



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel




@require_tf
class       lowercase__      :
							'''simple docstring'''







							_UpperCAmelCase   					=       MBartConfig
							_UpperCAmelCase   					=       {}
							_UpperCAmelCase   					=       'gelu'






							def __init__(	self						,							snake_case						,							snake_case=13						,							snake_case=7						,							snake_case=True						,							snake_case=False						,							snake_case=99						,							snake_case=32						,							snake_case=2						,							snake_case=4						,							snake_case=37						,							snake_case=0.1						,							snake_case=0.1						,							snake_case=20						,							snake_case=2						,							snake_case=1						,							snake_case=0						,							)      ->				int:
									_UpperCAmelCase							=		parent
									_UpperCAmelCase							=		batch_size
									_UpperCAmelCase							=		seq_length
									_UpperCAmelCase							=		is_training
									_UpperCAmelCase							=		use_labels
									_UpperCAmelCase							=		vocab_size
									_UpperCAmelCase							=		hidden_size
									_UpperCAmelCase							=		num_hidden_layers
									_UpperCAmelCase							=		num_attention_heads
									_UpperCAmelCase							=		intermediate_size
									_UpperCAmelCase							=		hidden_dropout_prob
									_UpperCAmelCase							=		attention_probs_dropout_prob
									_UpperCAmelCase							=		max_position_embeddings
									_UpperCAmelCase							=		eos_token_id
									_UpperCAmelCase							=		pad_token_id
									_UpperCAmelCase							=		bos_token_id






							def 							lowerCamelCase_ (	self    )      ->				Tuple:
									_UpperCAmelCase							=		ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size    )
									_UpperCAmelCase							=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )						,							1    )
									_UpperCAmelCase							=		tf.concat([input_ids, eos_tensor]						,							axis=1    )

									_UpperCAmelCase							=		ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size    )

									_UpperCAmelCase							=		self.config_cls(
									    vocab_size=self.vocab_size						,							d_model=self.hidden_size						,							encoder_layers=self.num_hidden_layers						,							decoder_layers=self.num_hidden_layers						,							encoder_attention_heads=self.num_attention_heads						,							decoder_attention_heads=self.num_attention_heads						,							encoder_ffn_dim=self.intermediate_size						,							decoder_ffn_dim=self.intermediate_size						,							dropout=self.hidden_dropout_prob						,							attention_dropout=self.attention_probs_dropout_prob						,							max_position_embeddings=self.max_position_embeddings						,							eos_token_ids=[2]						,							bos_token_id=self.bos_token_id						,							pad_token_id=self.pad_token_id						,							decoder_start_token_id=self.pad_token_id						,							**self.config_updates						,							)
									_UpperCAmelCase							=		prepare_mbart_inputs_dict(lowerCAmelCase_						,							lowerCAmelCase_						,							lowerCAmelCase_    )
									return config, inputs_dict






							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case    )      ->				int:
									_UpperCAmelCase							=		TFMBartModel(config=lowerCAmelCase_    ).get_decoder()
									_UpperCAmelCase							=		inputs_dict['input_ids']

									_UpperCAmelCase							=		input_ids[:1, :]
									_UpperCAmelCase							=		inputs_dict['attention_mask'][:1, :]
									_UpperCAmelCase							=		inputs_dict['head_mask']
									_UpperCAmelCase							=		1

									# first forward pass
									_UpperCAmelCase							=		model(lowerCAmelCase_						,							attention_mask=lowerCAmelCase_						,							head_mask=lowerCAmelCase_						,							use_cache=lowerCAmelCase_    )

									_UpperCAmelCase							=		outputs.to_tuple()
									_UpperCAmelCase							=		past_key_values[1]







def 						UpperCAmelCase  (		A						:    List[Any]  ,					A						:    Dict  ,					A						:    Any  ,					A						:    Union[str, Any]=None  ,					A						:    Union[str, Any]=None  ,					A						:    int=None  ,					A						:    Any=None  ,					A						:    int=None  ,					):

		'''simple docstring'''


		if attention_mask is None:
				_UpperCAmelCase							=		tf.cast(tf.math.not_equal(A  ,					config.pad_token_id							)  ,					tf.inta							)
		if decoder_attention_mask is None:
				_UpperCAmelCase							=		tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape  ,					dtype=tf.inta							),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,					config.pad_token_id							)  ,					tf.inta							),
				    ]  ,					axis=-1  ,					)
		if head_mask is None:
				_UpperCAmelCase							=		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
		if decoder_head_mask is None:
				_UpperCAmelCase							=		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
		if cross_attn_head_mask is None:
				_UpperCAmelCase							=		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}




@require_tf
class       lowercase__      (   __UpperCAmelCase,       __UpperCAmelCase,       unittest.TestCase     ):
							'''simple docstring'''







							_UpperCAmelCase   					=       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							_UpperCAmelCase   					=       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							_UpperCAmelCase   					=       (
							    {
							        'conversational': TFMBartForConditionalGeneration,
							        'feature-extraction': TFMBartModel,
							        'summarization': TFMBartForConditionalGeneration,
							        'text2text-generation': TFMBartForConditionalGeneration,
							        'translation': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_UpperCAmelCase   					=       True
							_UpperCAmelCase   					=       False
							_UpperCAmelCase   					=       False






							def 							lowerCamelCase_ (	self						,							snake_case						,							snake_case						,							snake_case						,							snake_case						,							snake_case    )      ->				List[Any]:
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False






							def 							lowerCamelCase_ (	self    )      ->				Optional[int]:
									_UpperCAmelCase							=		TFMBartModelTester(self    )
									_UpperCAmelCase							=		ConfigTester(self						,							config_class=lowerCAmelCase_    )






							def 							lowerCamelCase_ (	self    )      ->				List[Any]:
									self.config_tester.run_common_tests()






							def 							lowerCamelCase_ (	self    )      ->				Dict:
									_UpperCAmelCase							=		self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_    )







@require_sentencepiece
@require_tokenizers
@require_tf
class       lowercase__      (   unittest.TestCase     ):
							'''simple docstring'''







							_UpperCAmelCase   					=       [
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							_UpperCAmelCase   					=       [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							_UpperCAmelCase   					=       'facebook/mbart-large-en-ro'






							@cached_property
							def 							lowerCamelCase_ (	self    )      ->				Union[str, Any]:
									return AutoTokenizer.from_pretrained(self.model_name    )






							@cached_property
							def 							lowerCamelCase_ (	self    )      ->				Optional[int]:
									_UpperCAmelCase							=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
									return model






							def 							lowerCamelCase_ (	self						,							**snake_case    )      ->				Optional[int]:
									_UpperCAmelCase							=		self.translate_src_text(**lowerCAmelCase_    )
									self.assertListEqual(self.expected_text						,							lowerCAmelCase_    )






							def 							lowerCamelCase_ (	self						,							**snake_case    )      ->				Tuple:
									_UpperCAmelCase							=		self.tokenizer(self.src_text						,							**lowerCAmelCase_						,							return_tensors='tf'    )
									_UpperCAmelCase							=		self.model.generate(
									    model_inputs.input_ids						,							attention_mask=model_inputs.attention_mask						,							num_beams=2    )
									_UpperCAmelCase							=		self.tokenizer.batch_decode(lowerCAmelCase_						,							skip_special_tokens=lowerCAmelCase_    )
									return generated_words






							@slow
							def 							lowerCamelCase_ (	self    )      ->				Any:
									self._assert_generated_batch_equal_expected()










"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'swin2sr'

						SCREAMING_SNAKE_CASE_:					Dict 					=				{
						    'hidden_size': 'embed_dim',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_=6_4   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=3   ,				lowerCAmelCase_=1_8_0   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=[6, 6, 6, 6, 6, 6]   ,				lowerCAmelCase_=8   ,				lowerCAmelCase_=2.0   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.0   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_="gelu"   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=0.02   ,				lowerCAmelCase_=1e-5   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1.0   ,				lowerCAmelCase_="1conv"   ,				lowerCAmelCase_="pixelshuffle"   ,				**lowerCAmelCase_   ,				)		->							Dict:
										super().__init__(**lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    depths
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    len(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    window_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    mlp_ratio
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    qkv_bias
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    drop_path_rate
										_SCREAMING_SNAKE_CASE   :		int       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    use_absolute_embeddings
										_SCREAMING_SNAKE_CASE   :		str       		=    layer_norm_eps
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    upscale
										_SCREAMING_SNAKE_CASE   :		str       		=    img_range
										_SCREAMING_SNAKE_CASE   :		Any       		=    resi_connection
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    upsampler

