




from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor







class     A:



 '''simple docstring'''


 def __init__(							self   :    List[Any]		,				A_   :    Dict		,				A_   :    Union[str, Any]=13		,				A_   :    List[Any]=30		,				A_   :    Optional[Any]=2		,				A_   :    List[str]=3		,				A_   :    List[str]=True		,				A_   :    Dict=True		,				A_   :    List[Any]=32		,				A_   :    Any=2		,				A_   :    Any=4		,				A_   :    Optional[int]=37		,				A_   :    Dict="gelu"		,				A_   :    List[Any]=0.1		,				A_   :    Optional[int]=0.1		,				A_   :    Union[str, Any]=10		,				A_   :    Optional[Any]=0.02		,				A_   :    List[Any]=3		,				A_   :    str=None		,				)  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       parent
       lowerCamelCase_							=       batch_size
       lowerCamelCase_							=       image_size
       lowerCamelCase_							=       patch_size
       lowerCamelCase_							=       num_channels
       lowerCamelCase_							=       is_training
       lowerCamelCase_							=       use_labels
       lowerCamelCase_							=       hidden_size
       lowerCamelCase_							=       num_hidden_layers
       lowerCamelCase_							=       num_attention_heads
       lowerCamelCase_							=       intermediate_size
       lowerCamelCase_							=       hidden_act
       lowerCamelCase_							=       hidden_dropout_prob
       lowerCamelCase_							=       attention_probs_dropout_prob
       lowerCamelCase_							=       type_sequence_label_size
       lowerCamelCase_							=       initializer_range
       lowerCamelCase_							=       scope

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       lowerCamelCase_							=       (image_size // patch_size) ** 2
       lowerCamelCase_							=       num_patches + 1

 def 							a__					(							self   :    List[str]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       lowerCamelCase_							=       None
       if self.use_labels:
             lowerCamelCase_							=       ids_tensor([self.batch_size]		,				self.type_sequence_label_size    )

       lowerCamelCase_							=       self.get_config()

       return config, pixel_values, labels

 def 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






       return ViTConfig(
           image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				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		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				is_decoder=A_		,				initializer_range=self.initializer_range		,				)

 def 							a__					(							self   :    Any		,				A_   :    int		,				A_   :    int		,				A_   :    int    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModel(config=A_    )
       lowerCamelCase_							=       model(A_		,				training=A_    )
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)    )

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_							=       self.image_size // 2
       lowerCamelCase_							=       pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_							=       model(A_		,				interpolate_pos_encoding=A_		,				training=A_    )
       lowerCamelCase_							=       (image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, seq_length, self.hidden_size)    )

 def 							a__					(							self   :    List[Any]		,				A_   :    List[Any]		,				A_   :    Any		,				A_   :    Any    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.type_sequence_label_size
       lowerCamelCase_							=       TFViTForImageClassification(A_    )
       lowerCamelCase_							=       model(A_		,				labels=A_		,				training=A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )

       # Test with an image with different size than the one specified in config.
       lowerCamelCase_							=       self.image_size // 2
       lowerCamelCase_							=       pixel_values[:, :, :image_size, :image_size]
       lowerCamelCase_							=       model(A_		,				interpolate_pos_encoding=A_		,				training=A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )

       # test greyscale images
       lowerCamelCase_							=       1
       lowerCamelCase_							=       TFViTForImageClassification(A_    )
       lowerCamelCase_							=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       lowerCamelCase_							=       model(A_    )
       self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)    )





 def 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.prepare_config_and_inputs()
       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       config_and_inputs
       lowerCamelCase_							=       {'pixel_values': pixel_values}
       return config, inputs_dict







@require_tf
class     A(		UpperCamelCase				, UpperCamelCase				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 UpperCamelCase		  =		(
     {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 UpperCamelCase		  =		False
 UpperCamelCase		  =		False
 UpperCamelCase		  =		False

 def 							a__					(							self   :    int    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModelTester(self    )
       lowerCamelCase_							=       ConfigTester(self		,				config_class=A_		,				has_text_modality=A_		,				hidden_size=37    )

 def 							a__					(							self   :    int    )  ->      List[str]:

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='ViT does not use inputs_embeds'    )
 def 							a__					(							self   :    List[str]    )  ->      Tuple:

       """simple docstring"""






       pass

 @unittest.skip(reason='ViT does not use inputs_embeds'    )
 def 							a__					(							self   :    int    )  ->      Optional[Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    str    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=       model_class(A_    )
             self.assertIsInstance(model.get_input_embeddings()		,				(tf.keras.layers.Layer)    )
             lowerCamelCase_							=       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A_		,				tf.keras.layers.Layer    )    )

 def 							a__					(							self   :    List[Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=       model_class(A_    )
             lowerCamelCase_							=       inspect.signature(model.call    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowerCamelCase_							=       [*signature.parameters.keys()]

             lowerCamelCase_							=       ['pixel_values']
             self.assertListEqual(arg_names[:1]		,				A_    )

 def 							a__					(							self   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_    )

 def 							a__					(							self   :    Tuple    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_    )





 @slow
 def 							a__					(							self   :    Any    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       TFViTModel.from_pretrained('google/vit-base-patch16-224'    )
       self.assertIsNotNone(A_    )

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
      return image







@require_tf
@require_vision
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 @cached_property
 def 							a__					(							self   :    int    )  ->      Tuple:

       """simple docstring"""






       return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'    ) if is_vision_available() else None





 @slow
 def 							a__					(							self   :    List[str]    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'    )

       lowerCamelCase_							=       self.default_image_processor
       lowerCamelCase_							=       prepare_img()
       lowerCamelCase_							=       image_processor(images=A_		,				return_tensors='tf'    )

       # forward pass
       lowerCamelCase_							=       model(**A_    )

       # verify the logits
       lowerCamelCase_							=       tf.TensorShape((1, 1000)    )
       self.assertEqual(outputs.logits.shape		,				A_    )

       lowerCamelCase_							=       tf.constant([-0.2744, 0.8215, -0.0836]    )

       tf.debugging.assert_near(outputs.logits[0, :3]		,				A_		,				atol=1E-4    )







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

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "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],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		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=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				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"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


