
from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


__magic_name__            =  [
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

__magic_name__            =  [
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		bootstrap_aggregation=lowerCAmelCase__    ,		rouge_keys=["rouge2", "rougeL"]							)
    assert isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							)
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		bootstrap_aggregation=lowerCAmelCase__    ,		rouge_keys=["rouge2"]							)
    assert (
        pd.DataFrame(no_aggregation["rouge2"]							).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["rouge2"]							).fmeasure.mean()
    )





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				"rougeLsum"
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__    ,		rouge_keys=[k]							)[k]
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__    ,		rouge_keys=[k]							)[k]
    assert score > score_no_sep





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				["rouge1", "rouge2", "rougeL"]
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__    ,		rouge_keys=lowerCAmelCase__							)
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__    ,		rouge_keys=lowerCAmelCase__							)
    assert score_sep == score_no_sep





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				[
        "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
        "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
    ]
    snake_case__												=				[
        "Margot Frank, died in 1945, a month earlier than previously thought.",
        "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
        " the final seconds on board Flight 9525.",
    ]
    assert calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__							) == calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		newline_sep=lowerCAmelCase__							)





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				[
        "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
    ]
    snake_case__												=				[
        " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
    ]

    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		rouge_keys=["rougeLsum"]    ,		newline_sep=lowerCAmelCase__							)["rougeLsum"]
    snake_case__												=				calculate_rouge(lowerCAmelCase__    ,		lowerCAmelCase__    ,		rouge_keys=["rougeLsum"]							)["rougeLsum"]
    assert new_score > prev_score





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__												=				Path("examples/seq2seq/test_data/wmt_en_ro"							)
    snake_case__												=				calculate_rouge_path(data_dir.joinpath("test.source"							)    ,		data_dir.joinpath("test.target"							)							)
    assert isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							)
    snake_case__												=				calculate_rouge_path(
        data_dir.joinpath("test.source"							)    ,		data_dir.joinpath("test.target"							)    ,		bootstrap_aggregation=lowerCAmelCase__							)
    assert isinstance(lowerCAmelCase__    ,		lowerCAmelCase__							)





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__magic_name__            =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__magic_name__            =  '''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir		,	"models/bert/" ) )
          snake_case__						=				self.transformer_dir
          shutil.copy(
              os.path.join(lowerCamelCase		,	"src/transformers/models/bert/modeling_bert.py" )		,	os.path.join(self.transformer_dir		,	"models/bert/modeling_bert.py" )		,	)
      def A_			(			self ):
          snake_case__						=				"src/transformers"
          shutil.rmtree(self.transformer_dir )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase=None ):
          snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
              snake_case__						=				comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          snake_case__						=				black.Mode(target_versions={black.TargetVersion.PYaa}		,	line_length=1_19 )
          snake_case__						=				black.format_str(lowerCamelCase		,	mode=lowerCamelCase )
          snake_case__						=				os.path.join(self.transformer_dir		,	"new_code.py" )
          with open(lowerCamelCase		,	"w"		,	newline="\n" ) as f:
              f.write(lowerCamelCase )
          if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase ) ) == 0 )
          else:
              check_copies.is_copy_consistent(f.name		,	overwrite=lowerCamelCase )
              with open(lowerCamelCase		,	"r" ) as f:
                  self.assertTrue(f.read()		,	lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )
      def A_			(			self ):
          # Base copy consistency
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	REFERENCE_CODE + "\n"		,	)

          # With no empty line at the end
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	lowerCamelCase		,	)

          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)

          # Copy consistency with a really long name
          snake_case__						=				"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
          self.check_copy_consistency(
              F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,	F"""{long_class_name}LMPredictionHead"""		,	re.sub("Bert"		,	lowerCamelCase		,	lowerCamelCase )		,	)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	lowerCamelCase		,	overwrite_result=re.sub("Bert"		,	"TestModel"		,	lowerCamelCase )		,	)



      def A_			(			self ):
          snake_case__						=				check_copies.LOCALIZED_READMES["README_zh-hans.md"]

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
              " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
              " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
              " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
              " Luong, Quoc V. Le, Christopher D. Manning."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
              " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
              " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
              " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
              " method has been applied to compress GPT2 into"
              " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
              " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
              " Multilingual BERT into"
              " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
              " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
              " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
              " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
              " Christopher D. Manning 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          self.assertFalse(lowerCamelCase )
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(lowerCamelCase )

          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
              " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
              " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
              " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
              " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )
          snake_case__						=				(
              "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
              " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
              " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
              " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
          )

          snake_case__						,					snake_case__						=				check_copies.convert_to_localized_md(
              lowerCamelCase		,	lowerCamelCase		,	localized_readme["format_model_list"] )

          # Check if the model link is synchronized.
          self.assertEqual(lowerCamelCase		,	lowerCamelCase )



