



import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor
class 				_snake_case			(					lowercase__):




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(__lowercase,					"hidden_sizes"						)						)
							self.parent.assertTrue(hasattr(__lowercase,					"num_attention_heads"						)						)
class 				_snake_case			:




				def __init__(						self					:						Any,					__lowercase					:						List[str],					__lowercase					:						Dict=13,					__lowercase					:						str=64,					__lowercase					:						str=3,					__lowercase					:						Any=3,					__lowercase					:						Tuple=2,					__lowercase					:						List[str]=1,					__lowercase					:						int=16,					__lowercase					:						Optional[int]=[128, 256, 384],					__lowercase					:						List[Any]=[4, 6, 8],					__lowercase					:						str=[2, 3, 4],					__lowercase					:						List[Any]=[16, 16, 16],					__lowercase					:						int=0,					__lowercase					:						str=[2, 2, 2],					__lowercase					:						Tuple=[2, 2, 2],					__lowercase					:						Dict=0.02,					__lowercase					:						Optional[Any]=True,					__lowercase					:						Any=True,					__lowercase					:						List[str]=2,					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				image_size
							lowercase__					  =				num_channels
							lowercase__					  =				kernel_size
							lowercase__					  =				stride
							lowercase__					  =				padding
							lowercase__					  =				hidden_sizes
							lowercase__					  =				num_attention_heads
							lowercase__					  =				depths
							lowercase__					  =				key_dim
							lowercase__					  =				drop_path_rate
							lowercase__					  =				patch_size
							lowercase__					  =				attention_ratio
							lowercase__					  =				mlp_ratio
							lowercase__					  =				initializer_range
							lowercase__					  =				[
							    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
							lowercase__					  =				is_training
							lowercase__					  =				use_labels
							lowercase__					  =				num_labels
							lowercase__					  =				initializer_range




				def 							A__      (						self					:						Dict						):
							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					:						Tuple						):
							return LevitConfig(
							    image_size=self.image_size,					num_channels=self.num_channels,					kernel_size=self.kernel_size,					stride=self.stride,					padding=self.padding,					patch_size=self.patch_size,					hidden_sizes=self.hidden_sizes,					num_attention_heads=self.num_attention_heads,					depths=self.depths,					key_dim=self.key_dim,					drop_path_rate=self.drop_path_rate,					mlp_ratio=self.mlp_ratio,					attention_ratio=self.attention_ratio,					initializer_range=self.initializer_range,					down_ops=self.down_ops,					)




				def 							A__      (						self					:						Dict,					__lowercase					:						Optional[int],					__lowercase					:						Any,					__lowercase					:						str						):
							lowercase__					  =				LevitModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase						)
							lowercase__					  =				(self.image_size, self.image_size)
							lowercase__  ,       lowercase__					  =				image_size[0], image_size[1]
							for _ in range(4						):
										lowercase__					  =				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
										lowercase__					  =				floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape,					(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1]),					)




				def 							A__      (						self					:						List[Any],					__lowercase					:						int,					__lowercase					:						List[str],					__lowercase					:						List[str]						):
							lowercase__					  =				self.num_labels
							lowercase__					  =				LevitForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase,					labels=__lowercase						)
							self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"pixel_values": pixel_values}
							return config, inputs_dict
@require_torch
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					str     		=(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase__				:					Dict     		=(
				    {
				        """feature-extraction""": LevitModel,
				        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase__				:					Optional[Any]     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					Any     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					List[str]     		=False




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				LevitModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__lowercase,					has_text_modality=__lowercase,					hidden_size=37						)




				def 							A__      (						self					:						Optional[int]						):
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




				def 							A__      (						self					:						int						):
							return




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




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




				@unittest.skip(reason="Levit does not output attentions"						)
				def 							A__      (						self					:						Tuple						):
							pass




				def 							A__      (						self					:						Optional[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.forward						)
										# 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					:						int						):
							def check_hidden_states_output(__lowercase					:						str,					__lowercase					:						Tuple,					__lowercase					:						List[Any]						):
										lowercase__					  =				model_class(__lowercase						)
										model.to(__lowercase						)
										model.eval()

										with torch.no_grad():
													lowercase__					  =				model(**self._prepare_for_class(__lowercase,					__lowercase						)						)

										lowercase__					  =				outputs.hidden_states

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

										lowercase__					  =				(self.model_tester.image_size, self.model_tester.image_size)
										lowercase__  ,       lowercase__					  =				image_size[0], image_size[1]
										for _ in range(4						):
													lowercase__					  =				floor(
													    (
													        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
													lowercase__					  =				floor(
													    (
													        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
										# verify the first hidden states (first block)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]						),					[
										        height * width,
										        self.model_tester.hidden_sizes[0],
										    ],					)

							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										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						)




				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
				def 							A__      (						self					:						Optional[int]						):
							pass




				def 							A__      (						self					:						int,					__lowercase					:						List[str],					__lowercase					:						List[str],					__lowercase					:						Dict=False						):
							lowercase__					  =				super()._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)

							if return_labels:
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													del inputs_dict["labels"]

							return inputs_dict




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




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)




				def 							A__      (						self					:						Dict						):
							if not self.model_tester.is_training:
										return

							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__					  =				True

							for model_class in self.all_model_classes:
										# LevitForImageClassificationWithTeacher supports inference-only
										if (
										    model_class in get_values(__lowercase						)
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
													continue
										lowercase__					  =				model_class(__lowercase						)
										model.to(__lowercase						)
										model.train()
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				model(**__lowercase						).loss
										loss.backward()




				def 							A__      (						self					:						Optional[int]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
										return

							lowercase__					  =				False
							lowercase__					  =				True

							for model_class in self.all_model_classes:
										if model_class in get_values(__lowercase						) or not model_class.supports_gradient_checkpointing:
													continue
										# LevitForImageClassificationWithTeacher supports inference-only
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													continue
										lowercase__					  =				model_class(__lowercase						)
										model.gradient_checkpointing_enable()
										model.to(__lowercase						)
										model.train()
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				model(**__lowercase						).loss
										loss.backward()




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

							lowercase__					  =				[
							    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
							    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
							    {"title": "regression", "num_labels": 1, "dtype": torch.float},
							]

							for model_class in self.all_model_classes:
										if (
										    model_class
										    not in [
										        *get_values(__lowercase						),
										    ]
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
													continue

										for problem_type in problem_types:
													with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''						):
																lowercase__					  =				problem_type["title"]
																lowercase__					  =				problem_type["num_labels"]

																lowercase__					  =				model_class(__lowercase						)
																model.to(__lowercase						)
																model.train()

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

																if problem_type["num_labels"] > 1:
																			lowercase__					  =				inputs["labels"].unsqueeze(1						).repeat(1,					problem_type["num_labels"]						)

																lowercase__					  =				inputs["labels"].to(problem_type["dtype"]						)

																# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																# they have the same size." which is a symptom something in wrong for the regression problem.
																# See https://github.com/huggingface/transformers/issues/11780
																with warnings.catch_warnings(record=__lowercase						) as warning_list:
																			lowercase__					  =				model(**__lowercase						).loss
																for w in warning_list:
																			if "Using a target size that is different to the input size" in str(w.message						):
																						raise ValueError(
																						    F'''Something is going wrong in the regression problem: intercepted {w.message}'''						)

																loss.backward()




				@slow
				def 							A__      (						self					:						Any						):
							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__					  =				LevitModel.from_pretrained(__lowercase						)
										self.assertIsNotNone(__lowercase						)



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




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




				@cached_property
				def 							A__      (						self					:						int						):
							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)




				@slow
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    __lowercase						)

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

							# forward pass
							with torch.no_grad():
										lowercase__					  =				model(**__lowercase						)

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

							lowercase__					  =				torch.tensor([1.0448, -0.3745, -1.8317]						).to(__lowercase						)

							self.assertTrue(torch.allclose(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)




