"""simple docstring"""




from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_a   :	Union[str, Any] 					=     [
    "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.",
]

_a   :	Dict 					=     [
    "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  (    ) ->   str:
  _lowerCAmelCase		: List[str]						    =  calculate_rouge(__snake_case    ,__snake_case    ,bootstrap_aggregation=__snake_case    ,rouge_keys=["""rouge2""", """rougeL"""]			)
  assert isinstance(__snake_case    ,__snake_case			)
  _lowerCAmelCase		: Optional[int]						    =  calculate_rouge(__snake_case    ,__snake_case    ,bootstrap_aggregation=__snake_case    ,rouge_keys=["""rouge2"""]			)
  assert (
      pd.DataFrame(no_aggregation["""rouge2"""]			).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]			).fmeasure.mean()
  )





def 							SCREAMING_SNAKE_CASE  (    ) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  "rougeLsum"
  _lowerCAmelCase		: Optional[Any]						    =  calculate_rouge(__snake_case    ,__snake_case    ,newline_sep=__snake_case    ,rouge_keys=[k]			)[k]
  _lowerCAmelCase		: Optional[Any]						    =  calculate_rouge(__snake_case    ,__snake_case    ,newline_sep=__snake_case    ,rouge_keys=[k]			)[k]
  assert score > score_no_sep





def 							SCREAMING_SNAKE_CASE  (    ) ->   str:
  _lowerCAmelCase		: str						    =  ["rouge1", "rouge2", "rougeL"]
  _lowerCAmelCase		: Optional[Any]						    =  calculate_rouge(__snake_case    ,__snake_case    ,newline_sep=__snake_case    ,rouge_keys=__snake_case			)
  _lowerCAmelCase		: Union[str, Any]						    =  calculate_rouge(__snake_case    ,__snake_case    ,newline_sep=__snake_case    ,rouge_keys=__snake_case			)
  assert score_sep == score_no_sep





def 							SCREAMING_SNAKE_CASE  (    ) ->   Optional[Any]:
  _lowerCAmelCase		: List[str]						    =  [
      "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 .",
  ]
  _lowerCAmelCase		: Optional[Any]						    =  [
      "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(__snake_case    ,__snake_case    ,newline_sep=__snake_case			) == calculate_rouge(__snake_case    ,__snake_case    ,newline_sep=__snake_case			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   List[Any]:
  _lowerCAmelCase		: Optional[Any]						    =  [
      "\" \"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\" "
  ]
  _lowerCAmelCase		: Optional[int]						    =  [
      " 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 ."
  ]

  _lowerCAmelCase		: Any						    =  calculate_rouge(__snake_case    ,__snake_case    ,rouge_keys=["""rougeLsum"""]    ,newline_sep=__snake_case			)["rougeLsum"]
  _lowerCAmelCase		: Union[str, Any]						    =  calculate_rouge(__snake_case    ,__snake_case    ,rouge_keys=["""rougeLsum"""]			)["rougeLsum"]
  assert new_score > prev_score






def 							SCREAMING_SNAKE_CASE  (    ) ->   Optional[Any]:
  _lowerCAmelCase		: int						    =  Path("""examples/seq2seq/test_data/wmt_en_ro"""			)
  _lowerCAmelCase		: Any						    =  calculate_rouge_path(data_dir.joinpath("""test.source"""			)    ,data_dir.joinpath("""test.target"""			)			)
  assert isinstance(__snake_case    ,__snake_case			)
  _lowerCAmelCase		: List[str]						    =  calculate_rouge_path(
      data_dir.joinpath("""test.source"""			)    ,data_dir.joinpath("""test.target"""			)    ,bootstrap_aggregation=__snake_case			)
  assert isinstance(__snake_case    ,__snake_case			)

"""simple docstring"""




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFTransfoXLForSequenceClassification,
         TFTransfoXLLMHeadModel,
         TFTransfoXLModel,
     )
class 		__A							:






     def __init__(	self       ,  a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Tuple						    =  13
       _lowerCAmelCase		: Tuple						    =  7
       _lowerCAmelCase		: Any						    =  30
       _lowerCAmelCase		: Optional[int]						    =  self.seq_length + self.mem_len
       _lowerCAmelCase		: Dict						    =  15
       _lowerCAmelCase		: List[Any]						    =  True
       _lowerCAmelCase		: Any						    =  True
       _lowerCAmelCase		: List[str]						    =  99
       _lowerCAmelCase		: List[Any]						    =  [10, 50, 80]
       _lowerCAmelCase		: Tuple						    =  32
       _lowerCAmelCase		: int						    =  32
       _lowerCAmelCase		: Dict						    =  4
       _lowerCAmelCase		: List[str]						    =  8
       _lowerCAmelCase		: Tuple						    =  128
       _lowerCAmelCase		: Any						    =  2
       _lowerCAmelCase		: List[Any]						    =  2
       _lowerCAmelCase		: List[Any]						    =  None
       _lowerCAmelCase		: Optional[Any]						    =  1
       _lowerCAmelCase		: Tuple						    =  0
       _lowerCAmelCase		: List[Any]						    =  3
       _lowerCAmelCase		: Optional[int]						    =  self.vocab_size - 1
       _lowerCAmelCase		: Dict						    =  0.0_1






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)
       _lowerCAmelCase		: Optional[Any]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

       _lowerCAmelCase		: List[str]						    =  None
       if self.use_labels:
         _lowerCAmelCase		: int						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

       _lowerCAmelCase		: Union[str, Any]						    =  TransfoXLConfig(
           vocab_size=self.vocab_size       ,  mem_len=self.mem_len       ,  clamp_len=self.clamp_len       ,  cutoffs=self.cutoffs       ,  d_model=self.hidden_size       ,  d_embed=self.d_embed       ,  n_head=self.num_attention_heads       ,  d_head=self.d_head       ,  d_inner=self.d_inner       ,  div_val=self.div_val       ,  n_layer=self.num_hidden_layers       ,  eos_token_id=self.eos_token_id       ,  pad_token_id=self.vocab_size - 1       ,  init_range=self.init_range       ,  num_labels=self.num_labels       ,  )

       return (config, input_ids_a, input_ids_a, lm_labels)






     def 					__A						(	self						):
       random.seed(self.seed						)
       tf.random.set_seed(self.seed						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  TFTransfoXLModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Optional[Any]						    =  {"""input_ids""": input_ids_a, """mems""": mems_a}

       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model(a__						).to_tuple()

       self.parent.assertEqual(hidden_states_a.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)						)
       self.parent.assertEqual(hidden_states_a.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)						)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  TFTransfoXLLMHeadModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Dict						    =  {"""input_ids""": input_ids_a, """labels""": lm_labels}
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  model([input_ids_a, mems_a]						).to_tuple()

       _lowerCAmelCase		: Any						    =  {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).to_tuple()

       self.parent.assertEqual(lm_logits_a.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)						)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )

       self.parent.assertEqual(lm_logits_a.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)						)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]       ,  [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers       ,  )






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLForSequenceClassification(a__						)
       _lowerCAmelCase		: int						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.prepare_config_and_inputs()
       ((_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase))		: Dict						    =  config_and_inputs
       _lowerCAmelCase		: List[Any]						    =  {"""input_ids""": input_ids_a}
       return config, inputs_dict







@require_tf
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _UpperCamelCase							:    Tuple	      =	() if is_tf_available() else ()
     _UpperCamelCase							:    Any	      =	(
         {
             "feature-extraction": TFTransfoXLModel,
             "text-classification": TFTransfoXLForSequenceClassification,
             "text-generation": TFTransfoXLLMHeadModel,
             "zero-shot": TFTransfoXLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    Tuple	      =	False
     _UpperCamelCase							:    Any	      =	False






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
         # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
         # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
         # tokenizer.
         return True

       return False






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLModelTester(self						)
       _lowerCAmelCase		: List[Any]						    =  ConfigTester(self       ,  config_class=a__       ,  d_embed=37						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*a__						)






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: List[Any]						    =  [TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: Optional[Any]						    =  model_class(a__						)
         assert isinstance(model.get_input_embeddings()       ,  tf.keras.layers.Layer						)
         if model_class in list_other_models_with_output_ebd:
           _lowerCAmelCase		: str						    =  model.get_output_embeddings()
           assert isinstance(a__       ,  tf.keras.layers.Layer						)
           _lowerCAmelCase		: Optional[int]						    =  model.get_bias()
           assert name is None
         else:
           _lowerCAmelCase		: Union[str, Any]						    =  model.get_output_embeddings()
           assert x is None
           _lowerCAmelCase		: Optional[int]						    =  model.get_bias()
           assert name is None






     def 					__A						(	self						):
       # TODO JP: Make TransfoXL XLA compliant
       pass






     @slow
     def 					__A						(	self						):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Union[str, Any]						    =  TFTransfoXLModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)






     @unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""						)
     def 					__A						(	self						):
       pass







@require_tf
class 		__A							(				unittest.TestCase	):






     @unittest.skip("""Skip test until #12651 is resolved."""						)
     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
       # fmt: off
       _lowerCAmelCase		: List[str]						    =  tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]       ,  dtype=tf.intaa						)  # noqa: E231
       # fmt: on
       #  In 1991 , the remains of Russian Tsar Nicholas II and his family
       #  ( except for Alexei and Maria ) are discovered .
       #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
       #  remainder of the story . 1883 Western Siberia ,
       #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
       #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
       #  father initially slaps him for making such an accusation , Rasputin watches as the
       #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
       #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
       #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

       # fmt: off
       _lowerCAmelCase		: List[Any]						    =  [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # noqa: E231
       # fmt: on
       #  In 1991, the remains of Russian Tsar Nicholas II and his family (
       #  except for Alexei and Maria ) are discovered. The voice of young son,
       #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
       #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
       #  and a group of men to perform magic. Rasputin has a vision and
       #  denounces one of the men as a horse thief. Although his father initially
       #  slaps him for making such an accusation, Rasputin watches as the man
       #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
       #  of the Virgin Mary, prompting him to become a priest.
       #  Rasputin quickly becomes famous, with people, even a bishop, begging for
       #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
       # Nicholas II and his family were discovered. The voice of <unk> young son,
       # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

       _lowerCAmelCase		: Tuple						    =  model.generate(a__       ,  max_length=200       ,  do_sample=a__						)
       self.assertListEqual(output_ids[0].numpy().tolist()       ,  a__						)




