
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 					lowerCamelCase		:
     UpperCAmelCase__    :							Dict									=		XGLMConfig
     UpperCAmelCase__    :							Dict									=		{}
     UpperCAmelCase__    :							str									=		'''gelu'''





     def __init__(self				:   Optional[int]    ,   _A				:   Any    ,   _A				:   Dict=1_4    ,   _A				:   Optional[int]=7    ,   _A				:   Dict=True    ,   _A				:   int=True    ,   _A				:   List[Any]=True    ,   _A				:   int=9_9    ,   _A				:   Optional[Any]=3_2    ,   _A				:   Any=2    ,   _A				:   Any=4    ,   _A				:   List[Any]=3_7    ,   _A				:   List[str]="gelu"    ,   _A				:   Any=0.1    ,   _A				:   Optional[int]=0.1    ,   _A				:   Tuple=5_1_2    ,   _A				:   str=0.02    ,   )						->						Union[str, Any]:
         snake_case								= parent
         snake_case								= batch_size
         snake_case								= seq_length
         snake_case								= is_training
         snake_case								= use_input_mask
         snake_case								= use_labels
         snake_case								= vocab_size
         snake_case								= d_model
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= ffn_dim
         snake_case								= activation_function
         snake_case								= activation_dropout
         snake_case								= attention_dropout
         snake_case								= max_position_embeddings
         snake_case								= initializer_range
         snake_case								= None
         snake_case								= 0
         snake_case								= 2
         snake_case								= 1





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         return XGLMConfig.from_pretrained("facebook/xglm-564M"       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Dict:
         snake_case								= tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size       )    ,   clip_value_min=0    ,   clip_value_max=3       )

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

         snake_case								= self.get_config()

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

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Tuple:
         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 		UpperCAmelCase(self				:   Dict       )						->						Tuple:
         snake_case								= self.prepare_config_and_inputs()

         (
             (
             snake_case				
         )					,							(
             snake_case				
         )					,							(
             snake_case				
         )					,							(
             snake_case				
         )					,											
         )								= config_and_inputs

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

         return config, inputs_dict







@require_tf
class 					lowerCamelCase		(  A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Optional[int]									=		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     UpperCAmelCase__    :							Optional[Any]									=		(TFXGLMForCausalLM,) if is_tf_available() else ()
     UpperCAmelCase__    :							str									=		(
         {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
     )
     UpperCAmelCase__    :							str									=		False
     UpperCAmelCase__    :							str									=		False
     UpperCAmelCase__    :							Dict									=		False





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Optional[int]:
         snake_case								= TFXGLMModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_a    ,   n_embd=3_7       )





     def 		UpperCAmelCase(self				:   Any       )						->						Union[str, Any]:
         self.config_tester.run_common_tests()





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						List[str]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case								= TFXGLMModel.from_pretrained(_a       )
             self.assertIsNotNone(_a       )





     @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."       )
     def 		UpperCAmelCase(self				:   int       )						->						int:
         super().test_resize_token_embeddings()







@require_tf
class 					lowerCamelCase		(  unittest.TestCase  ):





     @slow
     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   str=True       )						->						Optional[int]:
         snake_case								= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
         snake_case								= tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]]    ,   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
         snake_case								= [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
         # fmt: on
         snake_case								= model.generate(_a    ,   do_sample=_a    ,   num_beams=1       )
         if verify_outputs:
             self.assertListEqual(output_ids[0].numpy().tolist()    ,   _a       )





     @slow
     def 		UpperCAmelCase(self				:   Dict       )						->						str:
         snake_case								= XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )
         snake_case								= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )

         tf.random.set_seed(0       )
         snake_case								= tokenizer("Today is a nice day and"    ,   return_tensors="tf"       )
         snake_case								= 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"       ):
             snake_case								= model.generate(_a    ,   do_sample=_a    ,   seed=[7, 0]       )
         snake_case								= tokenizer.decode(output_ids[0]    ,   skip_special_tokens=_a       )

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





     @slow
     def 		UpperCAmelCase(self				:   List[Any]       )						->						Tuple:
         snake_case								= TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"       )
         snake_case								= XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

         snake_case								= "left"

         # use different length sentences to test batching
         snake_case								= [
             "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",
         ]

         snake_case								= tokenizer(_a    ,   return_tensors="tf"    ,   padding=_a       )
         snake_case								= inputs["input_ids"]

         snake_case								= model.generate(input_ids=_a    ,   attention_mask=inputs["attention_mask"]    ,   max_new_tokens=1_2       )

         snake_case								= tokenizer(sentences[0]    ,   return_tensors="tf"       ).input_ids
         snake_case								= model.generate(input_ids=_a    ,   max_new_tokens=1_2       )

         snake_case								= tokenizer(sentences[1]    ,   return_tensors="tf"       ).input_ids
         snake_case								= model.generate(input_ids=_a    ,   max_new_tokens=1_2       )

         snake_case								= tokenizer.batch_decode(_a    ,   skip_special_tokens=_a       )
         snake_case								= tokenizer.decode(output_non_padded[0]    ,   skip_special_tokens=_a       )
         snake_case								= tokenizer.decode(output_padded[0]    ,   skip_special_tokens=_a       )

         snake_case								= [
             "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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

