


import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin


@require_phonemizer
class   a__	(						lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     Dict							    =     WavaVecaPhonemeCTCTokenizer
       lowerCamelCase__:     Any							    =     False

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
              super().setUp()

              a_    :     Optional[Any]										=    (
                  """<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː """
                  """ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː """
                  """ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 """
                  """oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ """
                  """pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ """
                  """yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ """
                  """əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ """
                  """ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ """
                  """ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ """
                  """uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ """
                  """ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ """
                  """ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ """
                  """ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"""
              ).split(""" """    )
              a_    :     Optional[int]										=    dict(zip(lowerCamelCase_      ,							range(len(lowerCamelCase_    )    )    )    )

              a_    :     Tuple										=    {"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

              a_    :     Union[str, Any]										=    os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]    )
              with open(self.vocab_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as fp:
                     fp.write(json.dumps(lowerCamelCase_    ) + """\n"""    )

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Dict=False      ,							lowerCamelCase_      :     List[Any]=2_0      ,							lowerCamelCase_      :     Any=5    ):
              a_    :     Dict										=    [(i, tokenizer.decode([i]      ,							clean_up_tokenization_spaces=lowerCamelCase_    )) for i in range(len(lowerCamelCase_    )    )]
              a_    :     Any										=    list(filter(lambda lowerCamelCase_    : [t[0]] == tokenizer.encode(t[1]      ,							do_phonemize=lowerCamelCase_    )      ,							lowerCamelCase_    )    )
              if max_length is not None and len(lowerCamelCase_    ) > max_length:
                     a_    :     Tuple										=    toks[:max_length]
              if min_length is not None and len(lowerCamelCase_    ) < min_length and len(lowerCamelCase_    ) > 0:
                     while len(lowerCamelCase_    ) < min_length:
                            a_    :     Optional[Any]										=    toks + toks
        # toks_str = [t[1] for t in toks]
              a_    :     Dict										=    [t[0] for t in toks]

              # Ensure consistency
              a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_      ,							clean_up_tokenization_spaces=lowerCamelCase_    )
              if " " not in output_txt and len(lowerCamelCase_    ) > 1:
                     a_    :     Optional[int]										=    (
                         tokenizer.decode([toks_ids[0]]      ,							clean_up_tokenization_spaces=lowerCamelCase_    )
                         + """ """
                         + tokenizer.decode(toks_ids[1:]      ,							clean_up_tokenization_spaces=lowerCamelCase_    )
                     )
              if with_prefix_space:
                     a_    :     Any										=    """ """ + output_txt
              a_    :     int										=    tokenizer.encode(lowerCamelCase_      ,							add_special_tokens=lowerCamelCase_    )
              return output_txt, output_ids

       def 						UpperCAmelCase(   self      :     Dict      ,							**lowerCamelCase_      :     List[Any]    ):
              kwargs.update(self.special_tokens_map    )
              return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname      ,							**lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
              a_    :     List[Any]										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )

              # check adding a single token
              tokenizer.add_tokens("""xxx"""    )
              a_    :     Optional[int]										=    tokenizer("""m xxx ɪ"""      ,							do_phonemize=lowerCamelCase_    ).input_ids
              self.assertEqual(lowerCamelCase_      ,							[1_3, 3_9_2, 1_7]    )  # xxx should be last token

              tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""]    )
              a_    :     Any										=    tokenizer("""m aaa ɪ ccc"""      ,							do_phonemize=lowerCamelCase_    ).input_ids
              self.assertEqual(lowerCamelCase_      ,							[1_3, 3_9_3, 1_7, 3_9_5]    )  # aaa and ccc should be after xxx and 2 after aaa

              a_    :     Tuple										=    tokenizer("""maɪ c"""      ,							do_phonemize=lowerCamelCase_    ).input_ids
              self.assertEqual(lowerCamelCase_      ,							[3, 2_0_0]    )  # mai should be <unk> (=3)

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
              a_    :     Any										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )

              a_    :     List[str]										=    """Hello how are you"""
              a_    :     List[str]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )
              self.assertEqual(lowerCamelCase_      ,							"""h ə l oʊ h aʊ ɑːɹ j uː"""    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
              a_    :     Dict										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )

              a_    :     Dict										=    """Hello how are you"""
              a_    :     Optional[int]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )
              self.assertEqual(tokenizer(lowerCamelCase_    ).input_ids      ,							tokenizer(lowerCamelCase_      ,							do_phonemize=lowerCamelCase_    ).input_ids    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
              a_    :     List[Any]										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )
              a_    :     str										=    """Hello how are you"""
              a_    :     List[Any]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )

              a_    :     Dict										=    tokenizer.decode(tokenizer(lowerCamelCase_    ).input_ids    )

              self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
              a_    :     List[str]										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )

              a_    :     Union[str, Any]										=    [
                  [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8],
                  [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7],
              ]
              a_    :     Tuple										=    tokenizer.decode(sample_ids[0]    )
              a_    :     Tuple										=    tokenizer.batch_decode(lowerCamelCase_    )
              self.assertEqual(lowerCamelCase_      ,							batch_tokens[0]    )
              self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]    )

       def 						UpperCAmelCase(   self      :     int    ):
              a_    :     Optional[int]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              a_    :     Any										=    """Hello how are you"""
              a_    :     Any										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )
              self.assertEqual(lowerCamelCase_      ,							"""h ə l oʊ | h aʊ | ɑːɹ | j uː |"""    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
              a_    :     Optional[int]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              a_    :     Any										=    """Hello how are you"""
              a_    :     List[Any]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )
              self.assertEqual(tokenizer(lowerCamelCase_    ).input_ids      ,							tokenizer(lowerCamelCase_      ,							do_phonemize=lowerCamelCase_    ).input_ids    )

       def 						UpperCAmelCase(   self      :     Optional[int]    ):
              a_    :     Union[str, Any]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              # fmt: off
              a_    :     Optional[int]										=    [
                  [1_1, 5, 1_5, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 1_5, 8, tokenizer.word_delimiter_token_id, 9_8],
                  [tokenizer.word_delimiter_token_id, 2_4, 2_2, tokenizer.word_delimiter_token_id, 5, 2_4, 2_2, 5, 7_7],
              ]
              # fmt: on

              # decode with word_del_token filter
              a_    :     List[str]										=    tokenizer.decode(sample_ids[0]    )
              a_    :     List[str]										=    tokenizer.batch_decode(lowerCamelCase_    )
              self.assertEqual(lowerCamelCase_      ,							batch_tokens[0]    )
              self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]    )

              # decode with no word_del_token filter
              a_    :     Optional[int]										=    tokenizer.decode(sample_ids[0]      ,							filter_word_delimiter_token=lowerCamelCase_    )
              a_    :     List[str]										=    tokenizer.batch_decode(lowerCamelCase_      ,							filter_word_delimiter_token=lowerCamelCase_    )
              self.assertEqual(lowerCamelCase_      ,							batch_tokens[0]    )
              self.assertEqual(lowerCamelCase_      ,							["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""]    )

       def 						UpperCAmelCase(   self      :     Dict    ):
              a_    :     List[Any]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              a_    :     Optional[int]										=    """Hello how are you"""
              a_    :     List[Any]										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )

              a_    :     Optional[int]										=    tokenizer.decode(tokenizer(lowerCamelCase_    ).input_ids      ,							filter_word_delimiter_token=lowerCamelCase_    )

              self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict    ):
              a_    :     Union[str, Any]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              a_    :     str										=    """Hello how are you"""
              a_    :     Tuple										=    tokenizer.phonemize(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    )

              a_    :     Union[str, Any]										=    tokenizer.decode(tokenizer(lowerCamelCase_    ).input_ids      ,							filter_word_delimiter_token=lowerCamelCase_    )

              self.assertEqual(""" """.join([p.strip() for p in phonemes.split(""" |"""    )]    ).strip()      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
              a_    :     Optional[Any]										=    self.tokenizer_class.from_pretrained(
                  """facebook/wav2vec2-lv-60-espeak-cv-ft"""      ,							word_delimiter_token=lowerCamelCase_    )
              a_    :     Union[str, Any]										=    """Hello how are you"""

              a_    :     str										=    tokenizer(lowerCamelCase_      ,							phonemizer_lang="""en-us"""    ).input_ids
              a_    :     Dict										=    tokenizer(lowerCamelCase_      ,							phonemizer_lang="""fr-fr"""    ).input_ids

              self.assertNotEqual(lowerCamelCase_      ,							lowerCamelCase_    )

              a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_    )
              a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_    )

              self.assertEqual(lowerCamelCase_      ,							"""h ə l oʊ h aʊ ɑːɹ j uː"""    )
              self.assertEqual(lowerCamelCase_      ,							"""ɛ l o h aʊ a ʁ j u"""    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
              a_    :     str										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )
              a_    :     Optional[Any]										=    """Hello how Are you"""
              a_    :     List[str]										=    """hello how are you"""

              a_    :     Tuple										=    tokenizer(lowerCamelCase_    ).input_ids
              a_    :     Any										=    tokenizer(lowerCamelCase_    ).input_ids

              self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[str]    ):
              a_    :     Dict										=    self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""    )
              tokenizer.add_tokens(["""!""", """?"""]    )
              tokenizer.add_special_tokens({"""cls_token""": """$$$"""}    )

              # fmt: off
              a_    :     Union[str, Any]										=    [
                  [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 8, 9_8, 3_9_2, 3_9_2, 3_9_3, 3_9_2, 3_9_2, 3_9_3, 3_9_4, 3_9_4],
                  [2_4, 2_2, 5, 2_4, 2_2, 5, 7_7, tokenizer.pad_token_id, 3_9_4, 3_9_4],
              ]
              # fmt: on

              a_    :     int										=    tokenizer.batch_decode(lowerCamelCase_    )
              self.assertEqual(lowerCamelCase_      ,							["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Any    ):
              a_    :     Optional[Any]										=    [d[key] for d in offsets]
              return retrieved_list

       def 						UpperCAmelCase(   self      :     Tuple    ):
              a_    :     Dict										=    self.get_tokenizer(word_delimiter_token="""|"""    )
              tokenizer.add_tokens("""|"""    )

              # fmt: off
              # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
              a_    :     Tuple										=    [1_1, 5, 5, 5, 1_5, 1_5, tokenizer.pad_token_id, 1_5, 1_5, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 1_5, 8, 8, 8, tokenizer.word_delimiter_token_id, 9_8]
              # fmt: on

              a_    :     Union[str, Any]										=    tokenizer.decode(lowerCamelCase_      ,							output_char_offsets=lowerCamelCase_      ,							filter_word_delimiter_token=lowerCamelCase_    )
              # check Wav2Vec2CTCTokenizerOutput keys for char
              self.assertEqual(len(outputs.keys()    )      ,							2    )
              self.assertTrue("""text""" in outputs    )
              self.assertTrue("""char_offsets""" in outputs    )
              self.assertTrue(isinstance(lowerCamelCase_      ,							lowerCamelCase_    )    )

              # check that order of chars is correct and identical for both outputs
              self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""]      ,							"""char"""    )    )      ,							outputs.text    )
              self.assertListEqual(
                  self.get_from_offsets(outputs["""char_offsets"""]      ,							"""char"""    )      ,							["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""]    )

              # check that offsets are actually correct for char
              # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
              # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
              self.assertListEqual(
                  self.get_from_offsets(outputs["""char_offsets"""]      ,							"""start_offset"""    )      ,							[0, 1, 4, 7, 9, 1_1, 1_2, 1_5, 1_6]    )
              self.assertListEqual(
                  self.get_from_offsets(outputs["""char_offsets"""]      ,							"""end_offset"""    )      ,							[1, 4, 6, 9, 1_0, 1_2, 1_5, 1_6, 1_7]    )

       def 						UpperCAmelCase(   self      :     Any    ):
              a_    :     List[Any]										=    self.get_tokenizer(word_delimiter_token="""|"""    )

              def check_list_tuples_equal(lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Tuple    ):
                     self.assertTrue(isinstance(lowerCamelCase_      ,							lowerCamelCase_    )    )
                     self.assertTrue(isinstance(outputs_list[0]      ,							lowerCamelCase_    )    )

                     # transform list to ModelOutput
                     a_    :     Any										=    WavaVecaPhonemeCTCTokenizerOutput(
                         {k: [d[k] for d in outputs_list] for k in outputs_list[0]}    )

                     self.assertListEqual(outputs_batch["""text"""]      ,							outputs_batch_a["""text"""]    )

                     def recursive_check(lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int    ):
                            if isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                                   [recursive_check(lowerCamelCase_      ,							lowerCamelCase_    ) for la, la in zip(lowerCamelCase_      ,							lowerCamelCase_    )]
                            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )

                     if "char_offsets" in outputs_batch:
                            recursive_check(outputs_batch["""char_offsets"""]      ,							outputs_batch_a["""char_offsets"""]    )

        # fmt: off
              a_    :     Tuple										=    [
                  [1_1, 5, 1_5, tokenizer.pad_token_id, 1_5, 4, 8, 9_8, 3_2, 3_2, 3_2, 3_2, 4, 3_3, tokenizer.word_delimiter_token_id, 3_2, 3_2, 3_3, 3_4, 3_4],
                  [2_4, 2_2, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 2_4, 2_2, 2_2, 2_2, 4, 5, 7_7, tokenizer.pad_token_id, 2_2, 2_2, 4, 3_4, 3_4, 3_4, 3_4],
              ]
              # fmt: on

              # We assume that `decode` works as expected. All we will check now is
              # the output type is correct and the output is identical to `decode`

              # char
              a_    :     int										=    tokenizer.batch_decode(lowerCamelCase_      ,							output_char_offsets=lowerCamelCase_    )
              a_    :     Union[str, Any]										=    [tokenizer.decode(lowerCamelCase_      ,							output_char_offsets=lowerCamelCase_    ) for ids in sample_ids]
              check_list_tuples_equal(lowerCamelCase_      ,							lowerCamelCase_    )

       @unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""    )
       def 						UpperCAmelCase(   self      :     Any    ):
              pass

       @unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"""    )
       def 						UpperCAmelCase(   self      :     str    ):
              pass

       @unittest.skip("""encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"""    )
       def 						UpperCAmelCase(   self      :     int    ):
              pass

       @unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing"""    )
       def 						UpperCAmelCase(   self      :     Tuple    ):
              pass

       def 						UpperCAmelCase(   self      :     Dict    ):
              a_    :     int										=    self.get_tokenizers(do_lower_case=lowerCamelCase_    )
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                            a_    :     Tuple										=    tokenizer.vocab_size
                            a_    :     List[Any]										=    len(lowerCamelCase_    )

                            self.assertNotEqual(lowerCamelCase_      ,							0    )

                            # We usually have added tokens from the start in tests because our vocab fixtures are
                            # smaller than the original vocabs - let's not assert this
                            # self.assertEqual(vocab_size, all_size)

                            a_    :     Union[str, Any]										=    ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
                            a_    :     Dict										=    tokenizer.add_tokens(lowerCamelCase_    )
                            a_    :     Union[str, Any]										=    tokenizer.vocab_size
                            a_    :     Optional[int]										=    len(lowerCamelCase_    )

                            self.assertNotEqual(lowerCamelCase_      ,							0    )
                            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )
                            self.assertEqual(lowerCamelCase_      ,							len(lowerCamelCase_    )    )
                            self.assertEqual(lowerCamelCase_      ,							all_size + len(lowerCamelCase_    )    )

                            a_    :     Dict										=    tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""      ,							add_special_tokens=lowerCamelCase_    )

                            self.assertGreaterEqual(len(lowerCamelCase_    )      ,							4    )
                            self.assertGreater(tokens[0]      ,							tokenizer.vocab_size - 1    )
                            self.assertGreater(tokens[-3]      ,							tokenizer.vocab_size - 1    )

                            a_    :     List[Any]										=    {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
                            a_    :     Dict										=    tokenizer.add_special_tokens(lowerCamelCase_    )
                            a_    :     Optional[int]										=    tokenizer.vocab_size
                            a_    :     Tuple										=    len(lowerCamelCase_    )

                            self.assertNotEqual(lowerCamelCase_      ,							0    )
                            self.assertEqual(lowerCamelCase_      ,							lowerCamelCase_    )
                            self.assertEqual(lowerCamelCase_      ,							len(lowerCamelCase_    )    )
                            self.assertEqual(lowerCamelCase_      ,							all_size_a + len(lowerCamelCase_    )    )

                            a_    :     Union[str, Any]										=    tokenizer.encode(
                                """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""      ,							add_special_tokens=lowerCamelCase_    )

                            self.assertGreaterEqual(len(lowerCamelCase_    )      ,							6    )
                            self.assertGreater(tokens[0]      ,							tokenizer.vocab_size - 1    )
                            self.assertGreater(tokens[0]      ,							tokens[1]    )
                            self.assertGreater(tokens[-3]      ,							tokenizer.vocab_size - 1    )
                            self.assertGreater(tokens[-3]      ,							tokens[-4]    )
                            self.assertEqual(tokens[0]      ,							tokenizer.eos_token_id    )
                            self.assertEqual(tokens[-3]      ,							tokenizer.pad_token_id    )

       @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""    )
       def 						UpperCAmelCase(   self      :     Optional[int]    ):
              pass

       @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""    )
       def 						UpperCAmelCase(   self      :     List[str]    ):
              pass

       def 						UpperCAmelCase(   self      :     Any    ):
              # The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
              # is not the case for Wav2Vec2PhonemeCTCTokenizer.
              a_    :     Optional[int]										=    self.get_tokenizers(fast=lowerCamelCase_      ,							do_lower_case=lowerCamelCase_    )
              for tokenizer in tokenizers:
                     with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
                            a_    :     List[str]										=    ["""ð""", """ɪ""", """s""", """ɪ""", """z""", """ɐ""", """t""", """ɛ""", """k""", """s""", """t"""]
                            a_    :     Tuple										=    tokenizer.convert_tokens_to_string(lowerCamelCase_    )

                            self.assertIsInstance(output["""text"""]      ,							lowerCamelCase_    )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase        =     False
__lowerCamelCase        =     False



def  _a			(						__UpperCamelCase ):
     return TrainCommand(__UpperCamelCase )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     List[Any]										=    parser.add_parser("""train"""      ,							help="""CLI tool to train a model on a task."""    )

            train_parser.add_argument(
                """--train_data"""      ,							type=lowerCamelCase_      ,							required=lowerCamelCase_      ,							help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""      ,							)
            train_parser.add_argument(
                """--column_label"""      ,							type=lowerCamelCase_      ,							default=0      ,							help="""Column of the dataset csv file with example labels."""    )
            train_parser.add_argument(
                """--column_text"""      ,							type=lowerCamelCase_      ,							default=1      ,							help="""Column of the dataset csv file with example texts."""    )
            train_parser.add_argument(
                """--column_id"""      ,							type=lowerCamelCase_      ,							default=2      ,							help="""Column of the dataset csv file with example ids."""    )
            train_parser.add_argument(
                """--skip_first_row"""      ,							action="""store_true"""      ,							help="""Skip the first row of the csv file (headers)."""    )

            train_parser.add_argument("""--validation_data"""      ,							type=lowerCamelCase_      ,							default=""""""      ,							help="""path to validation dataset."""    )
            train_parser.add_argument(
                """--validation_split"""      ,							type=lowerCamelCase_      ,							default=0.1      ,							help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""      ,							)

            train_parser.add_argument("""--output"""      ,							type=lowerCamelCase_      ,							default="""./"""      ,							help="""path to saved the trained model."""    )

            train_parser.add_argument(
                """--task"""      ,							type=lowerCamelCase_      ,							default="""text_classification"""      ,							help="""Task to train the model on."""    )
            train_parser.add_argument(
                """--model"""      ,							type=lowerCamelCase_      ,							default="""bert-base-uncased"""      ,							help="""Model's name or path to stored model."""    )
            train_parser.add_argument("""--train_batch_size"""      ,							type=lowerCamelCase_      ,							default=3_2      ,							help="""Batch size for training."""    )
            train_parser.add_argument("""--valid_batch_size"""      ,							type=lowerCamelCase_      ,							default=6_4      ,							help="""Batch size for validation."""    )
            train_parser.add_argument("""--learning_rate"""      ,							type=lowerCamelCase_      ,							default=3E-5      ,							help="""Learning rate."""    )
            train_parser.add_argument("""--adam_epsilon"""      ,							type=lowerCamelCase_      ,							default=1E-08      ,							help="""Epsilon for Adam optimizer."""    )
            train_parser.set_defaults(func=lowerCamelCase_    )

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Namespace    ):
            a_    :     Optional[int]										=    logging.get_logger("""transformers-cli/training"""    )

            a_    :     List[str]										=    """tf""" if is_tf_available() else """torch"""

            os.makedirs(args.output      ,							exist_ok=lowerCamelCase_    )
            a_    :     List[str]										=    args.output

            a_    :     Optional[int]										=    args.column_label
            a_    :     Tuple										=    args.column_text
            a_    :     Optional[int]										=    args.column_id

            self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''    )
            if args.task == "text_classification":
                 a_    :     str										=    TextClassificationPipeline.from_pretrained(args.model    )
            elif args.task == "token_classification":
                 raise NotImplementedError
            elif args.task == "question_answering":
                 raise NotImplementedError

            self.logger.info(F'''Loading dataset from {args.train_data}'''    )
            a_    :     int										=    Processor.create_from_csv(
                args.train_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)
            a_    :     Union[str, Any]										=    None
            if args.validation_data:
                 self.logger.info(F'''Loading validation dataset from {args.validation_data}'''    )
                 a_    :     Tuple										=    Processor.create_from_csv(
                     args.validation_data      ,							column_label=args.column_label      ,							column_text=args.column_text      ,							column_id=args.column_id      ,							skip_first_row=args.skip_first_row      ,							)

            a_    :     str										=    args.validation_split
            a_    :     Tuple										=    args.train_batch_size
            a_    :     List[Any]										=    args.valid_batch_size
            a_    :     str										=    args.learning_rate
            a_    :     str										=    args.adam_epsilon

       def 						UpperCAmelCase(   self      :     List[str]    ):
            if self.framework == "tf":
                 return self.run_tf()
            return self.run_torch()

       def 						UpperCAmelCase(   self      :     Any    ):
            raise NotImplementedError

       def 						UpperCAmelCase(   self      :     List[str]    ):
            self.pipeline.fit(
                self.train_dataset      ,							validation_data=self.valid_dataset      ,							validation_split=self.validation_split      ,							learning_rate=self.learning_rate      ,							adam_epsilon=self.adam_epsilon      ,							train_batch_size=self.train_batch_size      ,							valid_batch_size=self.valid_batch_size      ,							)

            # Save trained pipeline
            self.pipeline.save_pretrained(self.output    )



