
"""simple docstring"""
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


__magic_name__									=					logging.get_logger(__name__)


@add_end_docstrings(
    a_     ,							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  _lowerCAmelCase    (    a_    ):



							def    _a (   self			,    a_ )				->				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    _a (   self			,    a_ )				->				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    _a (   self			,    a_ )				->				Tuple:
								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    _a (   self			,    a_			,    a_=None			,    **a_ )				->				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    _a (   self			,    a_ )				->				str:
								_UpperCAmelCase			=    self.model(**__UpperCamelCase )
								_UpperCAmelCase			=    model_inputs["input_ids"]
								return model_outputs



							def    _a (   self			,    a_			,    a_=5			,    a_=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    _a (   self			,    a_			,    a_=None )				->				Union[str, Any]:
								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    _a (   self			,    a_=None			,    a_=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			,    a_			,    *a_			,    **a_ )				->				List[str]:
								_UpperCAmelCase			=    super().__call__(__UpperCamelCase			,    **__UpperCamelCase )
								if isinstance(__UpperCamelCase			,    __UpperCamelCase ) and len(__UpperCamelCase ) == 1:
									return outputs[0]
								return outputs












# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase      :       str    =						[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase      :       List[Any]    =						dict(zip(vocab, range(len(vocab))))
lowerCamelCase      :       List[str]    =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCamelCase      :       int    =						Path(tmpdirname)
 lowerCamelCase      :       Optional[int]    =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
 lowerCamelCase      :       List[str]    =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
 lowerCamelCase      :       int    =						build_dir / VOCAB_FILES_NAMES['''merges_file''']
 with open(src_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, '''w''') as fp:
  fp.write('''\n'''.join(merges))

 lowerCamelCase      :       Dict    =						FSMTTokenizer(
     langs=['''en''', '''ru'''],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowerCamelCase      :       int    =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCamelCase      :       int    =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase      :       Dict    =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






