
"""simple docstring"""


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			CodeGenTokenizer
       UpperCamelCase_		:					Union[str, Any]   =			CodeGenTokenizerFast
       UpperCamelCase_		:					Any   =			True
       UpperCamelCase_		:					Any   =			{'''add_prefix_space''': True}
       UpperCamelCase_		:					int   =			False






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase					 =		[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "<unk>",
                  "<|endoftext|>",
              ]
              UpperCamelCase					 =		dict(zip(UpperCAmelCase_       ,      range(len(UpperCAmelCase_			)			)			)			)
              UpperCamelCase					 =		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              UpperCamelCase					 =		{"unk_token": "<unk>"}

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      **UpperCAmelCase_ :							Tuple			)->  List[str]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      **UpperCAmelCase_ :							Any			)->  int:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							Optional[int]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		"lower newer"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		CodeGenTokenizer(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map			)
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokens + [tokenizer.unk_token]
              UpperCamelCase					 =		[14, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Dict:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_			)

              UpperCamelCase					 =		"lower newer"

              # Testing tokenization
              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # Testing conversion to ids without special tokens
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # Testing conversion to ids with special tokens
              UpperCamelCase					 =		self.get_rust_tokenizer(add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # Testing the unknown token
              UpperCamelCase					 =		tokens + [rust_tokenizer.unk_token]
              UpperCamelCase					 =		[14, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      *UpperCAmelCase_ :							Optional[Any]       ,      **UpperCAmelCase_ :							Union[str, Any]			)->  List[str]:







              """simple docstring"""
              # It's very difficult to mix/test pretokenization with byte-level
              # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
              pass






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							int=15			)->  Any:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)

                            # Simple input
                            UpperCamelCase					 =		"This is a simple input"
                            UpperCamelCase					 =		["This is a simple input 1", "This is a simple input 2"]
                            UpperCamelCase					 =		("This is a simple input", "This is a pair")
                            UpperCamelCase					 =		[
                                ("This is a simple input 1", "This is a simple input 2"),
                                ("This is a simple pair 1", "This is a simple pair 2"),
                            ]

                            # Simple input tests
                            self.assertRaises(UpperCAmelCase_       ,      tokenizer_r.encode       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"			)

                            # Simple input
                            self.assertRaises(UpperCAmelCase_       ,      tokenizer_r.encode_plus       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"			)

                            # Simple input
                            self.assertRaises(
                                UpperCAmelCase_       ,      tokenizer_r.batch_encode_plus       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"       ,      )

                            # Pair input
                            self.assertRaises(UpperCAmelCase_       ,      tokenizer_r.encode       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"			)

                            # Pair input
                            self.assertRaises(UpperCAmelCase_       ,      tokenizer_r.encode_plus       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"			)

                            # Pair input
                            self.assertRaises(
                                UpperCAmelCase_       ,      tokenizer_r.batch_encode_plus       ,      UpperCAmelCase_       ,      max_length=UpperCAmelCase_       ,      padding="max_length"       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      pad_token="<pad>"			)

              # Simple input
              UpperCamelCase					 =		"This is a simple input"
              UpperCamelCase					 =		["This is a simple input looooooooong", "This is a simple input"]
              UpperCamelCase					 =		("This is a simple input", "This is a pair")
              UpperCamelCase					 =		[
                  ("This is a simple input loooooong", "This is a simple input"),
                  ("This is a simple pair loooooong", "This is a simple pair"),
              ]

              UpperCamelCase					 =		tokenizer.pad_token_id

              UpperCamelCase					 =		tokenizer(UpperCAmelCase_       ,      padding="max_length"       ,      max_length=30       ,      return_tensors="np"			)
              UpperCamelCase					 =		tokenizer(UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncate=UpperCAmelCase_       ,      return_tensors="np"			)
              UpperCamelCase					 =		tokenizer(*UpperCAmelCase_       ,      padding="max_length"       ,      max_length=60       ,      return_tensors="np"			)
              UpperCamelCase					 =		tokenizer(UpperCAmelCase_       ,      padding=UpperCAmelCase_       ,      truncate=UpperCAmelCase_       ,      return_tensors="np"			)

              # s
              # test single string max_length padding
              self.assertEqual(out_s["input_ids"].shape[-1]       ,      30			)
              self.assertTrue(pad_token_id in out_s["input_ids"]			)
              self.assertTrue(0 in out_s["attention_mask"]			)

              # s2
              # test automatic padding
              self.assertEqual(out_sa["input_ids"].shape[-1]       ,      33			)
              # long slice doesn't have padding
              self.assertFalse(pad_token_id in out_sa["input_ids"][0]			)
              self.assertFalse(0 in out_sa["attention_mask"][0]			)
              # short slice does have padding
              self.assertTrue(pad_token_id in out_sa["input_ids"][1]			)
              self.assertTrue(0 in out_sa["attention_mask"][1]			)

              # p
              # test single pair max_length padding
              self.assertEqual(out_p["input_ids"].shape[-1]       ,      60			)
              self.assertTrue(pad_token_id in out_p["input_ids"]			)
              self.assertTrue(0 in out_p["attention_mask"]			)

              # p2
              # test automatic padding pair
              self.assertEqual(out_pa["input_ids"].shape[-1]       ,      52			)
              # long slice pair doesn't have padding
              self.assertFalse(pad_token_id in out_pa["input_ids"][0]			)
              self.assertFalse(0 in out_pa["attention_mask"][0]			)
              # short slice pair does have padding
              self.assertTrue(pad_token_id in out_pa["input_ids"][1]			)
              self.assertTrue(0 in out_pa["attention_mask"][1]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		"$$$"
              UpperCamelCase					 =		CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      bos_token=UpperCAmelCase_       ,      add_bos_token=UpperCAmelCase_			)

              UpperCamelCase					 =		"This is a simple input"
              UpperCamelCase					 =		["This is a simple input 1", "This is a simple input 2"]

              UpperCamelCase					 =		tokenizer.bos_token_id

              UpperCamelCase					 =		tokenizer(UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer(UpperCAmelCase_			)

              self.assertEqual(out_s.input_ids[0]       ,      UpperCAmelCase_			)
              self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids			)			)

              UpperCamelCase					 =		tokenizer.decode(out_s.input_ids			)
              UpperCamelCase					 =		tokenizer.batch_decode(out_sa.input_ids			)

              self.assertEqual(decode_s.split()[0]       ,      UpperCAmelCase_			)
              self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa			)			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono"			)

              UpperCamelCase					 =		"\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
              UpperCamelCase					 =		"\nif len_a > len_b:      result = a\nelse:      result = b"

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		["^#", re.escape("<|endoftext|>"			), "^'''", "^\"\"\"", "\n\n\n"]
              UpperCamelCase					 =		tokenizer.decode(UpperCAmelCase_       ,      truncate_before_pattern=UpperCAmelCase_			)
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  List[str]:







              """simple docstring"""
              pass







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





