





"""simple docstring"""
import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
		import torch

		from transformers import BlipTextModel
		from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class    _UpperCAmelCase  :


					def __init__(   self     :str	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :List[Any]=12	,			__UpperCamelCase     :Any=7	,			__UpperCamelCase     :Optional[int]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :str=True	,			__UpperCamelCase     :List[Any]=99	,			__UpperCamelCase     :Union[str, Any]=32	,			__UpperCamelCase     :Union[str, Any]=32	,			__UpperCamelCase     :List[Any]=5	,			__UpperCamelCase     :Optional[int]=4	,			__UpperCamelCase     :str=37	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[str]=0.1	,			__UpperCamelCase     :Dict=5_12	,			__UpperCamelCase     :Optional[int]=0.02	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :Tuple=None	,			):
												A						=    parent
												A						=    batch_size
												A						=    seq_length
												A						=    is_training
												A						=    use_input_mask
												A						=    use_labels
												A						=    vocab_size
												A						=    hidden_size
												A						=    projection_dim
												A						=    num_hidden_layers
												A						=    num_attention_heads
												A						=    intermediate_size
												A						=    dropout
												A						=    attention_dropout
												A						=    max_position_embeddings
												A						=    initializer_range
												A						=    scope
												A						=    bos_token_id


					def       lowerCamelCase			(   self     :Dict						):
												A						=    ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size						)

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

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

												A						=    self.get_config()

												return config, input_ids, input_mask


					def       lowerCamelCase			(   self     :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       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :int	,			__UpperCamelCase     :str						):
												A						=    BlipTextModel(config=snake_case__						)
												model.to(snake_case__						)
												model.eval()
												with torch.no_grad():
																			A						=    model(snake_case__	,			attention_mask=snake_case__						)
																			A						=    model(snake_case__						)
												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       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.prepare_config_and_inputs()
												A,      A,      A						=    config_and_inputs
												A						=    {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      a_	,				unittest.TestCase					):
					UpperCamelCase						=				(BlipTextModel,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    BlipTextModelTester(self						)
												A						=    ConfigTester(self	,			config_class=snake_case__	,			hidden_size=37						)


					def       lowerCamelCase			(   self     :List[Any]						):
												self.config_tester.run_common_tests()


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__						)


					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					def       lowerCamelCase			(   self     :str						):
												pass


					@unittest.skip(reason="Blip does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


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


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


					@slow
					def       lowerCamelCase			(   self     :Tuple						):


												for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    BlipTextModel.from_pretrained(snake_case__						)
																			self.assertIsNotNone(snake_case__						)


					def       lowerCamelCase			(   self     :List[Any]						):
												super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__						)







import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY


@is_pipeline_test
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				lowercase__     =    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									if isinstance(model.config  ,			snake_case__     ):
														UpperCamelCase_	,     UpperCamelCase_			= list(tokenizer.lang_code_to_id.keys()     )[:2]
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang=snake_case__  ,			tgt_lang=snake_case__     )
									else:
														UpperCamelCase_			= TranslationPipeline(model=snake_case__  ,			tokenizer=snake_case__     )
									return translator, ["Some string", "Some other text"]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= translator("Some string"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}]     )

									UpperCamelCase_			= translator(["Some string", "other string"]     )
									self.assertEqual(snake_case__  ,			[{"translation_text": ANY(snake_case__     )}, {"translation_text": ANY(snake_case__     )}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_ro"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
									                " Beide Beide"
									            )
									        }
									    ]  ,			)

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="pt"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)

				@require_tf
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= pipeline("translation_en_to_de"  ,			model="patrickvonplaten/t5-tiny-random"  ,			framework="tf"     )
									UpperCamelCase_			= translator("This is a test string"  ,			max_length=20     )
									self.assertEqual(
									    snake_case__  ,			[
									        {
									            "translation_text": (
									                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
									                " urine urine urine urine urine urine urine"
									            )
									        }
									    ]  ,			)




class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation_cn_to_ar"     )

									# but we do for this one
									UpperCamelCase_			= pipeline(task="translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )
									UpperCamelCase_			= MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"     )

									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__     )
									# Missing src_lang, tgt_lang
									with self.assertRaises(snake_case__     ):
														translator("This is a test"     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

									UpperCamelCase_			= translator("This is a test"  ,			src_lang="en_XX"  ,			tgt_lang="hi_IN"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "यह एक परीक्षण है"}]     )

									# src_lang, tgt_lang can be defined at pipeline call time
									UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__  ,			tokenizer=snake_case__  ,			src_lang="en_XX"  ,			tgt_lang="ar_AR"     )
									UpperCamelCase_			= translator("This is a test"     )
									self.assertEqual(snake_case__  ,			[{"translation_text": "هذا إختبار"}]     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									UpperCamelCase_			= pipeline(task="translation_cn_to_ar"  ,			model=snake_case__     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"cn"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"ar"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "patrickvonplaten/t5-tiny-random"
									with pytest.warns(snake_case__  ,			match=R".*translation_en_to_de.*"     ):
														UpperCamelCase_			= pipeline(task="translation"  ,			model=snake_case__     )
									self.assertEqual(translator.task  ,			"translation_en_to_de"     )
									self.assertEqual(translator._preprocess_params["src_lang"]  ,			"en"     )
									self.assertEqual(translator._preprocess_params["tgt_lang"]  ,			"de"     )

				@require_torch
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									with self.assertRaises(snake_case__     ):
														pipeline(task="translation"     )
