
"""simple docstring"""
import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		CLIPTokenizer
							_snake_case						      =		CLIPTokenizerFast
							_snake_case						      =		True
							_snake_case						      =		{}
							_snake_case						      =		False




							def    A__						( self				)       ->  Optional[int]:
													super().setUp()

													# fmt: off
													__lowerCAmelCase     =  ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
													# fmt: on
													__lowerCAmelCase     =  dict(zip(snake_case_    ,				range(len(snake_case_				)				)				)				)
													__lowerCAmelCase     =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
													__lowerCAmelCase     =  {"""unk_token""": """<unk>"""}

													__lowerCAmelCase     =  os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""vocab_file"""]				)
													__lowerCAmelCase     =  os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""merges_file"""]				)
													with open(self.vocab_file    ,				"""w"""    ,				encoding="""utf-8"""				) as fp:
																			fp.write(json.dumps(snake_case_				) + """\n"""				)
													with open(self.merges_file    ,				"""w"""    ,				encoding="""utf-8"""				) as fp:
																			fp.write("""\n""".join(snake_case_				)				)




							def    A__						( self    ,				**snake_case_				)       ->  List[Any]:
													kwargs.update(self.special_tokens_map				)
													return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				**snake_case_				)




							def    A__						( self    ,				**snake_case_				)       ->  List[str]:
													kwargs.update(self.special_tokens_map				)
													return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_				)       ->  List[str]:
													__lowerCAmelCase     =  """lower newer"""
													__lowerCAmelCase     =  """lower newer"""
													return input_text, output_text




							def    A__						( self				)       ->  Any:
													__lowerCAmelCase     =  CLIPTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map				)
													__lowerCAmelCase     =  """lower newer"""
													__lowerCAmelCase     =  ["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
													__lowerCAmelCase     =  tokenizer.tokenize(snake_case_				)
													self.assertListEqual(snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  tokens + [tokenizer.unk_token]
													__lowerCAmelCase     =  [10, 2, 16, 9, 3, 2, 16, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_				)    ,				snake_case_				)




							@require_ftfy
							def    A__						( self				)       ->  Tuple:
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																									__lowerCAmelCase     =  self.tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)
																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)

																									__lowerCAmelCase     =  """A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
																									__lowerCAmelCase     =  tokenizer_s.tokenize(snake_case_				)
																									__lowerCAmelCase     =  tokenizer_r.tokenize(snake_case_				)

																									self.assertListEqual(snake_case_    ,				snake_case_				)

																									# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																									# with Tilde) encoded in 2 different ways
																									__lowerCAmelCase     =  """xa\u0303y""" + """ """ + """x\xe3y"""
																									__lowerCAmelCase     =  tokenizer_s.tokenize(snake_case_				)
																									__lowerCAmelCase     =  tokenizer_r.tokenize(snake_case_				)

																									self.assertListEqual(snake_case_    ,				snake_case_				)

																									# Test that the tokenization is identical on unicode of space type
																									__lowerCAmelCase     =  [
																									    """\u0009""",  # (horizontal tab, '\t')
																									    """\u000B""",  # (vertical tab)
																									    """\u000C""",  # (form feed)
																									    """\u0020""",  # (space, ' ')
																									    """\u200E""",  # (left-to-right mark):w
																									    """\u200F""",  # (right-to-left mark)
																									]
																									for unicode_seq in spaces_unicodes:
																															__lowerCAmelCase     =  tokenizer_s.tokenize(snake_case_				)
																															__lowerCAmelCase     =  tokenizer_r.tokenize(snake_case_				)

																															self.assertListEqual(snake_case_    ,				snake_case_				)

																									# Test that the tokenization is identical on unicode of line break type
																									__lowerCAmelCase     =  [
																									    """\u000A""",  # (line feed, '\n')
																									    """\r\n""",  # (carriage return and line feed, '\r\n')
																									    """\u000D""",  # (carriage return, '\r')
																									    """\r""",  # (carriage return, '\r')
																									    """\u000D""",  # (carriage return, '\r')
																									    """\u2028""",  # (line separator)
																									    """\u2029""",  # (paragraph separator)
																									    # "\u0085", # (next line)
																									]

																									# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																									# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																									# space (and thus into an empty list).

																									for unicode_seq in line_break_unicodes:
																															__lowerCAmelCase     =  tokenizer_s.tokenize(snake_case_				)
																															__lowerCAmelCase     =  tokenizer_r.tokenize(snake_case_				)

																															self.assertListEqual(snake_case_    ,				snake_case_				)




							def    A__						( self				)       ->  Tuple:
													# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																									__lowerCAmelCase     =  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																									__lowerCAmelCase     =  f"""{text_of_1_token} {text_of_1_token}"""

																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(
																									    snake_case_    ,				use_fast=snake_case_    ,				)
																									__lowerCAmelCase     =  tokenizer_r(snake_case_    ,				return_offsets_mapping=snake_case_    ,				add_special_tokens=snake_case_				)
																									self.assertEqual(encoding.offset_mapping[0]    ,				(0, len(snake_case_				))				)
																									self.assertEqual(
																									    encoding.offset_mapping[1]    ,				(len(snake_case_				) + 1, len(snake_case_				) + 1 + len(snake_case_				))    ,				)

																									__lowerCAmelCase     =  f""" {text}"""

																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(
																									    snake_case_    ,				use_fast=snake_case_    ,				)
																									__lowerCAmelCase     =  tokenizer_r(snake_case_    ,				return_offsets_mapping=snake_case_    ,				add_special_tokens=snake_case_				)
																									self.assertEqual(encoding.offset_mapping[0]    ,				(1, 1 + len(snake_case_				))				)
																									self.assertEqual(
																									    encoding.offset_mapping[1]    ,				(1 + len(snake_case_				) + 1, 1 + len(snake_case_				) + 1 + len(snake_case_				))    ,				)




							def    A__						( self				)       ->  int:
													# Test related to the breaking change introduced in transformers v4.17.0
													# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
													with self.assertRaises(snake_case_				) as context:
																			self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""				)

													self.assertTrue(
													    context.exception.args[0].startswith(
													        """The `backend_tokenizer` provided does not match the expected format."""				)				)




							@require_ftfy
							def    A__						( self				)       ->  List[Any]:
													super().test_tokenization_python_rust_equals()




							def    A__						( self				)       ->  Any:
													# CLIP always lower cases letters
													pass


"""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)
