




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.xglm.modeling_tf_xglm import (
       TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXGLMForCausalLM,
       TFXGLMModel,
   )




@require_tf
class       a__	:
     a		:      Any		 =     XGLMConfig
     a		:      Tuple		 =     {}
     a		:      Optional[int]		 =     """gelu"""







     def __init__(     self  ,		A  ,		A=14  ,		A=7  ,		A=True  ,		A=True  ,		A=True  ,		A=99  ,		A=32  ,		A=2  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=512  ,		A=0.0_2  ,		)  ->   Tuple:



          '''simple docstring'''



          a     =				parent
          a     =				batch_size
          a     =				seq_length
          a     =				is_training
          a     =				use_input_mask
          a     =				use_labels
          a     =				vocab_size
          a     =				d_model
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				ffn_dim
          a     =				activation_function
          a     =				activation_dropout
          a     =				attention_dropout
          a     =				max_position_embeddings
          a     =				initializer_range
          a     =				None
          a     =				0
          a     =				2
          a     =				1







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          return XGLMConfig.from_pretrained("facebook/xglm-564M"			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tf.clip_by_value(
              ids_tensor([self.batch_size, self.seq_length]  ,		self.vocab_size			)  ,		clip_value_min=0  ,		clip_value_max=3			)

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

          a     =				self.get_config()

          a     =				floats_tensor([self.num_hidden_layers, self.num_attention_heads]  ,		2			)

          return (
              config,
              input_ids,
              input_mask,
              head_mask,
          )







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return 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=A  ,		bos_token_id=self.bos_token_id  ,		eos_token_id=self.eos_token_id  ,		pad_token_id=self.pad_token_id  ,		return_dict=A  ,		)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()

          (
              (
              a
          )  ,				(
              a
          )  ,				(
              a
          )  ,				(
              a
          )  ,				
          )     =				config_and_inputs

          a     =				{
              "input_ids": input_ids,
              "head_mask": head_mask,
          }

          return config, inputs_dict







@require_tf
class       a__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Union[str, Any]		 =     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     a		:      int		 =     (TFXGLMForCausalLM,) if is_tf_available() else ()
     a		:      int		 =     (
         {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     a		:      Dict		 =     False
     a		:      Any		 =     False
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				TFXGLMModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		n_embd=37			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a     =				TFXGLMModel.from_pretrained(A			)
               self.assertIsNotNone(A			)







     @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."			)
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          super().test_resize_token_embeddings()







@require_tf
class       a__	( unittest.TestCase				):







     @slow
     def       lowerCAmelCase_    (     self  ,		A=True			)  ->   List[Any]:



          '''simple docstring'''



          a     =				TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)
          a     =				tf.convert_to_tensor([[2, 268, 9865]]  ,		dtype=tf.intaa			)  # The dog
          # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
          # fmt: off
          a     =				[2, 268, 9865, 67, 11, 1988, 57252, 9865, 5, 984, 67, 1988, 213838, 1658, 53, 70446, 33, 6657, 278, 1581]
          # fmt: on
          a     =				model.generate(A  ,		do_sample=A  ,		num_beams=1			)
          if verify_outputs:
               self.assertListEqual(output_ids[0].numpy().tolist()  ,		A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				XGLMTokenizer.from_pretrained("facebook/xglm-564M"			)
          a     =				TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)

          tf.random.set_seed(0			)
          a     =				tokenizer("Today is a nice day and"  ,		return_tensors="tf"			)
          a     =				tokenized.input_ids
          # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
          with tf.device(":/CPU:0"			):
               a     =				model.generate(A  ,		do_sample=A  ,		seed=[7, 0]			)
          a     =				tokenizer.decode(output_ids[0]  ,		skip_special_tokens=A			)

          a     =				(
              "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
          )
          self.assertEqual(A  ,		A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)
          a     =				XGLMTokenizer.from_pretrained("facebook/xglm-564M"			)

          a     =				"left"

          # use different length sentences to test batching
          a     =				[
              "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
              "left-padding, such as in batched generation. The output for the sequence below should be the same "
              "regardless of whether left padding is applied or not. When",
              "Hello, my dog is a little",
          ]

          a     =				tokenizer(A  ,		return_tensors="tf"  ,		padding=A			)
          a     =				inputs["input_ids"]

          a     =				model.generate(input_ids=A  ,		attention_mask=inputs["attention_mask"]  ,		max_new_tokens=12			)

          a     =				tokenizer(sentences[0]  ,		return_tensors="tf"			).input_ids
          a     =				model.generate(input_ids=A  ,		max_new_tokens=12			)

          a     =				tokenizer(sentences[1]  ,		return_tensors="tf"			).input_ids
          a     =				model.generate(input_ids=A  ,		max_new_tokens=12			)

          a     =				tokenizer.batch_decode(A  ,		skip_special_tokens=A			)
          a     =				tokenizer.decode(output_non_padded[0]  ,		skip_special_tokens=A			)
          a     =				tokenizer.decode(output_padded[0]  ,		skip_special_tokens=A			)

          a     =				[
              "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
              "left-padding, such as in batched generation. The output for the sequence below should be the same "
              "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
              "a single",
              "Hello, my dog is a little bit of a shy one, but he is very friendly",
          ]
          self.assertListEqual(A  ,		A			)
          self.assertListEqual(A  ,		[non_padded_sentence, padded_sentence]			)







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowercase__				:      int	   = logging.get_logger(__name__)


lowercase__				:      Dict	   = "▁"

lowercase__				:      Union[str, Any]	   = {"vocab_file": "spiece.model"}

lowercase__				:      Union[str, Any]	   = {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowercase__				:      Tuple	   = {
    "google/reformer-crime-and-punishment": 524_288,
}




class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     VOCAB_FILES_NAMES
     a		:      List[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      Dict		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="</s>"  ,		A="<unk>"  ,		A=[]  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A  ,		unk_token=A  ,		additional_special_tokens=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**A  ,		)

          a     =				vocab_file
          a     =				spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.sp_model.get_piece_size()







     def       lowerCAmelCase_    (     self			)  ->   Dict[str, int]:



          '''simple docstring'''



          a     =				{self.convert_ids_to_tokens(A			): i for i in range(self.vocab_size			)}
          vocab.update(self.added_tokens_encoder			)
          return vocab







     def __getstate__(     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          return state







     def __setstate__(     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				d

          # for backward compatibility
          if not hasattr(self  ,		"sp_model_kwargs"			):
               a     =				{}

          a     =				spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(self.vocab_file			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          return self.sp_model.encode(A  ,		out_type=A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.piece_to_id(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          if index < self.sp_model.get_piece_size():
               a     =				self.sp_model.IdToPiece(A			)
          return token







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          a     =				""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(A			) + token
                    a     =				[]
               else:
                    current_sub_tokens.append(A			)
          out_string += self.sp_model.decode(A			)
          return out_string.strip()







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[str]:



          '''simple docstring'''



          if not os.path.isdir(A			):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
          a     =				os.path.join(
              A  ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

          if os.path.abspath(self.vocab_file			) != os.path.abspath(A			) and os.path.isfile(self.vocab_file			):
               copyfile(self.vocab_file  ,		A			)
          elif not os.path.isfile(self.vocab_file			):
               with open(A  ,		"wb"			) as fi:
                    a     =				self.sp_model.serialized_model_proto()
                    fi.write(A			)

          return (out_vocab_file,)

