


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


a : Dict		=				logging.get_logger(__name__)
@add_end_docstrings(
    lowerCAmelCase__     ,     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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def A      (				self ,       snake_case_						)   ->			np.ndarray:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			np.ndarray:







								'''simple docstring'''

								__lowercase 					=   self.get_masked_index(snake_case_						)
								__lowercase 					=   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 A      (				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								if isinstance(snake_case_ ,       snake_case_						):
											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(snake_case_						)






					def A      (				self ,       snake_case_ ,       snake_case_=None ,       **snake_case_						)   ->			Dict[str, GenericTensor]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.model(**snake_case_						)
								__lowercase 					=   model_inputs['''input_ids''']
								return model_outputs






					def A      (				self ,       snake_case_ ,       snake_case_=5 ,       snake_case_=None						)   ->			Dict:







								'''simple docstring'''

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

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

											__lowercase 					=   outputs.numpy()

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

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

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

											__lowercase					, __lowercase 					=   probs.topk(snake_case_						)

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

														__lowercase 					=   p
														# Filter padding out:
														__lowercase 					=   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
														__lowercase 					=   self.tokenizer.decode(snake_case_ ,       skip_special_tokens=snake_case_						)
														__lowercase 					=   {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]						), '''sequence''': sequence}
														row.append(snake_case_						)
											result.append(snake_case_						)
								if single_mask:
											return result[0]
								return result






					def A      (				self ,       snake_case_ ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_=None ,       snake_case_=None						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {}

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

								if top_k is not None:
											__lowercase 					=   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 ,       snake_case_ ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

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









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


