





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       :
			lowerCamelCase_     :     Tuple       			=      XGLMConfig
			lowerCamelCase_     :     List[str]       			=      {}
			lowerCamelCase_     :     Union[str, Any]       			=      '''gelu'''






			def __init__(self					,							__magic_name__					,							__magic_name__=14					,							__magic_name__=7					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=99					,							__magic_name__=32					,							__magic_name__=2					,							__magic_name__=4					,							__magic_name__=37					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=0.02					,							)     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    str				=  parent
							snake_case_						:    int				=  batch_size
							snake_case_						:    List[str]				=  seq_length
							snake_case_						:    int				=  is_training
							snake_case_						:    int				=  use_input_mask
							snake_case_						:    Tuple				=  use_labels
							snake_case_						:    Any				=  vocab_size
							snake_case_						:    Dict				=  d_model
							snake_case_						:    Optional[int]				=  num_hidden_layers
							snake_case_						:    Tuple				=  num_attention_heads
							snake_case_						:    Dict				=  ffn_dim
							snake_case_						:    Optional[int]				=  activation_function
							snake_case_						:    str				=  activation_dropout
							snake_case_						:    int				=  attention_dropout
							snake_case_						:    Optional[Any]				=  max_position_embeddings
							snake_case_						:    Optional[int]				=  initializer_range
							snake_case_						:    str				=  None
							snake_case_						:    List[Any]				=  0
							snake_case_						:    str				=  2
							snake_case_						:    Union[str, Any]				=  1






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


							'''simple docstring'''



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






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  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_						:    str				=  None
							if self.use_input_mask:
											snake_case_						:    Tuple				=  random_attention_mask([self.batch_size, self.seq_length]    )

							snake_case_						:    Dict				=  self.get_config()

							snake_case_						:    Optional[Any]				=  floats_tensor([self.num_hidden_layers, self.num_attention_heads]					,							2    )

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)






			def 			lowerCamelCase  (self    )     ->   int:


							'''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=UpperCAmelCase_					,							bos_token_id=self.bos_token_id					,							eos_token_id=self.eos_token_id					,							pad_token_id=self.pad_token_id					,							return_dict=UpperCAmelCase_					,							)






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    str				=  self.prepare_config_and_inputs()

							(
							    snake_case_					
							)						:    Union[str, Any]				=  config_and_inputs

							snake_case_						:    str				=  {
							    "input_ids": input_ids,
							    "head_mask": head_mask,
							}

							return config, inputs_dict




@require_tf
class 					__lowerCAmelCase       (			__SCREAMING_SNAKE_CASE,     __SCREAMING_SNAKE_CASE,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[int]       			=      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			lowerCamelCase_     :     str       			=      (TFXGLMForCausalLM,) if is_tf_available() else ()
			lowerCamelCase_     :     Any       			=      (
			    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
			)
			lowerCamelCase_     :     List[Any]       			=      False
			lowerCamelCase_     :     int       			=      False
			lowerCamelCase_     :     str       			=      False






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  TFXGLMModelTester(self    )
							snake_case_						:    str				=  ConfigTester(self					,							config_class=UpperCAmelCase_					,							n_embd=37    )






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			@slow
			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_						:    Dict				=  TFXGLMModel.from_pretrained(UpperCAmelCase_    )
											self.assertIsNotNone(UpperCAmelCase_    )






			@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''    )
			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							super().test_resize_token_embeddings()




@require_tf
class 					__lowerCAmelCase       (			unittest.TestCase     ):






			@slow
			def 			lowerCamelCase  (self					,							__magic_name__=True    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
							snake_case_						:    Optional[int]				=  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
							snake_case_						:    Union[str, Any]				=  [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
							# fmt: on
							snake_case_						:    List[str]				=  model.generate(UpperCAmelCase_					,							do_sample=UpperCAmelCase_					,							num_beams=1    )
							if verify_outputs:
											self.assertListEqual(output_ids[0].numpy().tolist()					,							UpperCAmelCase_    )






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


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''    )
							snake_case_						:    Tuple				=  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )

							tf.random.set_seed(0    )
							snake_case_						:    Union[str, Any]				=  tokenizer('''Today is a nice day and'''					,							return_tensors='''tf'''    )
							snake_case_						:    Dict				=  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_						:    str				=  model.generate(UpperCAmelCase_					,							do_sample=UpperCAmelCase_					,							seed=[7, 0]    )
							snake_case_						:    str				=  tokenizer.decode(output_ids[0]					,							skip_special_tokens=UpperCAmelCase_    )

							snake_case_						:    Union[str, Any]				=  (
							    "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
							)
							self.assertEqual(UpperCAmelCase_					,							UpperCAmelCase_    )






			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
							snake_case_						:    Tuple				=  XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''    )

							snake_case_						:    str				=  "left"

							# use different length sentences to test batching
							snake_case_						:    List[Any]				=  [
							    "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_						:    Optional[Any]				=  tokenizer(UpperCAmelCase_					,							return_tensors='''tf'''					,							padding=UpperCAmelCase_    )
							snake_case_						:    Optional[int]				=  inputs["input_ids"]

							snake_case_						:    str				=  model.generate(input_ids=UpperCAmelCase_					,							attention_mask=inputs['''attention_mask''']					,							max_new_tokens=12    )

							snake_case_						:    Tuple				=  tokenizer(sentences[0]					,							return_tensors='''tf'''    ).input_ids
							snake_case_						:    Union[str, Any]				=  model.generate(input_ids=UpperCAmelCase_					,							max_new_tokens=12    )

							snake_case_						:    Optional[Any]				=  tokenizer(sentences[1]					,							return_tensors='''tf'''    ).input_ids
							snake_case_						:    Union[str, Any]				=  model.generate(input_ids=UpperCAmelCase_					,							max_new_tokens=12    )

							snake_case_						:    Optional[Any]				=  tokenizer.batch_decode(UpperCAmelCase_					,							skip_special_tokens=UpperCAmelCase_    )
							snake_case_						:    Any				=  tokenizer.decode(output_non_padded[0]					,							skip_special_tokens=UpperCAmelCase_    )
							snake_case_						:    Union[str, Any]				=  tokenizer.decode(output_padded[0]					,							skip_special_tokens=UpperCAmelCase_    )

							snake_case_						:    int				=  [
							    "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(UpperCAmelCase_					,							UpperCAmelCase_    )
							self.assertListEqual(UpperCAmelCase_					,							[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_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

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

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_       , tokenizer_file=UpperCAmelCase_       , do_lower_case=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , tokenize_chinese_chars=UpperCAmelCase_       , strip_accents=UpperCAmelCase_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =[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 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      Optional[int]			  =[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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

