



import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     SCREAMING_SNAKE_CASE_:Dict             =						"""pt"""
elif is_tf_available():
     SCREAMING_SNAKE_CASE_:Optional[int]             =						"""tf"""
else:
     SCREAMING_SNAKE_CASE_:Optional[int]             =						"""jax"""






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Any					=      PerceiverTokenizer
    __lowerCamelCase			: List[str]					=      False






    def 			_lowerCAmelCase					(       self		):
        super().setUp()
        A :       Optional[Any]          =   PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname		)






    @cached_property
    def 			_lowerCAmelCase					(       self		):
        return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""		)






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        return self.tokenizer_class.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=False,		lowerCamelCase__=20,		lowerCamelCase__=5		):
        # XXX The default common tokenizer tests assume that every ID is decodable on its own.
        # This assumption is invalid for Perceiver because single bytes might not be
        # valid utf-8 (byte 128 for instance).
        # Here we're overriding the smallest possible method to provide
        # a clean sequence without making the same assumption.

        A :       List[Any]          =   []
        for i in range(len(lowerCamelCase__		)		):
            try:
                A :       int          =   tokenizer.decode([i],		clean_up_tokenization_spaces=lowerCamelCase__		)
            except UnicodeDecodeError:
                pass
            toks.append((i, tok)		)

        A :       Optional[int]          =   list(filter(lambda lowerCamelCase__		: re.match(R"""^[ a-zA-Z]+$""",		t[1]		),		lowerCamelCase__		)		)
        A :       Optional[int]          =   list(filter(lambda lowerCamelCase__		: [t[0]] == tokenizer.encode(t[1],		add_special_tokens=lowerCamelCase__		),		lowerCamelCase__		)		)
        if max_length is not None and len(lowerCamelCase__		) > max_length:
            A :       Dict          =   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[int]          =   toks + toks
        # toks_str = [t[1] for t in toks]
        A :       int          =   [t[0] for t in toks]

        # Ensure consistency
        A :       Any          =   tokenizer.decode(lowerCamelCase__,		clean_up_tokenization_spaces=lowerCamelCase__		)
        if " " not in output_txt and len(lowerCamelCase__		) > 1:
            A :       Dict          =   (
                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 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.perceiver_tokenizer
        A :       List[str]          =   """Unicode €."""
        A :       Any          =   tokenizer(lowerCamelCase__		)
        A :       List[str]          =   [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
        self.assertEqual(encoded["""input_ids"""],		lowerCamelCase__		)

        # decoding
        A :       Union[str, Any]          =   tokenizer.decode(lowerCamelCase__		)
        self.assertEqual(lowerCamelCase__,		"""[CLS]Unicode €.[SEP]"""		)

        A :       int          =   tokenizer("""e è é ê ë"""		)
        A :       str          =   [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
        self.assertEqual(encoded["""input_ids"""],		lowerCamelCase__		)
        # decoding
        A :       str          =   tokenizer.decode(lowerCamelCase__		)
        self.assertEqual(lowerCamelCase__,		"""[CLS]e è é ê ë[SEP]"""		)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		),		"""[CLS]e è é ê ë[SEP]"""		)






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   self.perceiver_tokenizer
        A :       Optional[Any]          =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        # fmt: off
        A :       Optional[int]          =   [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
        # fmt: on
        A :       Optional[Any]          =   tokenizer(lowerCamelCase__,		padding=lowerCamelCase__,		return_tensors=lowerCamelCase__		)
        self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)

        if FRAMEWORK != "jax":
            A :       int          =   list(batch.input_ids.numpy()[0]		)
        else:
            A :       List[str]          =   list(batch.input_ids.tolist()[0]		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

        self.assertEqual((2, 38),		batch.input_ids.shape		)
        self.assertEqual((2, 38),		batch.attention_mask.shape		)






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   self.perceiver_tokenizer
        A :       List[Any]          =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        A :       Optional[int]          =   tokenizer(lowerCamelCase__,		padding=lowerCamelCase__,		return_tensors=lowerCamelCase__		)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("""input_ids""",		lowerCamelCase__		)
        self.assertIn("""attention_mask""",		lowerCamelCase__		)
        self.assertNotIn("""decoder_input_ids""",		lowerCamelCase__		)
        self.assertNotIn("""decoder_attention_mask""",		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.perceiver_tokenizer
        A :       Optional[Any]          =   [
            """Summary of the text.""",
            """Another summary.""",
        ]
        A :       Any          =   tokenizer(
            text_target=lowerCamelCase__,		max_length=32,		padding="""max_length""",		truncation=lowerCamelCase__,		return_tensors=lowerCamelCase__		)
        self.assertEqual(32,		targets["""input_ids"""].shape[1]		)






    def 			_lowerCAmelCase					(       self		):
        # safety check on max_len default value so we are sure the test works
        A :       str          =   self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                self.assertNotEqual(tokenizer.model_max_length,		42		)

        # Now let's start the test
        A :       Optional[int]          =   self.get_tokenizers()
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                # Isolate this from the other tests because we save additional tokens/etc
                A :       Union[str, Any]          =   tempfile.mkdtemp()

                A :       str          =   """ He is very happy, UNwant\u00E9d,running"""
                A :       Dict          =   tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		)
                tokenizer.save_pretrained(lowerCamelCase__		)

                A :       List[str]          =   tokenizer.__class__.from_pretrained(lowerCamelCase__		)
                A :       Optional[Any]          =   after_tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		)
                self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)

                shutil.rmtree(lowerCamelCase__		)

        A :       Dict          =   self.get_tokenizers(model_max_length=42		)
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                # Isolate this from the other tests because we save additional tokens/etc
                A :       Union[str, Any]          =   tempfile.mkdtemp()

                A :       Optional[int]          =   """ He is very happy, UNwant\u00E9d,running"""
                tokenizer.add_tokens(["""bim""", """bambam"""]		)
                A :       List[str]          =   tokenizer.additional_special_tokens
                additional_special_tokens.append("""new_additional_special_token"""		)
                tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
                A :       Union[str, Any]          =   tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		)
                tokenizer.save_pretrained(lowerCamelCase__		)

                A :       List[Any]          =   tokenizer.__class__.from_pretrained(lowerCamelCase__		)
                A :       Optional[Any]          =   after_tokenizer.encode(lowerCamelCase__,		add_special_tokens=lowerCamelCase__		)
                self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)
                self.assertIn("""new_additional_special_token""",		after_tokenizer.additional_special_tokens		)
                self.assertEqual(after_tokenizer.model_max_length,		42		)

                A :       Optional[int]          =   tokenizer.__class__.from_pretrained(lowerCamelCase__,		model_max_length=43		)
                self.assertEqual(tokenizer.model_max_length,		43		)

                shutil.rmtree(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   []
        if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

        if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
                tokenizer_utils.save_pretrained(lowerCamelCase__		)

                with open(os.path.join(lowerCamelCase__,		"""special_tokens_map.json"""		),		encoding="""utf-8"""		) as json_file:
                    A :       Optional[Any]          =   json.load(lowerCamelCase__		)

                with open(os.path.join(lowerCamelCase__,		"""tokenizer_config.json"""		),		encoding="""utf-8"""		) as json_file:
                    A :       List[Any]          =   json.load(lowerCamelCase__		)

                A :       Union[str, Any]          =   [f'''<extra_id_{i}>''' for i in range(125		)]

                A :       Any          =   added_tokens_extra_ids + [
                    """an_additional_special_token"""
                ]
                A :       Union[str, Any]          =   added_tokens_extra_ids + [
                    """an_additional_special_token"""
                ]

                with open(os.path.join(lowerCamelCase__,		"""special_tokens_map.json"""		),		"""w""",		encoding="""utf-8"""		) as outfile:
                    json.dump(lowerCamelCase__,		lowerCamelCase__		)
                with open(os.path.join(lowerCamelCase__,		"""tokenizer_config.json"""		),		"""w""",		encoding="""utf-8"""		) as outfile:
                    json.dump(lowerCamelCase__,		lowerCamelCase__		)

                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                # "special_tokens_map.json" files
                A :       Any          =   tokenizer_class.from_pretrained(
                    lowerCamelCase__,		)
                self.assertIn(
                    """an_additional_special_token""",		tokenizer_without_change_in_init.additional_special_tokens		)
                self.assertEqual(
                    ["""an_additional_special_token"""],		tokenizer_without_change_in_init.convert_ids_to_tokens(
                        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]		)		),		)

                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                A :       Union[str, Any]          =   added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""",		lstrip=lowerCamelCase__		)]
                A :       Any          =   tokenizer_class.from_pretrained(
                    lowerCamelCase__,		additional_special_tokens=lowerCamelCase__,		)

                self.assertIn("""a_new_additional_special_token""",		tokenizer.additional_special_tokens		)
                self.assertEqual(
                    ["""a_new_additional_special_token"""],		tokenizer.convert_ids_to_tokens(
                        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]		)		),		)






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]		),		"""�"""		)






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
        # strings and special added tokens as tokens
        A :       Dict          =   self.get_tokenizers(fast=lowerCamelCase__,		do_lower_case=lowerCamelCase__		)
        for tokenizer in tokenizers:
            with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                A :       Union[str, Any]          =   ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                A :       List[str]          =   tokenizer.convert_tokens_to_string(lowerCamelCase__		)

                self.assertIsInstance(lowerCamelCase__,		lowerCamelCase__		)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       Any          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       int          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


