




"""simple docstring"""

import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           SqueezeBertForMaskedLM,
           SqueezeBertForMultipleChoice,
           SqueezeBertForQuestionAnswering,
           SqueezeBertForSequenceClassification,
           SqueezeBertForTokenClassification,
           SqueezeBertModel,
       )





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			str=13       ,				_UpperCAmelCase			:			Dict=7       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Dict=False       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=99       ,				_UpperCAmelCase			:			Union[str, Any]=32       ,				_UpperCAmelCase			:			Tuple=5       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			Optional[int]=64       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			List[str]=0.1       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Union[str, Any]=512       ,				_UpperCAmelCase			:			Optional[int]=16       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			Tuple=3       ,				_UpperCAmelCase			:			List[str]=4       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			str=2       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			List[str]=2       ,				_UpperCAmelCase			:			Dict=4       ,				_UpperCAmelCase			:			str=1       ,				):
       _A     =  parent
       _A     =  batch_size
       _A     =  seq_length
       _A     =  is_training
       _A     =  use_input_mask
       _A     =  use_token_type_ids
       _A     =  use_labels
       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  type_sequence_label_size
       _A     =  initializer_range
       _A     =  num_labels
       _A     =  num_choices
       _A     =  scope
       _A     =  q_groups
       _A     =  k_groups
       _A     =  v_groups
       _A     =  post_attention_groups
       _A     =  intermediate_groups
       _A     =  output_groups

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size						)

       _A     =  None
       if self.use_input_mask:
             _A     =  random_attention_mask([self.batch_size, self.seq_length]						)

       _A     =  None
       _A     =  None
       _A     =  None
       if self.use_labels:
             _A     =  ids_tensor([self.batch_size]       ,				self.type_sequence_label_size						)
             _A     =  ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels						)
             _A     =  ids_tensor([self.batch_size]       ,				self.num_choices						)

       _A     =  self.get_config()

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

 def      lowerCAmelCase_				(					self			:			List[str]						):
       return SqueezeBertConfig(
           embedding_size=self.hidden_size       ,				vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				attention_probs_dropout_prob=self.hidden_dropout_prob       ,				attention_dropout=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				q_groups=self.q_groups       ,				k_groups=self.k_groups       ,				v_groups=self.v_groups       ,				post_attention_groups=self.post_attention_groups       ,				intermediate_groups=self.intermediate_groups       ,				output_groups=self.output_groups       ,				)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  SqueezeBertModel(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  model(_UpperCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)						)

 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Optional[Any]						):
       _A     =  SqueezeBertForMaskedLM(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase       ,				attention_mask=_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.vocab_size)						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			str						):
       _A     =  SqueezeBertForQuestionAnswering(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(
           _UpperCAmelCase       ,				attention_mask=_UpperCAmelCase       ,				start_positions=_UpperCAmelCase       ,				end_positions=_UpperCAmelCase						)
       self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length)						)

 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			List[str]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  self.num_labels
       _A     =  SqueezeBertForSequenceClassification(_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase       ,				attention_mask=_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)						)

 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  self.num_labels
       _A     =  SqueezeBertForTokenClassification(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()

       _A     =  model(_UpperCAmelCase       ,				attention_mask=_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  self.num_choices
       _A     =  SqueezeBertForMultipleChoice(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  input_ids.unsqueeze(1						).expand(-1       ,				self.num_choices       ,				-1						).contiguous()
       _A     =  input_mask.unsqueeze(1						).expand(-1       ,				self.num_choices       ,				-1						).contiguous()
       _A     =  model(
           _UpperCAmelCase       ,				attention_mask=_UpperCAmelCase       ,				labels=_UpperCAmelCase       ,				)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_choices)						)



 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  self.prepare_config_and_inputs()
       ((_A)  ,       (_A)  ,       (_A)  ,       (_A)  ,       (_A)  ,       (_A))     =  config_and_inputs
       _A     =  {'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict





@require_torch
class 							lowercase_			(	__lowerCAmelCase     ,      __lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      (
     (
         SqueezeBertModel,
         SqueezeBertForMaskedLM,
         SqueezeBertForMultipleChoice,
         SqueezeBertForQuestionAnswering,
         SqueezeBertForSequenceClassification,
         SqueezeBertForTokenClassification,
     )
     if is_torch_available()
     else None
 )
 UpperCAmelCase       :	List[str]     =      (
     {
         '''feature-extraction''': SqueezeBertModel,
         '''fill-mask''': SqueezeBertForMaskedLM,
         '''question-answering''': SqueezeBertForQuestionAnswering,
         '''text-classification''': SqueezeBertForSequenceClassification,
         '''token-classification''': SqueezeBertForTokenClassification,
         '''zero-shot''': SqueezeBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase       :	int     =      False
 UpperCAmelCase       :	Union[str, Any]     =      True
 UpperCAmelCase       :	Tuple     =      False

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  SqueezeBertModelTester(self						)
       _A     =  ConfigTester(self       ,				config_class=_UpperCAmelCase       ,				dim=37						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       self.config_tester.run_common_tests()

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_model(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_masked_lm(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_question_answering(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_token_classification(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_UpperCAmelCase						)



 @slow
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =  SqueezeBertModel.from_pretrained(_UpperCAmelCase						)
             self.assertIsNotNone(_UpperCAmelCase						)





@require_sentencepiece
@require_tokenizers
@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @slow
 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'						)

       _A     =  torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]						)
       _A     =  model(_UpperCAmelCase						)[0]
       _A     =  torch.Size((1, 3)						)
       self.assertEqual(output.shape       ,				_UpperCAmelCase						)
       _A     =  torch.tensor([[0.6401, -0.0349, -0.6041]]						)
       self.assertTrue(torch.allclose(_UpperCAmelCase       ,				_UpperCAmelCase       ,				atol=1E-4						)						)



"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  {
						    """en""": """Machine learning is great, isn't it?""",
						    """ru""": """Машинное обучение - это здорово, не так ли?""",
						    """de""": """Maschinelles Lernen ist großartig, nicht wahr?""",
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						__lowerCAmelCase     =  {
						    """wmt16-en-de-dist-12-1""": [28.3, 27.52],
						    """wmt16-en-de-dist-6-1""": [27.4, 27.11],
						    """wmt16-en-de-12-1""": [26.9, 25.75],
						}
						__lowerCAmelCase     =  f"""{src_lang}-{tgt_lang}"""

						__lowerCAmelCase     =  f"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"allenai/{model_name}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

"""
						model_card_dir.mkdir(parents=_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
						__lowerCAmelCase     =  os.path.join(_lowerCAmelCase ,    """README.md"""						)
						print(f"""Generating {path}"""						)
						with open(_lowerCAmelCase ,    """w""" ,    encoding="""utf-8"""						) as f:
												f.write(_lowerCAmelCase						)


# make sure we are under the root of the project
SCREAMING_SNAKE_CASE_					     =	Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_					     =	repo_dir / '''model_cards'''

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			SCREAMING_SNAKE_CASE_					     =	model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)
