		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=0.0		,		_UpperCAmelCase=0.0_2		,		**_UpperCAmelCase		,		)			-> Optional[Any]:
     super().__init__(**_UpperCAmelCase )

     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     attention_dropout
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     hidden_act




  @classmethod
  def    _UpperCAmelCase     (     cls		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> "PretrainedConfig":
     cls._set_token_in_kwargs(_UpperCAmelCase )

     UpperCamelCase_		,      UpperCamelCase_									=     cls.get_config_dict(_UpperCAmelCase		,		**_UpperCAmelCase )

     # get the vision config dict if we are loading from GITConfig
     if config_dict.get('model_type' ) == "git":
        UpperCamelCase_									=     config_dict['vision_config']

     if "model_type" in config_dict and hasattr(cls		,		'model_type' ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
            f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )

     return cls.from_dict(_UpperCAmelCase		,		**_UpperCAmelCase )




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """git"""




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=6		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=0		,		_UpperCAmelCase="absolute"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=101		,		_UpperCAmelCase=102		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Optional[int]:
     super().__init__(bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		**_UpperCAmelCase )

     if vision_config is None:
        UpperCamelCase_									=     {}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.' )

     UpperCamelCase_									=     GitVisionConfig(**_UpperCAmelCase )
     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     hidden_size
     UpperCamelCase_									=     num_hidden_layers
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     hidden_act
     UpperCamelCase_									=     intermediate_size
     UpperCamelCase_									=     hidden_dropout_prob
     UpperCamelCase_									=     attention_probs_dropout_prob
     UpperCamelCase_									=     max_position_embeddings
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     layer_norm_eps
     UpperCamelCase_									=     position_embedding_type
     UpperCamelCase_									=     use_cache
     UpperCamelCase_									=     tie_word_embeddings
     UpperCamelCase_									=     num_image_with_embedding

     UpperCamelCase_									=     bos_token_id
     UpperCamelCase_									=     eos_token_id




  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     copy.deepcopy(self.__dict__ )
     UpperCamelCase_									=     self.vision_config.to_dict()
     UpperCamelCase_									=     self.__class__.model_type
     return output





