"""simple docstring"""





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      UpperCAmelCase_	:


      def __init__(   self		,			a		,			a=1_2		,			a=7		,			a=True		,			a=True		,			a=True		,			a=9_9		,			a=3_2		,			a=3_2		,			a=2		,			a=4		,			a=3_7		,			a=0.1		,			a=0.1		,			a=5_1_2		,			a=0.02		,			a=0		,			a=None		,			)       ->					Union[str, Any]:
             lowercase__			:		Any       =	parent
             lowercase__			:		str       =	batch_size
             lowercase__			:		List[Any]       =	seq_length
             lowercase__			:		Union[str, Any]       =	is_training
             lowercase__			:		List[str]       =	use_input_mask
             lowercase__			:		int       =	use_labels
             lowercase__			:		List[Any]       =	vocab_size
             lowercase__			:		str       =	hidden_size
             lowercase__			:		int       =	projection_dim
             lowercase__			:		Optional[int]       =	num_hidden_layers
             lowercase__			:		Any       =	num_attention_heads
             lowercase__			:		Optional[Any]       =	intermediate_size
             lowercase__			:		Optional[Any]       =	dropout
             lowercase__			:		Optional[int]       =	attention_dropout
             lowercase__			:		Optional[int]       =	max_position_embeddings
             lowercase__			:		str       =	initializer_range
             lowercase__			:		Tuple       =	scope
             lowercase__			:		int       =	bos_token_id


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		List[Any]       =	ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size							)

             lowercase__			:		int       =	None
             if self.use_input_mask:
                    lowercase__			:		Optional[Any]       =	random_attention_mask([self.batch_size, self.seq_length]							)

             if input_mask is not None:
                    lowercase__			:		int       =	input_mask.numpy()
                    lowercase__    ,      lowercase__			:		Tuple       =	input_mask.shape
                    lowercase__			:		List[str]       =	np.random.randint(1		,			seq_length - 1		,			size=(batch_size,)							)
                    for batch_idx, start_index in enumerate(a							):
                           lowercase__			:		Dict       =	1
                           lowercase__			:		Union[str, Any]       =	0

             lowercase__			:		Tuple       =	self.get_config()

             return config, input_ids, tf.convert_to_tensor(a							)


      def 		_UpperCAmelCase       (   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 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Any:
             lowercase__			:		List[Any]       =	TFBlipTextModel(config=a							)
             lowercase__			:		Optional[int]       =	model(a		,			attention_mask=a		,			training=a							)
             lowercase__			:		List[str]       =	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							)       ->					Any:
             lowercase__			:		Optional[Any]       =	self.prepare_config_and_inputs()
             lowercase__    ,      lowercase__    ,      lowercase__			:		Any       =	config_and_inputs
             lowercase__			:		Any       =	{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_tf
class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      lowerCamelCase__		:    Dict   =  (TFBlipTextModel,) if is_tf_available() else ()
      lowerCamelCase__		:    Optional[Any]   =  False
      lowerCamelCase__		:    List[str]   =  False
      lowerCamelCase__		:    Any   =  False


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	BlipTextModelTester(self							)
             lowercase__			:		int       =	ConfigTester(self		,			config_class=a		,			hidden_size=3_7							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             self.config_tester.run_common_tests()


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Dict       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             pass


      @unittest.skip(reason='Blip does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             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							)       ->					str:
             pass


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					int:
             for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__			:		Any       =	TFBlipTextModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      def 		_UpperCAmelCase       (   self		,			a=True							)       ->					List[str]:
             super().test_pt_tf_model_equivalence(allow_missing_keys=a							)






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowercase: Union[str, Any]      			=		get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
				_lowercase: List[str]      			=		json.load(f)



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
						if torch_device == "cuda":
											model.half()
						return model




	@parameterized.expand(
	    [
	        ["en-ru", 26.0],
	        ["ru-en", 22.0],
	        ["en-de", 22.0],
	        ["de-en", 29.0],
	    ]		)
	@slow
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   F'''facebook/wmt19-{pair}'''
						a					     =   self.get_tokenizer(lowerCamelCase_		)
						a					     =   self.get_model(lowerCamelCase_		)

						a					     =   bleu_data[pair]["src"]
						a					     =   bleu_data[pair]["tgt"]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="pt"		,       truncation=lowerCamelCase_		,       padding="longest"		).to(lowerCamelCase_		)
						a					     =   model.generate(
						    input_ids=batch.input_ids		,       num_beams=8		,       )
						a					     =   tokenizer.batch_decode(
						    lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_		)
						a					     =   calculate_bleu(lowerCamelCase_		,       lowerCamelCase_		)
						print(lowerCamelCase_		)
						self.assertGreaterEqual(scores["bleu"]		,       lowerCamelCase_		)
