




import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase    : Dict		= logging.get_logger(__name__)

_lowerCAmelCase    : int		= {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase    : Optional[int]		= {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

_lowerCAmelCase    : Optional[int]		= {'stas/tiny-wmt19-en-de': 1_0_2_4}
_lowerCAmelCase    : List[Any]		= {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1_0_2_4,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}



def     lowerCAmelCase		(		_lowerCAmelCase				: Dict						):




 """simple docstring"""







 UpperCAmelCase__							=    set()
 UpperCAmelCase__							=    word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)						)
  UpperCAmelCase__							=    char
 return pairs



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[Any]						):




 """simple docstring"""







 UpperCAmelCase__							=    text.replace("，"    ,				","						)
 UpperCAmelCase__							=    re.sub(R"。\s*"    ,				". "    ,				_lowerCAmelCase						)
 UpperCAmelCase__							=    text.replace("、"    ,				","						)
 UpperCAmelCase__							=    text.replace("”"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("“"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("∶"    ,				":"						)
 UpperCAmelCase__							=    text.replace("："    ,				":"						)
 UpperCAmelCase__							=    text.replace("？"    ,				"?"						)
 UpperCAmelCase__							=    text.replace("《"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("》"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("）"    ,				")"						)
 UpperCAmelCase__							=    text.replace("！"    ,				"!"						)
 UpperCAmelCase__							=    text.replace("（"    ,				"("						)
 UpperCAmelCase__							=    text.replace("；"    ,				";"						)
 UpperCAmelCase__							=    text.replace("１"    ,				"1"						)
 UpperCAmelCase__							=    text.replace("」"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("「"    ,				"\""						)
 UpperCAmelCase__							=    text.replace("０"    ,				"0"						)
 UpperCAmelCase__							=    text.replace("３"    ,				"3"						)
 UpperCAmelCase__							=    text.replace("２"    ,				"2"						)
 UpperCAmelCase__							=    text.replace("５"    ,				"5"						)
 UpperCAmelCase__							=    text.replace("６"    ,				"6"						)
 UpperCAmelCase__							=    text.replace("９"    ,				"9"						)
 UpperCAmelCase__							=    text.replace("７"    ,				"7"						)
 UpperCAmelCase__							=    text.replace("８"    ,				"8"						)
 UpperCAmelCase__							=    text.replace("４"    ,				"4"						)
 UpperCAmelCase__							=    re.sub(R"．\s*"    ,				". "    ,				_lowerCAmelCase						)
 UpperCAmelCase__							=    text.replace("～"    ,				"~"						)
 UpperCAmelCase__							=    text.replace("’"    ,				"'"						)
 UpperCAmelCase__							=    text.replace("…"    ,				"..."						)
 UpperCAmelCase__							=    text.replace("━"    ,				"-"						)
 UpperCAmelCase__							=    text.replace("〈"    ,				"<"						)
 UpperCAmelCase__							=    text.replace("〉"    ,				">"						)
 UpperCAmelCase__							=    text.replace("【"    ,				"["						)
 UpperCAmelCase__							=    text.replace("】"    ,				"]"						)
 UpperCAmelCase__							=    text.replace("％"    ,				"%"						)
 return text



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[int]						):




 """simple docstring"""







 UpperCAmelCase__							=    []
 for char in text:
  UpperCAmelCase__							=    unicodedata.category(_lowerCAmelCase						)
  if cat.startswith("C"						):
   continue
  output.append(_lowerCAmelCase						)
 return "".join(_lowerCAmelCase						)


class       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		VOCAB_FILES_NAMES
      UpperCAmelCase_          =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_          =		PRETRAINED_INIT_CONFIGURATION
      UpperCAmelCase_          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_          =		['''input_ids''', '''attention_mask''']
      def __init__(      self :Any			,   lowerCamelCase :Any=None			,   lowerCamelCase :Dict=None			,   lowerCamelCase :Optional[int]=None			,   lowerCamelCase :str=None			,   lowerCamelCase :Dict=False			,   lowerCamelCase :Tuple="<unk>"			,   lowerCamelCase :int="<s>"			,   lowerCamelCase :Optional[int]="</s>"			,   lowerCamelCase :Union[str, Any]="<pad>"			,   **lowerCamelCase :List[str]			,   )  ->      Tuple:
       super().__init__(
           langs=_UpperCAmelCase			,   src_vocab_file=_UpperCAmelCase			,   tgt_vocab_file=_UpperCAmelCase			,   merges_file=_UpperCAmelCase			,   do_lower_case=_UpperCAmelCase			,   unk_token=_UpperCAmelCase			,   bos_token=_UpperCAmelCase			,   sep_token=_UpperCAmelCase			,   pad_token=_UpperCAmelCase			,   **_UpperCAmelCase			,   )

       try:
        import sacremoses
       except ImportError:
        raise ImportError(
            "You need to install sacremoses to use XLMTokenizer. "
            "See https://pypi.org/project/sacremoses/ for installation."   )

       UpperCAmelCase__							=    sacremoses

       UpperCAmelCase__							=    src_vocab_file
       UpperCAmelCase__							=    tgt_vocab_file
       UpperCAmelCase__							=    merges_file
       UpperCAmelCase__							=    do_lower_case

       # cache of sm.MosesPunctNormalizer instance
       UpperCAmelCase__							=    {}
       # cache of sm.MosesTokenizer instance
       UpperCAmelCase__							=    {}
       UpperCAmelCase__							=    {}

       if langs and len(_UpperCAmelCase   ) == 2:
        UpperCAmelCase__			,			UpperCAmelCase__							=    langs
       else:
        raise ValueError(
            f'''arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '''
            "Usually that means that tokenizer can't find a mapping for the given model path "
            "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."   )

       with open(_UpperCAmelCase			,   encoding="utf-8"   ) as src_vocab_handle:
        UpperCAmelCase__							=    json.load(_UpperCAmelCase   )
       with open(_UpperCAmelCase			,   encoding="utf-8"   ) as tgt_vocab_handle:
        UpperCAmelCase__							=    json.load(_UpperCAmelCase   )
        UpperCAmelCase__							=    {v: k for k, v in tgt_vocab.items()}
       with open(_UpperCAmelCase			,   encoding="utf-8"   ) as merges_handle:
        UpperCAmelCase__							=    merges_handle.read().split("\n"   )[:-1]
       UpperCAmelCase__							=    [tuple(merge.split()[:2]   ) for merge in merges]
       UpperCAmelCase__							=    dict(zip(_UpperCAmelCase			,   range(len(_UpperCAmelCase   )   )   )   )
       UpperCAmelCase__							=    {}
      def 						UpperCAmelCase_    (      self :Dict   )  ->      Dict[str, int]:
       return self.get_src_vocab()
      @property
      def 						UpperCAmelCase_    (      self :Any   )  ->      int:
       return self.src_vocab_size
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Dict			,   lowerCamelCase :int   )  ->      int:
       if lang not in self.cache_moses_punct_normalizer:
        UpperCAmelCase__							=    self.sm.MosesPunctNormalizer(lang=_UpperCAmelCase   )
        UpperCAmelCase__							=    punct_normalizer
       return self.cache_moses_punct_normalizer[lang].normalize(_UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :List[str]			,   lowerCamelCase :Dict   )  ->      str:
       if lang not in self.cache_moses_tokenizer:
        UpperCAmelCase__							=    self.sm.MosesTokenizer(lang=_UpperCAmelCase   )
        UpperCAmelCase__							=    moses_tokenizer
       return self.cache_moses_tokenizer[lang].tokenize(
           _UpperCAmelCase			,   aggressive_dash_splits=_UpperCAmelCase			,   return_str=_UpperCAmelCase			,   escape=_UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :List[Any]			,   lowerCamelCase :Optional[int]   )  ->      Dict:
       if lang not in self.cache_moses_detokenizer:
        UpperCAmelCase__							=    self.sm.MosesDetokenizer(lang=_UpperCAmelCase   )
        UpperCAmelCase__							=    moses_detokenizer
       return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase   )
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :Optional[int]			,   lowerCamelCase :List[Any]   )  ->      List[str]:
       UpperCAmelCase__							=    replace_unicode_punct(_UpperCAmelCase   )
       UpperCAmelCase__							=    self.moses_punct_norm(_UpperCAmelCase			,   _UpperCAmelCase   )
       UpperCAmelCase__							=    remove_non_printing_char(_UpperCAmelCase   )
       return text
      @property
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      Dict:
       return len(self.encoder   )
      @property
      def 						UpperCAmelCase_    (      self :Any   )  ->      Union[str, Any]:
       return len(self.decoder   )
      def 						UpperCAmelCase_    (      self :List[Any]   )  ->      Optional[Any]:
       return dict(self.encoder			,   **self.added_tokens_encoder   )
      def 						UpperCAmelCase_    (      self :str   )  ->      Union[str, Any]:
       return dict(self.decoder			,   **self.added_tokens_decoder   )
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :List[str]   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    tuple(token[:-1]   ) + (token[-1] + "</w>",)
       if token in self.cache:
        return self.cache[token]
       UpperCAmelCase__							=    get_pairs(_UpperCAmelCase   )

       if not pairs:
        return token + "</w>"

       while True:
        UpperCAmelCase__							=    min(_UpperCAmelCase			,   key=lambda lowerCamelCase   : self.bpe_ranks.get(_UpperCAmelCase			,   float("inf"   )   )   )
        if bigram not in self.bpe_ranks:
         break
        UpperCAmelCase__			,			UpperCAmelCase__							=    bigram
        UpperCAmelCase__							=    []
        UpperCAmelCase__							=    0
        while i < len(_UpperCAmelCase   ):
         try:
          UpperCAmelCase__							=    word.index(_UpperCAmelCase			,   _UpperCAmelCase   )
         except ValueError:
          new_word.extend(word[i:]   )
          break
         else:
          new_word.extend(word[i:j]   )
          UpperCAmelCase__							=    j

         if word[i] == first and i < len(_UpperCAmelCase   ) - 1 and word[i + 1] == second:
          new_word.append(first + second   )
          i += 2
         else:
          new_word.append(word[i]   )
          i += 1
        UpperCAmelCase__							=    tuple(_UpperCAmelCase   )
        UpperCAmelCase__							=    new_word
        if len(_UpperCAmelCase   ) == 1:
         break
        else:
         UpperCAmelCase__							=    get_pairs(_UpperCAmelCase   )
       UpperCAmelCase__							=    " ".join(_UpperCAmelCase   )
       if word == "\n  </w>":
        UpperCAmelCase__							=    "\n</w>"
       UpperCAmelCase__							=    word
       return word
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :str			,   lowerCamelCase :Optional[int]="en"			,   lowerCamelCase :Dict=False   )  ->      str:
       UpperCAmelCase__							=    self.src_lang

       if self.do_lower_case:
        UpperCAmelCase__							=    text.lower()

       if bypass_tokenizer:
        UpperCAmelCase__							=    text.split()
       else:
        UpperCAmelCase__							=    self.moses_pipeline(_UpperCAmelCase			,   lang=_UpperCAmelCase   )
        UpperCAmelCase__							=    self.moses_tokenize(_UpperCAmelCase			,   lang=_UpperCAmelCase   )

       UpperCAmelCase__							=    []
       for token in text:
        if token:
         split_tokens.extend(list(self.bpe(_UpperCAmelCase   ).split(" "   )   )   )

       return split_tokens
      def 						UpperCAmelCase_    (      self :Any			,   lowerCamelCase :str   )  ->      List[str]:
       return self.encoder.get(_UpperCAmelCase			,   self.encoder.get(self.unk_token   )   )
      def 						UpperCAmelCase_    (      self :Optional[int]			,   lowerCamelCase :int   )  ->      Tuple:
       return self.decoder.get(_UpperCAmelCase			,   self.unk_token   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :List[str]   )  ->      List[Any]:
       UpperCAmelCase__							=    [t.replace(" "			,   ""   ).replace("</w>"			,   " "   ) for t in tokens]
       UpperCAmelCase__							=    "".join(_UpperCAmelCase   ).split()
       # detokenize
       UpperCAmelCase__							=    self.moses_detokenize(_UpperCAmelCase			,   self.tgt_lang   )
       return text
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :int = None   )  ->      List[int]:
       UpperCAmelCase__							=    [self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
        return token_ids_a + sep
       return token_ids_a + sep + token_ids_a + sep
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :str = None			,   lowerCamelCase :List[Any] = False   )  ->      List[int]:

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_UpperCAmelCase			,   token_ids_a=_UpperCAmelCase			,   already_has_special_tokens=_UpperCAmelCase   )
       # no bos used in fairseq
       if token_ids_a is not None:
        return ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]
       return ([0] * len(_UpperCAmelCase   )) + [1]
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Union[str, Any]			,   lowerCamelCase :Any = None   )  ->      List[int]:
       UpperCAmelCase__							=    [self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
        return len(token_ids_a + sep   ) * [0]
       return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :Tuple			,   lowerCamelCase :Optional[int] = None   )  ->      Tuple[str]:
       if not os.path.isdir(_UpperCAmelCase   ):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
        return

       UpperCAmelCase__							=    os.path.join(
           _UpperCAmelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]   )
       UpperCAmelCase__							=    os.path.join(
           _UpperCAmelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]   )
       UpperCAmelCase__							=    os.path.join(
           _UpperCAmelCase			,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

       with open(_UpperCAmelCase			,   "w"			,   encoding="utf-8"   ) as f:
        f.write(json.dumps(self.encoder			,   indent=2			,   sort_keys=_UpperCAmelCase			,   ensure_ascii=_UpperCAmelCase   ) + "\n"   )

       with open(_UpperCAmelCase			,   "w"			,   encoding="utf-8"   ) as f:
        UpperCAmelCase__							=    {v: k for k, v in self.decoder.items()}
        f.write(json.dumps(_UpperCAmelCase			,   indent=2			,   sort_keys=_UpperCAmelCase			,   ensure_ascii=_UpperCAmelCase   ) + "\n"   )

       UpperCAmelCase__							=    0
       with open(_UpperCAmelCase			,   "w"			,   encoding="utf-8"   ) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,   key=lambda lowerCamelCase   : kv[1]   ):
         if index != token_index:
          logger.warning(
              f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
              " Please check that the tokenizer is not corrupted!"   )
          UpperCAmelCase__							=    token_index
         writer.write(" ".join(_UpperCAmelCase   ) + "\n"   )
         index += 1

       return src_vocab_file, tgt_vocab_file, merges_file
      def __getstate__(      self :Tuple   )  ->      List[Any]:
       UpperCAmelCase__							=    self.__dict__.copy()
       UpperCAmelCase__							=    None
       return state







      def __setstate__(      self :str			,   lowerCamelCase :List[Any]   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    d

       try:
        import sacremoses
       except ImportError:
        raise ImportError(
            "You need to install sacremoses to use XLMTokenizer. "
            "See https://pypi.org/project/sacremoses/ for installation."   )

       UpperCAmelCase__							=    sacremoses













import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax
   import jax.numpy as jnp
   import numpy as np

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
   import torch


@require_flax
class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=14							, _UpperCAmelCase=7							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=99							, _UpperCAmelCase=32							, _UpperCAmelCase=5							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=512							, _UpperCAmelCase=0.02							, _UpperCAmelCase=None							, )		->				str:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=seq_length
        lowerCamelCase									=is_training
        lowerCamelCase									=use_input_mask
        lowerCamelCase									=use_labels
        lowerCamelCase									=vocab_size
        lowerCamelCase									=d_model
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=ffn_dim
        lowerCamelCase									=activation_function
        lowerCamelCase									=activation_dropout
        lowerCamelCase									=attention_dropout
        lowerCamelCase									=max_position_embeddings
        lowerCamelCase									=initializer_range
        lowerCamelCase									=None
        lowerCamelCase									=0
        lowerCamelCase									=2
        lowerCamelCase									=1





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=np.clip(ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size  )							, 3							, self.vocab_size  )

        lowerCamelCase									=None
        if self.use_input_mask:
           lowerCamelCase									=random_attention_mask([self.batch_size, self.seq_length]  )

        lowerCamelCase									=XGLMConfig(
            vocab_size=self.vocab_size							, d_model=self.hidden_size							, num_layers=self.num_hidden_layers							, attention_heads=self.num_attention_heads							, ffn_dim=self.ffn_dim							, activation_function=self.activation_function							, activation_dropout=self.activation_dropout							, attention_dropout=self.attention_dropout							, max_position_embeddings=self.max_position_embeddings							, initializer_range=self.initializer_range							, use_cache=_UpperCAmelCase							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, )

        return (config, input_ids, input_mask)





     def 						lowerCamelCase							(	self  )		->				Tuple:
        lowerCamelCase									=self.prepare_config_and_inputs()
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=config_and_inputs
        lowerCamelCase									={"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=self.prepare_config_and_inputs()

        lowerCamelCase									=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
        lowerCamelCase									=ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2  )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Optional[Any]:
        lowerCamelCase									=20
        lowerCamelCase									=model_class_name(_UpperCAmelCase  )

        lowerCamelCase									=model.init_cache(input_ids.shape[0]							, _UpperCAmelCase  )
        lowerCamelCase									=jnp.ones((input_ids.shape[0], max_decoder_length)							, dtype="""i4"""  )

        lowerCamelCase									=jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)  )
        lowerCamelCase									=model(
            input_ids[:, :-1]							, attention_mask=_UpperCAmelCase							, past_key_values=_UpperCAmelCase							, position_ids=_UpperCAmelCase							, )

        lowerCamelCase									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							, dtype="""i4"""  )
        lowerCamelCase									=model(
            input_ids[:, -1:]							, attention_mask=_UpperCAmelCase							, past_key_values=outputs_cache.past_key_values							, position_ids=_UpperCAmelCase							, )

        lowerCamelCase									=model(_UpperCAmelCase  )

        lowerCamelCase									=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1E-3							, msg=F'''Max diff is {diff}'''  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				List[Any]:
        lowerCamelCase									=20
        lowerCamelCase									=model_class_name(_UpperCAmelCase  )

        lowerCamelCase									=jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]							, axis=-1							, )

        lowerCamelCase									=model.init_cache(input_ids.shape[0]							, _UpperCAmelCase  )
        lowerCamelCase									=jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1  )[None, :]							, (input_ids.shape[0], input_ids.shape[-1] - 1)  )

        lowerCamelCase									=model(
            input_ids[:, :-1]							, attention_mask=_UpperCAmelCase							, past_key_values=_UpperCAmelCase							, position_ids=_UpperCAmelCase							, )
        lowerCamelCase									=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							, dtype="""i4"""  )
        lowerCamelCase									=model(
            input_ids[:, -1:]							, past_key_values=outputs_cache.past_key_values							, attention_mask=_UpperCAmelCase							, position_ids=_UpperCAmelCase							, )

        lowerCamelCase									=model(_UpperCAmelCase							, attention_mask=_UpperCAmelCase  )
        lowerCamelCase									=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
        self.parent.assertTrue(diff < 1E-3							, msg=F'''Max diff is {diff}'''  )




@require_sentencepiece
@require_flax
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Tuple 	=     (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     __lowerCamelCase       :    Dict 	=     (FlaxXGLMForCausalLM,) if is_flax_available() else ()





     def 						lowerCamelCase							(	self  )		->				List[str]:
        lowerCamelCase									=FlaxXGLMModelTester(self  )





     def 						lowerCamelCase							(	self  )		->				List[str]:
        for model_class_name in self.all_model_classes:
           lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Tuple:
        for model_class_name in self.all_model_classes:
           lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				Tuple:
        lowerCamelCase									=XGLMTokenizer.from_pretrained("""XGLM"""							, padding_side="""left"""  )
        lowerCamelCase									=tokenizer(["""Hello this is a long string""", """Hey"""]							, return_tensors="""np"""							, padding=_UpperCAmelCase							, truncation=_UpperCAmelCase  )

        lowerCamelCase									=FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""  )
        lowerCamelCase									=1
        lowerCamelCase									=False

        lowerCamelCase									=jax.jit(model.generate  )

        lowerCamelCase									=jit_generate(inputs["""input_ids"""]							, attention_mask=inputs["""attention_mask"""]  ).sequences

        lowerCamelCase									=tokenizer.batch_decode(_UpperCAmelCase							, skip_special_tokens=_UpperCAmelCase  )

        lowerCamelCase									=[
            """Hello this is a long string of questions, but I'm not sure if I'm""",
            """Hey, I'm a newbie to the forum and I'""",
        ]

        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )





     @is_pt_flax_cross_test
     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
              # prepare inputs
              lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
              lowerCamelCase									={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

              # load corresponding PyTorch class
              lowerCamelCase									=model_class.__name__[4:]  # Skip the "Flax" at the beginning
              lowerCamelCase									=getattr(_UpperCAmelCase							, _UpperCAmelCase  )

              lowerCamelCase				,       lowerCamelCase									=pt_inputs["""input_ids"""].shape
              lowerCamelCase									=np.random.randint(0							, seq_length - 1							, size=(batch_size,)  )
              for batch_idx, start_index in enumerate(_UpperCAmelCase  ):
                 lowerCamelCase									=0
                 lowerCamelCase									=1
                 lowerCamelCase									=0
                 lowerCamelCase									=1
              lowerCamelCase									=pt_model_class(_UpperCAmelCase  ).eval()
              # Flax models don't use the `use_cache` option and cache is not returned as a default.
              # So we disable `use_cache` here for PyTorch model.
              lowerCamelCase									=False
              lowerCamelCase									=model_class(_UpperCAmelCase							, dtype=jnp.floataa  )
              lowerCamelCase									=convert_pytorch_state_dict_to_flax(pt_model.state_dict()							, _UpperCAmelCase  )
              lowerCamelCase									=fx_state

              with torch.no_grad():
                 lowerCamelCase									=pt_model(**_UpperCAmelCase  ).to_tuple()

              lowerCamelCase									=fx_model(**_UpperCAmelCase  ).to_tuple()
              self.assertEqual(len(_UpperCAmelCase  )							, len(_UpperCAmelCase  )							, """Output lengths differ between Flax and PyTorch"""  )
              for fx_output, pt_output in zip(_UpperCAmelCase							, _UpperCAmelCase  ):
                 self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4E-2  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                 pt_model.save_pretrained(_UpperCAmelCase  )
                 lowerCamelCase									=model_class.from_pretrained(_UpperCAmelCase							, from_pt=_UpperCAmelCase  )

              lowerCamelCase									=fx_model_loaded(**_UpperCAmelCase  ).to_tuple()
              self.assertEqual(
                  len(_UpperCAmelCase  )							, len(_UpperCAmelCase  )							, """Output lengths differ between Flax and PyTorch"""  )
              for fx_output_loaded, pt_output in zip(_UpperCAmelCase							, _UpperCAmelCase  ):
                 self.assert_almost_equals(fx_output_loaded[:, -1]							, pt_output[:, -1].numpy()							, 4E-2  )





     @is_pt_flax_cross_test
     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
              # prepare inputs
              lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase  )
              lowerCamelCase									={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

              # load corresponding PyTorch class
              lowerCamelCase									=model_class.__name__[4:]  # Skip the "Flax" at the beginning
              lowerCamelCase									=getattr(_UpperCAmelCase							, _UpperCAmelCase  )

              lowerCamelCase									=pt_model_class(_UpperCAmelCase  ).eval()
              lowerCamelCase									=False
              lowerCamelCase									=model_class(_UpperCAmelCase							, dtype=jnp.floataa  )

              lowerCamelCase									=load_flax_weights_in_pytorch_model(_UpperCAmelCase							, fx_model.params  )
              lowerCamelCase				,       lowerCamelCase									=pt_inputs["""input_ids"""].shape
              lowerCamelCase									=np.random.randint(0							, seq_length - 1							, size=(batch_size,)  )
              for batch_idx, start_index in enumerate(_UpperCAmelCase  ):
                 lowerCamelCase									=0
                 lowerCamelCase									=1
                 lowerCamelCase									=0
                 lowerCamelCase									=1

              # make sure weights are tied in PyTorch
              pt_model.tie_weights()

              with torch.no_grad():
                 lowerCamelCase									=pt_model(**_UpperCAmelCase  ).to_tuple()

              lowerCamelCase									=fx_model(**_UpperCAmelCase  ).to_tuple()
              self.assertEqual(len(_UpperCAmelCase  )							, len(_UpperCAmelCase  )							, """Output lengths differ between Flax and PyTorch"""  )
              for fx_output, pt_output in zip(_UpperCAmelCase							, _UpperCAmelCase  ):
                 self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4E-2  )

              with tempfile.TemporaryDirectory() as tmpdirname:
                 fx_model.save_pretrained(_UpperCAmelCase  )
                 lowerCamelCase									=pt_model_class.from_pretrained(_UpperCAmelCase							, from_flax=_UpperCAmelCase  )

              with torch.no_grad():
                 lowerCamelCase									=pt_model_loaded(**_UpperCAmelCase  ).to_tuple()

              self.assertEqual(
                  len(_UpperCAmelCase  )							, len(_UpperCAmelCase  )							, """Output lengths differ between Flax and PyTorch"""  )
              for fx_output, pt_output in zip(_UpperCAmelCase							, _UpperCAmelCase  ):
                 self.assert_almost_equals(fx_output[:, -1]							, pt_output[:, -1].numpy()							, 4E-2  )





     @slow
     def 						lowerCamelCase							(	self  )		->				int:
        for model_class_name in self.all_model_classes:
           lowerCamelCase									=model_class_name.from_pretrained("""facebook/xglm-564M"""  )
           lowerCamelCase									=model(np.ones((1, 1)  )  )
           self.assertIsNotNone(_UpperCAmelCase  )