




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					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  BarthezTokenizer
     lowerCAmelCase    :	List[Any]       		=  BarthezTokenizerFast
     lowerCAmelCase    :	int       		=  True
     lowerCAmelCase    :	Tuple       		=  True
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         super().setUp()

         UpperCAmelCase_										=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''    )
         tokenizer.save_pretrained(self.tmpdirname    )
         tokenizer.save_pretrained(self.tmpdirname ,   legacy_format=__snake_case    )
         UpperCAmelCase_										=					tokenizer
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					'''<pad>'''
         UpperCAmelCase_										=					1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case    ) ,   __snake_case    )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case    ) ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      List[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(__snake_case    ) ,   10_11_22    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         self.assertEqual(self.get_tokenizer().vocab_size ,   10_11_22    )
     @require_torch
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         UpperCAmelCase_										=					[0, 57, 30_18, 7_03_07, 91, 2]

         UpperCAmelCase_										=					self.tokenizer(
             __snake_case ,   max_length=len(__snake_case    ) ,   padding=__snake_case ,   truncation=__snake_case ,   return_tensors='''pt'''    )
         self.assertIsInstance(__snake_case ,   __snake_case    )

         self.assertEqual((2, 6) ,   batch.input_ids.shape    )
         self.assertEqual((2, 6) ,   batch.attention_mask.shape    )
         UpperCAmelCase_										=					batch.input_ids.tolist()[0]
         self.assertListEqual(__snake_case ,   __snake_case    )
     def   lowerCamelCase_       (     self       :      Tuple    ):
         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(__snake_case    )
         UpperCAmelCase_										=					rust_tokenizer.tokenize(__snake_case    )
         self.assertListEqual(__snake_case ,   __snake_case    )

         UpperCAmelCase_										=					tokenizer.encode(__snake_case ,   add_special_tokens=__snake_case    )
         UpperCAmelCase_										=					rust_tokenizer.encode(__snake_case ,   add_special_tokens=__snake_case    )
         self.assertListEqual(__snake_case ,   __snake_case    )

         UpperCAmelCase_										=					self.get_rust_tokenizer()
         UpperCAmelCase_										=					tokenizer.encode(__snake_case    )
         UpperCAmelCase_										=					rust_tokenizer.encode(__snake_case    )
         self.assertListEqual(__snake_case ,   __snake_case    )




     @slow
     def   lowerCamelCase_       (     self       :      Any    ):
         # 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=__snake_case ,   model_name='''moussaKam/mbarthez''' ,   revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,   sequences=__snake_case ,   )











import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[dict, list, tuple, torch.Tensor]				)  ->							List[Tuple[int, ...]]:
    UpperCAmelCase_										=					[]
    if isinstance(__UpperCamelCase	,					__UpperCamelCase				):
        for v in tree.values():
            shapes.extend(_fetch_dims(__UpperCamelCase				)				)
    elif isinstance(__UpperCamelCase	,					(list, tuple)				):
        for t in tree:
            shapes.extend(_fetch_dims(__UpperCamelCase				)				)
    elif isinstance(__UpperCamelCase	,					torch.Tensor				):
        shapes.append(tree.shape				)
    else:
        raise ValueError('''Not supported'''				)

    return shapes




@torch.jit.ignore
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Tuple[int, ...]				)  ->							Tuple[int, ...]:
    UpperCAmelCase_										=					[]
    for d in reversed(__UpperCamelCase				):
        idx.append(flat_idx % d				)
        UpperCAmelCase_										=					flat_idx // d

    return tuple(reversed(__UpperCamelCase				)				)




@torch.jit.ignore
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Sequence[int]	,					__UpperCamelCase      :						Optional[Sequence[bool]] = None	,					__UpperCamelCase      :						Optional[Sequence[bool]] = None	,					)  ->							List[Tuple[slice, ...]]:

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(__UpperCamelCase      :						List[bool]				) -> None:
        UpperCAmelCase_										=					True
        for i in range(len(__UpperCamelCase				)				):
            UpperCAmelCase_										=					-1 * (i + 1)
            l[reversed_idx] &= tally
            UpperCAmelCase_										=					l[reversed_idx]

    if start_edges is None:
        UpperCAmelCase_										=					[s == 0 for s in start]
        reduce_edge_list(__UpperCamelCase				)
    if end_edges is None:
        UpperCAmelCase_										=					[e == (d - 1) for e, d in zip(__UpperCamelCase	,					__UpperCamelCase				)]
        reduce_edge_list(__UpperCamelCase				)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(__UpperCamelCase				) == 0:
        return [()]
    elif len(__UpperCamelCase				) == 1:
        return [(slice(start[0]	,					end[0] + 1				),)]

    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					[]

    # Dimensions common to start and end can be selected directly
    for s, e in zip(__UpperCamelCase	,					__UpperCamelCase				):
        if s == e:
            path_list.append(slice(__UpperCamelCase	,					s + 1				)				)
        else:
            break

    UpperCAmelCase_										=					tuple(__UpperCamelCase				)
    UpperCAmelCase_										=					len(__UpperCamelCase				)

    # start == end, and we're done
    if divergence_idx == len(__UpperCamelCase				):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        UpperCAmelCase_										=					start[divergence_idx]
        return tuple(
            path + (slice(__UpperCamelCase	,					sdi + 1				),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :]	,					[d - 1 for d in dims[divergence_idx + 1 :]]	,					dims[divergence_idx + 1 :]	,					start_edges=start_edges[divergence_idx + 1 :]	,					end_edges=[True for _ in end_edges[divergence_idx + 1 :]]	,					)				)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        UpperCAmelCase_										=					end[divergence_idx]
        return tuple(
            path + (slice(__UpperCamelCase	,					edi + 1				),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]]	,					end[divergence_idx + 1 :]	,					dims[divergence_idx + 1 :]	,					start_edges=[True for _ in start_edges[divergence_idx + 1 :]]	,					end_edges=end_edges[divergence_idx + 1 :]	,					)				)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]	,					end[divergence_idx] + 1				),)				)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]	,					end[divergence_idx]				),)				)
        slices.extend(lower()				)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()				)
        slices.append(path + (slice(start[divergence_idx] + 1	,					end[divergence_idx] + 1				),)				)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()				)
        UpperCAmelCase_										=					end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1	,					end[divergence_idx]				),)				)
        slices.extend(lower()				)

    return slices




@torch.jit.ignore
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						torch.Tensor	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							torch.Tensor:
    UpperCAmelCase_										=					t.shape[:no_batch_dims]
    UpperCAmelCase_										=					list(_flat_idx_to_idx(__UpperCamelCase	,					__UpperCamelCase				)				)
    # _get_minimal_slice_set is inclusive
    UpperCAmelCase_										=					list(_flat_idx_to_idx(flat_end - 1	,					__UpperCamelCase				)				)

    # Get an ordered list of slices to perform
    UpperCAmelCase_										=					_get_minimal_slice_set(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					)

    UpperCAmelCase_										=					[t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Callable	,					__UpperCamelCase      :						Dict[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						bool = False	,					__UpperCamelCase      :						Any = None	,					__UpperCamelCase      :						bool = False	,					)  ->							Any:
    if not (len(__UpperCamelCase				) > 0):
        raise ValueError('''Must provide at least one input'''				)

    UpperCAmelCase_										=					[shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase				)]
    UpperCAmelCase_										=					tuple([max(__UpperCamelCase				) for s in zip(*__UpperCamelCase				)]				)

    def _prep_inputs(__UpperCamelCase      :						torch.Tensor				) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
                UpperCAmelCase_										=					t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
            UpperCAmelCase_										=					t.reshape(-1	,					*t.shape[no_batch_dims:]				)
        else:
            UpperCAmelCase_										=					t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
        return t

    UpperCAmelCase_										=					tensor_tree_map(_prep_inputs	,					__UpperCamelCase				)
    UpperCAmelCase_										=					None
    if _out is not None:
        UpperCAmelCase_										=					tensor_tree_map(lambda __UpperCamelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				)	,					_out				)

    UpperCAmelCase_										=					1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    UpperCAmelCase_										=					flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(__UpperCamelCase      :						torch.Tensor				) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    UpperCAmelCase_										=					0
    UpperCAmelCase_										=					prepped_outputs
    for _ in range(__UpperCamelCase				):
        # Chunk the input
        if not low_mem:
            UpperCAmelCase_										=					_select_chunk
        else:
            UpperCAmelCase_										=					partial(
                _chunk_slice	,					flat_start=__UpperCamelCase	,					flat_end=min(__UpperCamelCase	,					i + chunk_size				)	,					no_batch_dims=len(__UpperCamelCase				)	,					)

        UpperCAmelCase_										=					tensor_tree_map(__UpperCamelCase	,					__UpperCamelCase				)

        # Run the layer on the chunk
        UpperCAmelCase_										=					layer(**__UpperCamelCase				)

        # Allocate space for the output
        if out is None:
            UpperCAmelCase_										=					tensor_tree_map(lambda __UpperCamelCase				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				)	,					__UpperCamelCase				)

        # Put the chunk in its pre-allocated space
        if isinstance(__UpperCamelCase	,					__UpperCamelCase				):

            def assign(__UpperCamelCase      :						dict	,					__UpperCamelCase      :						dict				) -> None:
                for k, v in da.items():
                    if isinstance(__UpperCamelCase	,					__UpperCamelCase				):
                        assign(__UpperCamelCase	,					da[k]				)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            UpperCAmelCase_										=					da[k]

            assign(__UpperCamelCase	,					__UpperCamelCase				)
        elif isinstance(__UpperCamelCase	,					__UpperCamelCase				):
            for xa, xa in zip(__UpperCamelCase	,					__UpperCamelCase				):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    UpperCAmelCase_										=					xa
        elif isinstance(__UpperCamelCase	,					torch.Tensor				):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                UpperCAmelCase_										=					output_chunk
        else:
            raise ValueError('''Not supported'''				)

        i += chunk_size

    UpperCAmelCase_										=					tensor_tree_map(lambda __UpperCamelCase				: t.view(orig_batch_dims + t.shape[1:]				)	,					__UpperCamelCase				)

    return out



class       a					:

     '''simple docstring'''





     def __init__(     self       :      List[Any] ,   __snake_case       :      int = 5_12 ,   ):
         UpperCAmelCase_										=					max_chunk_size
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Callable ,   __snake_case       :      tuple ,   __snake_case       :      int    ):
         logging.info('''Tuning chunk size...'''    )

         if min_chunk_size >= self.max_chunk_size:
             return min_chunk_size

         UpperCAmelCase_										=					[2**l for l in range(int(math.log(self.max_chunk_size ,   2    )    ) + 1    )]
         UpperCAmelCase_										=					[c for c in candidates if c > min_chunk_size]
         UpperCAmelCase_										=					[min_chunk_size] + candidates
         candidates[-1] += 4

         def test_chunk_size(__snake_case       :      int    ) -> bool:
             try:
                 with torch.no_grad():
                     fn(*__snake_case ,   chunk_size=__snake_case    )
                 return True
             except RuntimeError:
                 return False

         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					len(__snake_case    ) - 1
         while i > min_viable_chunk_size_index:
             UpperCAmelCase_										=					test_chunk_size(candidates[i]    )
             if not viable:
                 UpperCAmelCase_										=					(min_viable_chunk_size_index + i) // 2
             else:
                 UpperCAmelCase_										=					i
                 UpperCAmelCase_										=					(i + len(__snake_case    ) - 1) // 2

         return candidates[min_viable_chunk_size_index]
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      Iterable ,   __snake_case       :      Iterable    ):
         UpperCAmelCase_										=					True
         for aa, aa in zip(__snake_case ,   __snake_case    ):
             assert type(__snake_case    ) == type(__snake_case    )
             if isinstance(__snake_case ,   (list, tuple)    ):
                 consistent &= self._compare_arg_caches(__snake_case ,   __snake_case    )
             elif isinstance(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					[v for _, v in sorted(aa.items() ,   key=lambda __snake_case    : x[0]    )]
                 UpperCAmelCase_										=					[v for _, v in sorted(aa.items() ,   key=lambda __snake_case    : x[0]    )]
                 consistent &= self._compare_arg_caches(__snake_case ,   __snake_case    )
             else:
                 consistent &= aa == aa

         return consistent




     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Callable ,   __snake_case       :      tuple ,   __snake_case       :      int ,   ):
         UpperCAmelCase_										=					True
         UpperCAmelCase_										=					tree_map(lambda __snake_case    : a.shape if isinstance(__snake_case ,   torch.Tensor    ) else a ,   __snake_case ,   __snake_case    )
         if self.cached_arg_data is not None:
             # If args have changed shape/value, we need to re-tune
             assert len(self.cached_arg_data    ) == len(__snake_case    )
             UpperCAmelCase_										=					self._compare_arg_caches(self.cached_arg_data ,   __snake_case    )
         else:
             # Otherwise, we can reuse the precomputed value
             UpperCAmelCase_										=					False

         if not consistent:
             UpperCAmelCase_										=					self._determine_favorable_chunk_size(
                 __snake_case ,   __snake_case ,   __snake_case ,   )
             UpperCAmelCase_										=					arg_data

         assert self.cached_chunk_size is not None

         return self.cached_chunk_size





