



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 				_snake_case			:




				def __init__(						self					:						Optional[Any],					__lowercase					:						Tuple,					__lowercase					:						Optional[int]=3,					__lowercase					:						str=32,					__lowercase					:						Any=3,					__lowercase					:						int=10,					__lowercase					:						Optional[Any]=[10, 20, 30, 40],					__lowercase					:						int=[1, 1, 2, 1],					__lowercase					:						Any=True,					__lowercase					:						Dict=True,					__lowercase					:						Union[str, Any]="relu",					__lowercase					:						Optional[int]=3,					__lowercase					:						str=None,					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				image_size
							lowercase__					  =				num_channels
							lowercase__					  =				embeddings_size
							lowercase__					  =				hidden_sizes
							lowercase__					  =				depths
							lowercase__					  =				is_training
							lowercase__					  =				use_labels
							lowercase__					  =				hidden_act
							lowercase__					  =				num_labels
							lowercase__					  =				scope
							lowercase__					  =				len(__lowercase						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowercase__					  =				None
							if self.use_labels:
										lowercase__					  =				ids_tensor([self.batch_size],					self.num_labels						)

							lowercase__					  =				self.get_config()
							return config, pixel_values, labels




				def 							A__      (						self					:						Any						):
							return RegNetConfig(
							    num_channels=self.num_channels,					embeddings_size=self.embeddings_size,					hidden_sizes=self.hidden_sizes,					depths=self.depths,					hidden_act=self.hidden_act,					num_labels=self.num_labels,					)




				def 							A__      (						self					:						int,					__lowercase					:						Tuple,					__lowercase					:						Union[str, Any],					__lowercase					:						str						):
							lowercase__					  =				TFRegNetModel(config=__lowercase						)
							lowercase__					  =				model(__lowercase,					training=__lowercase						)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),					)




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						List[Any],					__lowercase					:						Union[str, Any],					__lowercase					:						Union[str, Any]						):
							lowercase__					  =				self.num_labels
							lowercase__					  =				TFRegNetForImageClassification(__lowercase						)
							lowercase__					  =				model(__lowercase,					labels=__lowercase,					training=__lowercase						)
							self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)						)




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"pixel_values": pixel_values}
							return config, inputs_dict
@require_tf
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
				UpperCamelCase__				:					List[str]     		=(
				    {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
				    if is_tf_available()
				    else {}
				)

				UpperCamelCase__				:					int     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					int     		=False
				UpperCamelCase__				:					Optional[int]     		=False
				UpperCamelCase__				:					Optional[int]     		=False




				def 							A__      (						self					:						str						):
							lowercase__					  =				TFRegNetModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__lowercase,					has_text_modality=__lowercase						)




				def 							A__      (						self					:						Tuple						):
							return




				@unittest.skip(reason="RegNet does not use inputs_embeds"						)
				def 							A__      (						self					:						Tuple						):
							pass




				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices("GPU"						)						) == 0,					reason="TF does not support backprop for grouped convolutions on CPU.",					)
				@slow
				def 							A__      (						self					:						Tuple						):
							super().test_keras_fit()




				@unittest.skip(reason="RegNet does not support input and output embeddings"						)
				def 							A__      (						self					:						Tuple						):
							pass




				def 							A__      (						self					:						List[Any]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase__					  =				["pixel_values"]
										self.assertListEqual(arg_names[:1],					__lowercase						)




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)




				def 							A__      (						self					:						int						):
							def check_hidden_states_output(__lowercase					:						Dict,					__lowercase					:						Optional[int],					__lowercase					:						Optional[int]						):
										lowercase__					  =				model_class(__lowercase						)
										lowercase__					  =				model(**self._prepare_for_class(__lowercase,					__lowercase						),					training=__lowercase						)

										lowercase__					  =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowercase__					  =				self.model_tester.num_stages
										self.assertEqual(len(__lowercase						),					expected_num_stages + 1						)

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]						),					[self.model_tester.image_size // 2, self.model_tester.image_size // 2],					)

							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__					  =				["basic", "bottleneck"]
							for model_class in self.all_model_classes:
										for layer_type in layers_type:
													lowercase__					  =				layer_type
													lowercase__					  =				True
													check_hidden_states_output(__lowercase,					__lowercase,					__lowercase						)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowercase__					  =				True

													check_hidden_states_output(__lowercase,					__lowercase,					__lowercase						)




				def 							A__      (						self					:						int						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							def check_equivalence(__lowercase					:						Any,					__lowercase					:						int,					__lowercase					:						Dict,					__lowercase					:						str={}						):
										lowercase__					  =				model(__lowercase,					return_dict=__lowercase,					**__lowercase						)
										lowercase__					  =				model(__lowercase,					return_dict=__lowercase,					**__lowercase						).to_tuple()

										def recursive_check(__lowercase					:						List[Any],					__lowercase					:						Dict						):
													if isinstance(__lowercase,					(List, Tuple)						):
																for tuple_iterable_value, dict_iterable_value in zip(__lowercase,					__lowercase						):
																			recursive_check(__lowercase,					__lowercase						)
													elif tuple_object is None:
																return
													else:
																self.assertTrue(
																    all(tf.equal(__lowercase,					__lowercase						)						),					msg=(
																        "Tuple and dict output are not equal. Difference:"
																        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object						)						)}'''
																    ),					)

													recursive_check(__lowercase,					__lowercase						)

							for model_class in self.all_model_classes:
										lowercase__					  =				model_class(__lowercase						)

										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase						)
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase						)
										check_equivalence(__lowercase,					__lowercase,					__lowercase						)

										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										check_equivalence(__lowercase,					__lowercase,					__lowercase						)

										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase						)
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase						)
										check_equivalence(__lowercase,					__lowercase,					__lowercase,					{"output_hidden_states": True}						)

										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										check_equivalence(__lowercase,					__lowercase,					__lowercase,					{"output_hidden_states": True}						)




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)




				@slow
				def 							A__      (						self					:						Tuple						):
							for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__					  =				TFRegNetModel.from_pretrained(__lowercase						)
										self.assertIsNotNone(__lowercase						)



def __lowerCAmelCase (     ):
			lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image




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




				@cached_property
				def 							A__      (						self					:						List[Any]						):
							return (
							    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
							    if is_vision_available()
							    else None
							)




				@slow
				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

							lowercase__					  =				self.default_image_processor
							lowercase__					  =				prepare_img()
							lowercase__					  =				image_processor(images=__lowercase,					return_tensors="tf"						)

							# forward pass
							lowercase__					  =				model(**__lowercase,					training=__lowercase						)

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

							lowercase__					  =				tf.constant([-0.4180, -1.5051, -3.4836]						)

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









# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path



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

			# BLUE scores as follows:
			# "pair": [fairseq, transformers]
			lowercase__					  =				{
			    "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
			    "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
			    "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
			    "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
			}
			lowercase__					  =				f'''{src_lang}-{tgt_lang}'''

			lowercase__					  =				f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
			os.makedirs(SCREAMING_SNAKE_CASE_						,  exist_ok=SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				os.path.join(SCREAMING_SNAKE_CASE_						,  "README.md"  )
			print(f'''Generating {path}'''  )
			with open(SCREAMING_SNAKE_CASE_						,  "w"						,  encoding="utf-8"  ) as f:
						f.write(SCREAMING_SNAKE_CASE_  )


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

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowercase_				,    lowercase_				,    lowercase_								=     model_name.split("""-""")
							lowercase_								=     model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)




