



'''simple docstring'''

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__lowerCAmelCase						      =       logging.getLogger(__name__)

class      __magic_name__						(   _UpperCamelCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	Optional[Any]=None		):
									_a					:				Any				=							self.layer[current_layer](_UpperCAmelCase     ,_UpperCAmelCase     ,head_mask[current_layer]		)

									_a					:				Optional[int]				=							layer_outputs[0]

									return hidden_states

@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'      ,						_UpperCamelCase      ,						)
class      __magic_name__						(   _UpperCamelCase       ):







							def __init__(							self :	Dict     ,_UpperCAmelCase :	List[str]		):
									super().__init__(_UpperCAmelCase		)

									_a					:				Optional[Any]				=							BertEncoderWithPabee(_UpperCAmelCase		)

									self.init_weights()
									_a					:				List[Any]				=							0
									_a					:				int				=							0
									_a					:				List[str]				=							0

									_a					:				Dict				=							0







							def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							threshold







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]		):
									_a					:				Optional[Any]				=							patience







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				Union[str, Any]				=							0
									_a					:				Tuple				=							0







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Tuple				=							self.inference_layers_num / self.inference_instances_num
									_a					:				Optional[int]				=							(
									    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
									    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
									)
									print(_UpperCAmelCase		)







							@add_start_docstrings_to_model_forward(_UpperCAmelCase		)
							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	List[str]=None     ,_UpperCAmelCase :	Dict=None     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	List[Any]=False     ,):

									if input_ids is not None and inputs_embeds is not None:
											raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
									elif input_ids is not None:
											_a					:				int				=							input_ids.size()
									elif inputs_embeds is not None:
											_a					:				str				=							inputs_embeds.size()[:-1]
									else:
											raise ValueError('You have to specify either input_ids or inputs_embeds'		)

									_a					:				Union[str, Any]				=							input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
											_a					:				Dict				=							torch.ones(_UpperCAmelCase     ,device=_UpperCAmelCase		)
									if token_type_ids is None:
											_a					:				Optional[int]				=							torch.zeros(_UpperCAmelCase     ,dtype=torch.long     ,device=_UpperCAmelCase		)

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									_a					:				torch.Tensor				=							self.get_extended_attention_mask(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		)

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
											_a	, _a	, _a					:				str				=							encoder_hidden_states.size()
											_a					:				int				=							(encoder_batch_size, encoder_sequence_length)
											if encoder_attention_mask is None:
													_a					:				Optional[Any]				=							torch.ones(_UpperCAmelCase     ,device=_UpperCAmelCase		)
											_a					:				List[Any]				=							self.invert_attention_mask(_UpperCAmelCase		)
									else:
											_a					:				List[Any]				=							None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									_a					:				List[str]				=							self.get_head_mask(_UpperCAmelCase     ,self.config.num_hidden_layers		)

									_a					:				List[str]				=							self.embeddings(
									    input_ids=_UpperCAmelCase     ,position_ids=_UpperCAmelCase     ,token_type_ids=_UpperCAmelCase     ,inputs_embeds=_UpperCAmelCase		)
									_a					:				Tuple				=							embedding_output

									if self.training:
											_a					:				Optional[Any]				=							[]
											for i in range(self.config.num_hidden_layers		):
													_a					:				List[Any]				=							self.encoder.adaptive_forward(
													    _UpperCAmelCase     ,current_layer=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,head_mask=_UpperCAmelCase		)

													_a					:				List[Any]				=							self.pooler(_UpperCAmelCase		)
													_a					:				Optional[Any]				=							output_layers[i](output_dropout(_UpperCAmelCase		)		)
													res.append(_UpperCAmelCase		)
									elif self.patience == 0:  # Use all layers for inference
											_a					:				Union[str, Any]				=							self.encoder(
											    _UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,head_mask=_UpperCAmelCase     ,encoder_hidden_states=_UpperCAmelCase     ,encoder_attention_mask=_UpperCAmelCase     ,)
											_a					:				str				=							self.pooler(encoder_outputs[0]		)
											_a					:				Tuple				=							[output_layers[self.config.num_hidden_layers - 1](_UpperCAmelCase		)]
									else:
											_a					:				str				=							0
											_a					:				Optional[int]				=							None
											_a					:				Any				=							0
											for i in range(self.config.num_hidden_layers		):
													calculated_layer_num += 1
													_a					:				Optional[Any]				=							self.encoder.adaptive_forward(
													    _UpperCAmelCase     ,current_layer=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,head_mask=_UpperCAmelCase		)

													_a					:				Dict				=							self.pooler(_UpperCAmelCase		)
													_a					:				List[Any]				=							output_layers[i](_UpperCAmelCase		)
													if regression:
															_a					:				Union[str, Any]				=							logits.detach()
															if patient_result is not None:
																	_a					:				List[str]				=							patient_result.detach()
															if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
																	patient_counter += 1
															else:
																	_a					:				int				=							0
													else:
															_a					:				List[Any]				=							logits.detach().argmax(dim=1		)
															if patient_result is not None:
																	_a					:				Any				=							patient_result.detach().argmax(dim=1		)
															if (patient_result is not None) and torch.all(labels.eq(_UpperCAmelCase		)		):
																	patient_counter += 1
															else:
																	_a					:				Optional[Any]				=							0

													_a					:				str				=							logits
													if patient_counter == self.patience:
															break
											_a					:				Optional[Any]				=							[patient_result]
											self.inference_layers_num += calculated_layer_num
											self.inference_instances_num += 1

									return res

@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '      ,						_UpperCamelCase      ,						)
class      __magic_name__						(   _UpperCamelCase       ):







							def __init__(							self :	str     ,_UpperCAmelCase :	Union[str, Any]		):
									super().__init__(_UpperCAmelCase		)
									_a					:				List[str]				=							config.num_labels

									_a					:				Optional[Any]				=							BertModelWithPabee(_UpperCAmelCase		)
									_a					:				Tuple				=							nn.Dropout(config.hidden_dropout_prob		)
									_a					:				List[str]				=							nn.ModuleList(
									    [nn.Linear(config.hidden_size     ,self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

									self.init_weights()







							@add_start_docstrings_to_model_forward(_UpperCAmelCase		)
							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	Union[str, Any]=None     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	Union[str, Any]=None     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	int=None     ,):
									_a					:				int				=							self.bert(
									    input_ids=_UpperCAmelCase     ,attention_mask=_UpperCAmelCase     ,token_type_ids=_UpperCAmelCase     ,position_ids=_UpperCAmelCase     ,head_mask=_UpperCAmelCase     ,inputs_embeds=_UpperCAmelCase     ,output_dropout=self.dropout     ,output_layers=self.classifiers     ,regression=self.num_labels == 1     ,)

									_a					:				Any				=							(logits[-1],)

									if labels is not None:
											_a					:				Union[str, Any]				=							None
											_a					:				int				=							0
											for ix, logits_item in enumerate(_UpperCAmelCase		):
													if self.num_labels == 1:
															#  We are doing regression
															_a					:				Optional[int]				=							MSELoss()
															_a					:				Optional[int]				=							loss_fct(logits_item.view(-1		)     ,labels.view(-1		)		)
													else:
															_a					:				List[str]				=							CrossEntropyLoss()
															_a					:				str				=							loss_fct(logits_item.view(-1     ,self.num_labels		)     ,labels.view(-1		)		)
													if total_loss is None:
															_a					:				Tuple				=							loss
													else:
															total_loss += loss * (ix + 1)
													total_weights += ix + 1
											_a					:				Optional[int]				=							(total_loss / total_weights,) + outputs

									return outputs









'''simple docstring'''

# 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						      =       '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
__lowerCAmelCase						      =       [
    '''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						      =       dict(zip(vocab, range(len(vocab))))
__lowerCAmelCase						      =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase						      =       Path(tmpdirname)
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
							__lowerCAmelCase						      =       build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
							__lowerCAmelCase						      =       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						      =       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						      =       FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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						      =       FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase						      =       tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__lowerCAmelCase						      =       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




