import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Dict						=      MvpTokenizer
    a__	:						str						=      MvpTokenizerFast
    a__	:						Any						=      True
    a__	:						Optional[int]						=      filter_roberta_detectors







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        super().setUp()
        __UpperCamelCase     :str			  =		[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        __UpperCamelCase     :int			  =		dict(zip(__lowercase , range(len(__lowercase))))
        __UpperCamelCase     :Dict			  =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        __UpperCamelCase     :List[str]			  =		{'''unk_token''': '''<unk>'''}

        __UpperCamelCase     :Optional[Any]			  =		os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase     :str			  =		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(__lowercase) + '''\n''')
        with open(self.merges_file , '''w''' , encoding='''utf-8''') as fp:
            fp.write('''\n'''.join(__lowercase))







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Optional[int]:
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self , **__lowercase)       ->	Union[str, Any]:
        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	str:
        return "lower newer", "lower newer"







    @cached_property
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''')







    @cached_property
    def 			UpperCamelCase__   (	self)       ->	Any:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''')







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Tuple			  =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        __UpperCamelCase     :Dict			  =		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __UpperCamelCase     :Tuple			  =		tokenizer(__lowercase , max_length=len(__lowercase) , padding=__lowercase , return_tensors='''pt''')
            self.assertIsInstance(__lowercase , __lowercase)

            self.assertEqual((2, 9) , batch.input_ids.shape)
            self.assertEqual((2, 9) , batch.attention_mask.shape)
            __UpperCamelCase     :List[Any]			  =		batch.input_ids.tolist()[0]
            self.assertListEqual(__lowercase , __lowercase)
            # Test that special tokens are reset







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :List[Any]			  =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __UpperCamelCase     :int			  =		tokenizer(__lowercase , padding=__lowercase , return_tensors='''pt''')
            # check if input_ids are returned and no labels
            self.assertIn('''input_ids''' , __lowercase)
            self.assertIn('''attention_mask''' , __lowercase)
            self.assertNotIn('''labels''' , __lowercase)
            self.assertNotIn('''decoder_attention_mask''' , __lowercase)







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :List[Any]			  =		[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __UpperCamelCase     :Union[str, Any]			  =		tokenizer(text_target=__lowercase , max_length=32 , padding='''max_length''' , return_tensors='''pt''')
            self.assertEqual(32 , targets['''input_ids'''].shape[1])







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __UpperCamelCase     :Union[str, Any]			  =		tokenizer(
                ['''I am a small frog''' * 1_024, '''I am a small frog'''] , padding=__lowercase , truncation=__lowercase , return_tensors='''pt''')
            self.assertIsInstance(__lowercase , __lowercase)
            self.assertEqual(batch.input_ids.shape , (2, 1_024))







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :List[Any]			  =		['''A long paragraph for summarization.''']
        __UpperCamelCase     :List[Any]			  =		[
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            __UpperCamelCase     :List[Any]			  =		tokenizer(__lowercase , text_target=__lowercase , return_tensors='''pt''')
            __UpperCamelCase     :Tuple			  =		inputs['''input_ids''']
            __UpperCamelCase     :Optional[int]			  =		inputs['''labels''']
            self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
            self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        pass







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                __UpperCamelCase     :List[Any]			  =		self.rust_tokenizer_class.from_pretrained(__lowercase , **__lowercase)
                __UpperCamelCase     :int			  =		self.tokenizer_class.from_pretrained(__lowercase , **__lowercase)
                __UpperCamelCase     :str			  =		'''A, <mask> AllenNLP sentence.'''
                __UpperCamelCase     :List[Any]			  =		tokenizer_r.encode_plus(__lowercase , add_special_tokens=__lowercase , return_token_type_ids=__lowercase)
                __UpperCamelCase     :Union[str, Any]			  =		tokenizer_p.encode_plus(__lowercase , add_special_tokens=__lowercase , return_token_type_ids=__lowercase)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']) , sum(tokens_p['''token_type_ids''']))

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask''']) , sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask''']) , )

                __UpperCamelCase     :Union[str, Any]			  =		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                __UpperCamelCase     :Optional[Any]			  =		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

                self.assertSequenceEqual(
                    __lowercase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])
                self.assertSequenceEqual(
                    __lowercase , ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''])






import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		R'''\w+[.]\d+'''
    __UpperCamelCase     :List[str]			  =		re.findall(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    for pat in pats:
        __UpperCamelCase     :int			  =		key.replace(SCREAMING_SNAKE_CASE  ,			'''_'''.join(pat.split('''.'''	)	)	)
    return key



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key	)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        __UpperCamelCase     :Any			  =		pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        __UpperCamelCase     :str			  =		pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        __UpperCamelCase     :List[Any]			  =		pt_tensor.transpose(2  ,			3  ,			1  ,			0	)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    __UpperCamelCase     :List[str]			  =		pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        __UpperCamelCase     :Any			  =		pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    __UpperCamelCase     :int			  =		pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=42	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    __UpperCamelCase     :str			  =		flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE	)	)

    __UpperCamelCase     :int			  =		flatten_dict(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        __UpperCamelCase     :List[Any]			  =		rename_key(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :List[Any]			  =		tuple(renamed_pt_key.split('''.'''	)	)

        # Correctly rename weight parameters
        __UpperCamelCase  ,       __UpperCamelCase     :Any			  =		rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
        __UpperCamelCase     :str			  =		jnp.asarray(SCREAMING_SNAKE_CASE	)

    return unflatten_dict(SCREAMING_SNAKE_CASE	)





