import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class 							a_					(		lowercase__			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   BertweetTokenizer
 UpperCAmelCase_      					=   False




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowerCAmelCase__	      =   ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
        lowerCAmelCase__	      =   dict(zip(_lowercase     ,       range(len(_lowercase))))
        lowerCAmelCase__	      =   ['#version: 0.2', 'a m</w>']
        lowerCAmelCase__	      =   {'unk_token': '<unk>'}

        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__	      =   os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['merges_file'])
        with open(self.vocab_file     ,       'w'     ,       encoding='utf-8') as fp:
               for token in vocab_tokens:
                      fp.write(F"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file     ,       'w'     ,       encoding='utf-8') as fp:
               fp.write('\n'.join(_lowercase))




 def 						__snake_case		(     self   :    int     ,       **lowercase__   :    List[Any]):







        '''simple docstring'''



        kwargs.update(self.special_tokens_map)
        return BertweetTokenizer.from_pretrained(self.tmpdirname     ,       **_lowercase)




 def 						__snake_case		(     self   :    Any     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'I am VinAI Research'
        lowerCAmelCase__	      =   'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
        return input_text, output_text




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   BertweetTokenizer(self.vocab_file     ,       self.merges_file     ,       **self.special_tokens_map)
        lowerCAmelCase__	      =   'I am VinAI Research'
        lowerCAmelCase__	      =   'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
        lowerCAmelCase__	      =   tokenizer.tokenize(_lowercase)
        self.assertListEqual(_lowercase     ,       _lowercase)

        lowerCAmelCase__	      =   tokens + [tokenizer.unk_token]

        lowerCAmelCase__	      =   [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)     ,       _lowercase)

"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )
class   UpperCAmelCase_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       parent
       _lowercase							     =       1_3
       _lowercase							     =       7
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       9_9
       _lowercase							     =       3_2
       _lowercase							     =       2
       _lowercase							     =       4
       _lowercase							     =       3_7
       _lowercase							     =       "gelu"
       _lowercase							     =       0.1
       _lowercase							     =       0.1
       _lowercase							     =       5_1_2
       _lowercase							     =       1_6
       _lowercase							     =       2
       _lowercase							     =       0.02
       _lowercase							     =       3
       _lowercase							     =       4
       _lowercase							     =       None
       _lowercase							     =       self.vocab_size - 1
    def 		_lowerCamelCase      (	self			:       int		)						->    List[Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

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

       _lowercase							     =       None
       if self.use_token_type_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

       _lowercase							     =       None
       if self.use_mc_token_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.num_choices]   ,						self.seq_length		)

       _lowercase							     =       None
       _lowercase							     =       None
       _lowercase							     =       None
       if self.use_labels:
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.num_choices		)

       _lowercase							     =       OpenAIGPTConfig(
           vocab_size=self.vocab_size   ,						n_embd=self.hidden_size   ,						n_layer=self.num_hidden_layers   ,						n_head=self.num_attention_heads   ,						n_positions=self.max_position_embeddings   ,						pad_token_id=self.pad_token_id   ,						)

       _lowercase							     =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2		)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]   ,						_lowercase			:       int   ,						*_lowercase			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       [input_ids, input_mask]
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       model(_lowercase		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						*_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       TFOpenAIGPTLMHeadModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTDoubleHeadsModel(config=_lowercase		)

       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)

       _lowercase							     =       {
           "input_ids": multiple_choice_inputs_ids,
           "mc_token_ids": mc_token_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(
           result.logits.shape   ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.mc_logits.shape   ,						(self.batch_size, self.num_choices)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       str		)						->    str:
       _lowercase							     =       self.num_labels
       _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
       _lowercase							     =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       _lowercase							     =       TFOpenAIGPTForSequenceClassification(_lowercase		)
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       self.prepare_config_and_inputs()

       (
           (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  
       )							     =       config_and_inputs

       _lowercase							     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
@require_tf
class   UpperCAmelCase_	(				lowercase__,    lowercase__,    unittest.TestCase   ):
    snake_case_											=      (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    snake_case_											=      (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    snake_case_											=      (
        {
            """feature-extraction""": TFOpenAIGPTModel,
            """text-classification""": TFOpenAIGPTForSequenceClassification,
            """text-generation""": TFOpenAIGPTLMHeadModel,
            """zero-shot""": TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any		)						->    List[str]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
          # tiny config could not be created.
          return True

       return False
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       TFOpenAIGPTModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						n_embd=3_7		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase		)




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       TFOpenAIGPTModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)
@require_tf
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @slow
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Union[str, Any]:
       _lowercase							     =       TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
       _lowercase							     =       tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]   ,						dtype=tf.intaa		)  # the president is
       _lowercase							     =       [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       _lowercase							     =       model.generate(_lowercase   ,						do_sample=_lowercase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,						_lowercase		)