import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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
@require_tokenizers
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     LEDTokenizer
							UpperCAmelCase				   =     LEDTokenizerFast
							UpperCAmelCase				   =     True







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

												_UpperCamelCase			     =     os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES['''vocab_file''']		)
												_UpperCamelCase			     =     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(_A		) + '''\n'''		)
												with open(self.merges_file ,			'''w''' ,			encoding='''utf-8'''		) as fp:
																	fp.write('''\n'''.join(_A		)		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			**_A      :     Dict		):
												kwargs.update(self.special_tokens_map		)
												return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**_A		)







							def  UpperCamelCase_						(   self      :     Any ,			**_A      :     Any		):
												kwargs.update(self.special_tokens_map		)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,			**_A		)







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     str		):
												return "lower newer", "lower newer"







							@cached_property
							def  UpperCamelCase_						(   self      :     str		):
												return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''		)







							@cached_property
							def  UpperCamelCase_						(   self      :     Any		):
												return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''		)







							@require_torch
							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												_UpperCamelCase			     =     [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

																	self.assertEqual((2, 9) ,			batch.input_ids.shape		)
																	self.assertEqual((2, 9) ,			batch.attention_mask.shape		)
																	_UpperCamelCase			     =     batch.input_ids.tolist()[0]
																	self.assertListEqual(_A ,			_A		)







							@require_torch
							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase			     =     tokenizer(_A ,			padding=_A ,			return_tensors='''pt'''		)
																	self.assertIn('''input_ids''' ,			_A		)
																	self.assertIn('''attention_mask''' ,			_A		)
																	self.assertNotIn('''labels''' ,			_A		)
																	self.assertNotIn('''decoder_attention_mask''' ,			_A		)







							@require_torch
							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     [
												    '''Summary of the text.''',
												    '''Another summary.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase			     =     tokenizer(text_target=_A ,			max_length=32 ,			padding='''max_length''' ,			return_tensors='''pt'''		)
																	self.assertEqual(32 ,			targets['''input_ids'''].shape[1]		)







							@require_torch
							def  UpperCamelCase_						(   self      :     List[Any]		):
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase			     =     tokenizer(
																	    ['''I am a small frog''' * 1024, '''I am a small frog'''] ,			padding=_A ,			truncation=_A ,			return_tensors='''pt'''		)
																	self.assertIsInstance(_A ,			_A		)
																	self.assertEqual(batch.input_ids.shape ,			(2, 5122)		)







							@require_torch
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     ['''A long paragraph for summarization.''']
												_UpperCamelCase			     =     [
												    '''Summary of the text.''',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase			     =     tokenizer(_A ,			return_tensors='''pt'''		)
																	_UpperCamelCase			     =     tokenizer(text_target=_A ,			return_tensors='''pt'''		)
																	_UpperCamelCase			     =     inputs['''input_ids''']
																	_UpperCamelCase			     =     targets['''input_ids''']
																	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()		)







							@require_torch
							def  UpperCamelCase_						(   self      :     str		):
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	_UpperCamelCase			     =     ['''Summary of the text.''', '''Another summary.''']
																	_UpperCamelCase			     =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																	_UpperCamelCase			     =     tokenizer(_A ,			padding=_A		)
																	_UpperCamelCase			     =     [[0] * len(_A		) for x in encoded_output['''input_ids''']]
																	_UpperCamelCase			     =     tokenizer.pad(_A		)
																	self.assertSequenceEqual(outputs['''global_attention_mask'''] ,			_A		)







							def  UpperCamelCase_						(   self      :     Any		):
												pass







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
																						_UpperCamelCase			     =     self.rust_tokenizer_class.from_pretrained(_A ,			**_A		)
																						_UpperCamelCase			     =     self.tokenizer_class.from_pretrained(_A ,			**_A		)
																						_UpperCamelCase			     =     '''A, <mask> AllenNLP sentence.'''
																						_UpperCamelCase			     =     tokenizer_r.encode_plus(_A ,			add_special_tokens=_A ,			return_token_type_ids=_A		)
																						_UpperCamelCase			     =     tokenizer_p.encode_plus(_A ,			add_special_tokens=_A ,			return_token_type_ids=_A		)
																						self.assertEqual(sum(tokens_r['''token_type_ids''']		) ,			sum(tokens_p['''token_type_ids''']		)		)
																						self.assertEqual(
																						    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		) ,			sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		) ,			)

																						_UpperCamelCase			     =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																						_UpperCamelCase			     =     tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)
																						self.assertSequenceEqual(tokens_p['''input_ids'''] ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
																						self.assertSequenceEqual(tokens_r['''input_ids'''] ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

																						self.assertSequenceEqual(
																						    _A ,			['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																						self.assertSequenceEqual(
																						    _A ,			['''<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      :     Any ,			_A      :     int ,			_A      :     int=12 ,			_A      :     int=7 ,			_A      :     Tuple=True ,			_A      :     Optional[int]=True ,			_A      :     Union[str, Any]=True ,			_A      :     str=99 ,			_A      :     str=32 ,			_A      :     int=32 ,			_A      :     Optional[Any]=2 ,			_A      :     Dict=4 ,			_A      :     int=37 ,			_A      :     List[Any]=0.1 ,			_A      :     str=0.1 ,			_A      :     Any=512 ,			_A      :     int=0.02 ,			_A      :     Optional[Any]=0 ,			_A      :     Dict=None ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     seq_length
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_input_mask
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     projection_dim
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     scope
												_UpperCamelCase			     =     bos_token_id







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

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

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

												_UpperCamelCase			     =     self.get_config()

												return config, input_ids, tf.convert_to_tensor(_A		)







							def  UpperCamelCase_						(   self      :     str		):
												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  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     str ,			_A      :     Optional[Any]		):
												_UpperCamelCase			     =     TFBlipTextModel(config=_A		)
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A ,			training=_A		)
												_UpperCamelCase			     =     model(_A ,			training=_A		)
												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  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict




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







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     BlipTextModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			hidden_size=37		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												pass







							def  UpperCamelCase_						(   self      :     Tuple		):
												pass







							@unittest.skip(reason='''Blip does not use inputs_embeds'''		)
							def  UpperCamelCase_						(   self      :     Dict		):
												pass







							@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''		)
							def  UpperCamelCase_						(   self      :     Dict		):
												pass







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







							@slow
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase			     =     TFBlipTextModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Optional[int]=True		):
												super().test_pt_tf_model_equivalence(allow_missing_keys=_A		)
