



"""simple docstring"""







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class       UpperCamelCase__   (	_UpperCAmelCase,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     MvpTokenizer
      _SCREAMING_SNAKE_CASE  				=     MvpTokenizerFast
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     filter_roberta_detectors



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        super().setUp()
        lowerCAmelCase_   :				Any								= [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        lowerCAmelCase_   :				Tuple								= dict(zip(SCREAMING_SNAKE_CASE_    ,			range(len(SCREAMING_SNAKE_CASE_       )       )       )       )
        lowerCAmelCase_   :				List[str]								= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        lowerCAmelCase_   :				str								= {'unk_token': '<unk>'}

        lowerCAmelCase_   :				Tuple								= os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']       )
        lowerCAmelCase_   :				List[Any]								= os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file']       )
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8'       ) as fp:
          fp.write(json.dumps(SCREAMING_SNAKE_CASE_       ) + '\n'       )
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'       ) as fp:
          fp.write('\n'.join(SCREAMING_SNAKE_CASE_       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			**SCREAMING_SNAKE_CASE_				:      str       ):
        kwargs.update(self.special_tokens_map       )
        return self.tokenizer_class.from_pretrained(self.tmpdirname    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        kwargs.update(self.special_tokens_map       )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Dict       ):
        return "lower newer", "lower newer"



      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        return MvpTokenizer.from_pretrained('RUCAIBox/mvp'       )



      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Union[str, Any]								= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        lowerCAmelCase_   :				Any								= [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase_   :				Any								= tokenizer(SCREAMING_SNAKE_CASE_    ,			max_length=len(SCREAMING_SNAKE_CASE_       )    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          self.assertEqual((2, 9)    ,			batch.input_ids.shape       )
          self.assertEqual((2, 9)    ,			batch.attention_mask.shape       )
          lowerCAmelCase_   :				Union[str, Any]								= batch.input_ids.tolist()[0]
          self.assertListEqual(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          # Test that special tokens are reset



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Tuple								= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase_   :				Any								= tokenizer(SCREAMING_SNAKE_CASE_    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )
          # check if input_ids are returned and no labels
          self.assertIn('input_ids'    ,			SCREAMING_SNAKE_CASE_       )
          self.assertIn('attention_mask'    ,			SCREAMING_SNAKE_CASE_       )
          self.assertNotIn('labels'    ,			SCREAMING_SNAKE_CASE_       )
          self.assertNotIn('decoder_attention_mask'    ,			SCREAMING_SNAKE_CASE_       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Any								= [
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase_   :				str								= tokenizer(text_target=SCREAMING_SNAKE_CASE_    ,			max_length=3_2    ,			padding='max_length'    ,			return_tensors='pt'       )
          self.assertEqual(3_2    ,			targets['input_ids'].shape[1]       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase_   :				str								= tokenizer(
              ['I am a small frog' * 1_0_2_4, 'I am a small frog']    ,			padding=SCREAMING_SNAKE_CASE_    ,			truncation=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )
          self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          self.assertEqual(batch.input_ids.shape    ,			(2, 1_0_2_4)       )



      @require_torch
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Any								= ['A long paragraph for summarization.']
        lowerCAmelCase_   :				List[str]								= [
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          lowerCAmelCase_   :				Optional[Any]								= tokenizer(SCREAMING_SNAKE_CASE_    ,			text_target=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )
          lowerCAmelCase_   :				Dict								= inputs['input_ids']
          lowerCAmelCase_   :				Dict								= inputs['labels']
          self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
          self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
          self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
          self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        pass





      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
            lowerCAmelCase_   :				Optional[Any]								= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				Optional[Any]								= self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				List[Any]								= 'A, <mask> AllenNLP sentence.'
            lowerCAmelCase_   :				Dict								= tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_    ,			return_token_type_ids=SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				str								= tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_    ,			return_token_type_ids=SCREAMING_SNAKE_CASE_       )

            # token_type_ids should put 0 everywhere
            self.assertEqual(sum(tokens_r['token_type_ids']       )    ,			sum(tokens_p['token_type_ids']       )       )

            # attention_mask should put 1 everywhere, so sum over length should be 1
            self.assertEqual(
                sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )    ,			sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )    ,			)

            lowerCAmelCase_   :				Tuple								= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
            lowerCAmelCase_   :				Optional[int]								= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

            # Rust correctly handles the space before the mask while python doesnt
            self.assertSequenceEqual(tokens_p['input_ids']    ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
            self.assertSequenceEqual(tokens_r['input_ids']    ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

            self.assertSequenceEqual(
                SCREAMING_SNAKE_CASE_    ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
            self.assertSequenceEqual(
                SCREAMING_SNAKE_CASE_    ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






