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        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=MvpTokenizer
			UpperCamelCase__    							=MvpTokenizerFast
			UpperCamelCase__    							=True
			UpperCamelCase__    							=filter_roberta_detectors





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										super().setUp()
										_lowerCAmelCase							=							[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_lowerCAmelCase							=							dict(zip(lowercase__  ,  range(len(lowercase__						)						)						)						)
										_lowerCAmelCase							=							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_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:
																	fp.write(json.dumps(lowercase__						) + '\n'						)
										with open(self.merges_file  ,  'w'  ,  encoding='utf-8'						) as fp:
																	fp.write('\n'.join(lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  **lowercase__	:	int						):
										kwargs.update(self.special_tokens_map						)
										return self.tokenizer_class.from_pretrained(self.tmpdirname  ,  **lowercase__						)





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	str						):
										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	:	Union[str, Any]						):
										return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'						)





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase							=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
										_lowerCAmelCase							=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

																	self.assertEqual((2, 9)  ,  batch.input_ids.shape						)
																	self.assertEqual((2, 9)  ,  batch.attention_mask.shape						)
																	_lowerCAmelCase							=							batch.input_ids.tolist()[0]
																	self.assertListEqual(lowercase__  ,  lowercase__						)
																	# Test that special tokens are reset





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





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							[
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_lowerCAmelCase							=							tokenizer(text_target=lowercase__  ,  max_length=32  ,  padding='max_length'  ,  return_tensors='pt'						)
																	self.assertEqual(32  ,  targets['input_ids'].shape[1]						)





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_lowerCAmelCase							=							tokenizer(
																	    ['I am a small frog' * 10_24, 'I am a small frog']  ,  padding=lowercase__  ,  truncation=lowercase__  ,  return_tensors='pt'						)
																	self.assertIsInstance(lowercase__  ,  lowercase__						)
																	self.assertEqual(batch.input_ids.shape  ,  (2, 10_24)						)





			@require_torch
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							['A long paragraph for summarization.']
										_lowerCAmelCase							=							[
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_lowerCAmelCase							=							tokenizer(lowercase__  ,  text_target=lowercase__  ,  return_tensors='pt'						)
																	_lowerCAmelCase							=							inputs['input_ids']
																	_lowerCAmelCase							=							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	:	Tuple						):
										pass





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

																								# 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							=							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']						)
																								_lowerCAmelCase							=							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_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)
																								self.assertSequenceEqual(tokens_r['input_ids']  ,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]						)

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







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers import TFBlipTextModel
				from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST



class        lowerCamelCase__				:





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[str]=12  ,  lowercase__	:	Optional[int]=7  ,  lowercase__	:	List[Any]=True  ,  lowercase__	:	str=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	List[str]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Tuple=32  ,  lowercase__	:	Dict=2  ,  lowercase__	:	Optional[int]=4  ,  lowercase__	:	str=37  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Union[str, Any]=5_12  ,  lowercase__	:	List[str]=0.0_2  ,  lowercase__	:	Tuple=0  ,  lowercase__	:	List[Any]=None  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							projection_dim
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							dropout
										_lowerCAmelCase							=							attention_dropout
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							bos_token_id





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

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

										if input_mask is not None:
																	_lowerCAmelCase							=							input_mask.numpy()
																	_lowerCAmelCase			,		_lowerCAmelCase							=							input_mask.shape
																	_lowerCAmelCase							=							np.random.randint(1  ,  seq_length - 1  ,  size=(batch_size,)						)
																	for batch_idx, start_index in enumerate(lowercase__						):
																								_lowerCAmelCase							=							1
																								_lowerCAmelCase							=							0

										_lowerCAmelCase							=							self.get_config()

										return config, input_ids, tf.convert_to_tensor(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										return BlipTextConfig(
										    vocab_size=self.vocab_size  ,  hidden_size=self.hidden_size  ,  projection_dim=self.projection_dim  ,  num_hidden_layers=self.num_hidden_layers  ,  num_attention_heads=self.num_attention_heads  ,  intermediate_size=self.intermediate_size  ,  dropout=self.dropout  ,  attention_dropout=self.attention_dropout  ,  max_position_embeddings=self.max_position_embeddings  ,  initializer_range=self.initializer_range  ,  bos_token_id=self.bos_token_id  ,  )





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							TFBlipTextModel(config=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  training=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  training=lowercase__						)
										self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)						)
										self.parent.assertEqual(result.pooler_output.shape  ,  (self.batch_size, self.hidden_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							config_and_inputs
										_lowerCAmelCase							=							{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_tf
class        lowerCamelCase__				(   UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(TFBlipTextModel,) if is_tf_available() else ()
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							BlipTextModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  hidden_size=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										self.config_tester.run_common_tests()





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										pass





			@unittest.skip(reason='Blip does not use inputs_embeds'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										pass





			@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										pass





			@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										pass





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							TFBlipTextModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Any=True						):
										super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase__						)






