



"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



      """simple docstring"""
      __UpperCamelCase		    =							BarthezTokenizer
      __UpperCamelCase		    =							BarthezTokenizerFast
      __UpperCamelCase		    =							True
      __UpperCamelCase		    =							True






      def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Tuple:
            super().setUp()

            UpperCAmelCase					=			BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
            tokenizer.save_pretrained(self.tmpdirname )
            tokenizer.save_pretrained(self.tmpdirname       ,  legacy_format=lowercase_ )
            UpperCAmelCase					=			tokenizer






      def      UpperCAmelCase__					( self			:Optional[int] )    ->      Union[str, Any]:
            UpperCAmelCase					=			'<pad>'
            UpperCAmelCase					=			1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ )       ,  lowercase_ )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ )       ,  lowercase_ )






      def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
            UpperCAmelCase					=			list(self.get_tokenizer().get_vocab().keys() )

            self.assertEqual(vocab_keys[0]       ,  '<s>' )
            self.assertEqual(vocab_keys[1]       ,  '<pad>' )
            self.assertEqual(vocab_keys[-1]       ,  '<mask>' )
            self.assertEqual(len(lowercase_ )       ,  10_11_22 )






      def      UpperCAmelCase__					( self			:Any )    ->      Optional[int]:
            self.assertEqual(self.get_tokenizer().vocab_size       ,  10_11_22 )






      @require_torch
      def      UpperCAmelCase__					( self			:Optional[int] )    ->      Dict:
            UpperCAmelCase					=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
            UpperCAmelCase					=			[0, 57, 30_18, 7_03_07, 91, 2]

            UpperCAmelCase					=			self.tokenizer(
                lowercase_       ,  max_length=len(lowercase_ )       ,  padding=lowercase_       ,  truncation=lowercase_       ,  return_tensors='pt' )
            self.assertIsInstance(lowercase_       ,  lowercase_ )

            self.assertEqual((2, 6)       ,  batch.input_ids.shape )
            self.assertEqual((2, 6)       ,  batch.attention_mask.shape )
            UpperCAmelCase					=			batch.input_ids.tolist()[0]
            self.assertListEqual(lowercase_       ,  lowercase_ )






      def      UpperCAmelCase__					( self			:Dict )    ->      str:
            if not self.test_rust_tokenizer:
                  return

            UpperCAmelCase					=			self.get_tokenizer()
            UpperCAmelCase					=			self.get_rust_tokenizer()

            UpperCAmelCase					=			'I was born in 92000, and this is falsé.'

            UpperCAmelCase					=			tokenizer.tokenize(lowercase_ )
            UpperCAmelCase					=			rust_tokenizer.tokenize(lowercase_ )
            self.assertListEqual(lowercase_       ,  lowercase_ )

            UpperCAmelCase					=			tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
            UpperCAmelCase					=			rust_tokenizer.encode(lowercase_       ,  add_special_tokens=lowercase_ )
            self.assertListEqual(lowercase_       ,  lowercase_ )

            UpperCAmelCase					=			self.get_rust_tokenizer()
            UpperCAmelCase					=			tokenizer.encode(lowercase_ )
            UpperCAmelCase					=			rust_tokenizer.encode(lowercase_ )
            self.assertListEqual(lowercase_       ,  lowercase_ )






      @slow
      def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      str:
            # fmt: off
            UpperCAmelCase					=			{'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            UpperCAmelCase					=			[
                'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
                'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
                'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
                'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
                'telles que la traduction et la synthèse de texte.',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=lowercase_       ,  model_name='moussaKam/mbarthez'       ,  revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'       ,  sequences=lowercase_       ,  )










"""simple docstring"""


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


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			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					=			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					=			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					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						# old PyTorch layer norm bias
						UpperCAmelCase					=			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				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

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

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												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					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





