





'''simple docstring'''


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
				import tensorflow as tf

				from transformers.models.swin.modeling_tf_swin import (
				    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSwinForImageClassification,
				    TFSwinForMaskedImageModeling,
				    TFSwinModel,
				)


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Tuple=13   ,   _lowerCAmelCase    :   Any=32   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Dict=3   ,   _lowerCAmelCase    :   Any=16   ,   _lowerCAmelCase    :   Tuple=[1, 2, 1]   ,   _lowerCAmelCase    :   List[Any]=[2, 2, 4]   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Tuple=2.0   ,   _lowerCAmelCase    :   Dict=True   ,   _lowerCAmelCase    :   Union[str, Any]=0.0   ,   _lowerCAmelCase    :   Any=0.0   ,   _lowerCAmelCase    :   Any=0.1   ,   _lowerCAmelCase    :   Tuple="gelu"   ,   _lowerCAmelCase    :   Union[str, Any]=False   ,   _lowerCAmelCase    :   Optional[int]=True   ,   _lowerCAmelCase    :   Union[str, Any]=0.02   ,   _lowerCAmelCase    :   str=1e-5   ,   _lowerCAmelCase    :   List[Any]=True   ,   _lowerCAmelCase    :   Union[str, Any]=None   ,   _lowerCAmelCase    :   Union[str, Any]=True   ,   _lowerCAmelCase    :   str=10   ,   _lowerCAmelCase    :   List[str]=8   ,   ):
			A					=   parent
			A					=   batch_size
			A					=   image_size
			A					=   patch_size
			A					=   num_channels
			A					=   embed_dim
			A					=   depths
			A					=   num_heads
			A					=   window_size
			A					=   mlp_ratio
			A					=   qkv_bias
			A					=   hidden_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   drop_path_rate
			A					=   hidden_act
			A					=   use_absolute_embeddings
			A					=   patch_norm
			A					=   layer_norm_eps
			A					=   initializer_range
			A					=   is_training
			A					=   scope
			A					=   use_labels
			A					=   type_sequence_label_size
			A					=   encoder_stride







		def 							A  (self    :   Optional[int]   ):
			A					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

			A					=   None
			if self.use_labels:
				A					=   ids_tensor([self.batch_size]   ,   self.type_sequence_label_size   )

			A					=   self.get_config()

			return config, pixel_values, labels







		def 							A  (self    :   int   ):
			return SwinConfig(
			    image_size=self.image_size   ,   patch_size=self.patch_size   ,   num_channels=self.num_channels   ,   embed_dim=self.embed_dim   ,   depths=self.depths   ,   num_heads=self.num_heads   ,   window_size=self.window_size   ,   mlp_ratio=self.mlp_ratio   ,   qkv_bias=self.qkv_bias   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   drop_path_rate=self.drop_path_rate   ,   hidden_act=self.hidden_act   ,   use_absolute_embeddings=self.use_absolute_embeddings   ,   path_norm=self.patch_norm   ,   layer_norm_eps=self.layer_norm_eps   ,   initializer_range=self.initializer_range   ,   encoder_stride=self.encoder_stride   ,   )







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			A					=   TFSwinModel(config=_lowerCAmelCase   )
			A					=   model(_lowerCAmelCase   )

			A					=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
			A					=   int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

			self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, expected_seq_len, expected_dim)   )







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Optional[Any]   ):
			A					=   TFSwinForMaskedImageModeling(config=_lowerCAmelCase   )
			A					=   model(_lowerCAmelCase   )
			self.parent.assertEqual(
			    result.logits.shape   ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)   )

			# test greyscale images
			A					=   1
			A					=   TFSwinForMaskedImageModeling(_lowerCAmelCase   )

			A					=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
			A					=   model(_lowerCAmelCase   )
			self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, 1, self.image_size, self.image_size)   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   int   ):
			A					=   self.type_sequence_label_size
			A					=   TFSwinForImageClassification(_lowerCAmelCase   )
			A					=   model(_lowerCAmelCase   ,   labels=_lowerCAmelCase   )
			self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)   )

			# test greyscale images
			A					=   1
			A					=   TFSwinForImageClassification(_lowerCAmelCase   )
			A					=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
			A					=   model(_lowerCAmelCase   )
			self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)   )







		def 							A  (self    :   Union[str, Any]   ):
			A					=   self.prepare_config_and_inputs()
			A	,     A	,     A					=   config_and_inputs
			A					=   {"""pixel_values""": pixel_values}
			return config, inputs_dict






@require_tf
class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     (
		    (
		        TFSwinModel,
		        TFSwinForImageClassification,
		        TFSwinForMaskedImageModeling,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowerCAmelCase								=     (
		    {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			A					=   TFSwinModelTester(self   )
			A					=   ConfigTester(self   ,   config_class=_lowerCAmelCase   ,   embed_dim=37   )







		def 							A  (self    :   Union[str, Any]   ):
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()







		def 							A  (self    :   Optional[int]   ):
			return







		def 							A  (self    :   Optional[Any]   ):
			A					=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_lowerCAmelCase   )







		def 							A  (self    :   Optional[int]   ):
			A					=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase   )







		def 							A  (self    :   Union[str, Any]   ):
			A					=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase   )







		@unittest.skip(reason="""Swin does not use inputs_embeds"""   )
		def 							A  (self    :   Union[str, Any]   ):
			pass







		def 							A  (self    :   int   ):
			A	,     A					=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				A					=   model_class(_lowerCAmelCase   )
				self.assertIsInstance(model.get_input_embeddings()   ,   tf.keras.layers.Layer   )
				A					=   model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(_lowerCAmelCase   ,   tf.keras.layers.Dense   )   )







		def 							A  (self    :   Union[str, Any]   ):
			A	,     A					=   self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				A					=   model_class(_lowerCAmelCase   )
				A					=   inspect.signature(model.call   )
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				A					=   [*signature.parameters.keys()]

				A					=   ["""pixel_values"""]
				self.assertListEqual(arg_names[:1]   ,   _lowerCAmelCase   )







		def 							A  (self    :   Any   ):
			A	,     A					=   self.model_tester.prepare_config_and_inputs_for_common()
			A					=   True

			for model_class in self.all_model_classes:
				A					=   True
				A					=   False
				A					=   True
				A					=   model_class(_lowerCAmelCase   )
				A					=   model(**self._prepare_for_class(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
				A					=   outputs.attentions
				A					=   len(self.model_tester.depths   )
				self.assertEqual(len(_lowerCAmelCase   )   ,   _lowerCAmelCase   )

				# check that output_attentions also work using config
				del inputs_dict["output_attentions"]
				A					=   True
				A					=   config.window_size**2
				A					=   model_class(_lowerCAmelCase   )
				A					=   model(**self._prepare_for_class(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
				A					=   outputs.attentions
				self.assertEqual(len(_lowerCAmelCase   )   ,   _lowerCAmelCase   )

				self.assertListEqual(
				    list(attentions[0].shape[-3:]   )   ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,   )
				A					=   len(_lowerCAmelCase   )

				# Check attention is always last and order is fine
				A					=   True
				A					=   True
				A					=   model_class(_lowerCAmelCase   )
				A					=   model(**self._prepare_for_class(_lowerCAmelCase   ,   _lowerCAmelCase   )   )

				if hasattr(self.model_tester   ,   """num_hidden_states_types"""   ):
					A					=   self.model_tester.num_hidden_states_types
				else:
					# also another +1 for reshaped_hidden_states
					A					=   2
				self.assertEqual(out_len + added_hidden_states   ,   len(_lowerCAmelCase   )   )

				A					=   outputs.attentions

				self.assertEqual(len(_lowerCAmelCase   )   ,   _lowerCAmelCase   )

				self.assertListEqual(
				    list(self_attentions[0].shape[-3:]   )   ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,   )







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ):
			A					=   model_class(_lowerCAmelCase   )
			A					=   model(**self._prepare_for_class(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			A					=   outputs.hidden_states

			A					=   getattr(
			    self.model_tester   ,   """expected_num_hidden_layers"""   ,   len(self.model_tester.depths   ) + 1   )
			self.assertEqual(len(_lowerCAmelCase   )   ,   _lowerCAmelCase   )

			# Swin has a different seq_length
			A					=   to_atuple(config.patch_size   )

			A					=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]   )   ,   [num_patches, self.model_tester.embed_dim]   ,   )

			A					=   outputs.reshaped_hidden_states
			self.assertEqual(len(_lowerCAmelCase   )   ,   _lowerCAmelCase   )

			A	,     A	,     A	,     A					=   reshaped_hidden_states[0].shape

			A					=   tf.reshape(reshaped_hidden_states[0]   ,   (batch_size, num_channels, height * width)   )
			A					=   tf.transpose(_lowerCAmelCase   ,   (0, 2, 1)   )

			self.assertListEqual(
			    list(reshaped_hidden_states.shape[-2:]   )   ,   [num_patches, self.model_tester.embed_dim]   ,   )







		def 							A  (self    :   List[Any]   ):
			A	,     A					=   self.model_tester.prepare_config_and_inputs_for_common()

			A					=   to_atuple(self.model_tester.image_size   )

			for model_class in self.all_model_classes:
				A					=   True
				self.check_hidden_states_output(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				A					=   True

				self.check_hidden_states_output(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )







		def 							A  (self    :   Dict   ):
			A	,     A					=   self.model_tester.prepare_config_and_inputs_for_common()
			A					=   3

			A					=   to_atuple(self.model_tester.image_size   )
			A					=   to_atuple(config.patch_size   )

			A					=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			A					=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes:
				A					=   True
				self.check_hidden_states_output(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   (padded_height, padded_width)   )

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				A					=   True
				self.check_hidden_states_output(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   (padded_height, padded_width)   )







		@slow
		def 							A  (self    :   Any   ):
			for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				A					=   TFSwinModel.from_pretrained(_lowerCAmelCase   )
				self.assertIsNotNone(_lowerCAmelCase   )






@require_vision
@require_tf
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		@cached_property
		def 							A  (self    :   Optional[int]   ):
			return (
			    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   )
			    if is_vision_available()
			    else None
			)







		@slow
		def 							A  (self    :   List[str]   ):
			A					=   TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""   )
			A					=   self.default_image_processor

			A					=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			A					=   image_processor(images=_lowerCAmelCase   ,   return_tensors="""tf"""   )

			# forward pass
			A					=   model(_lowerCAmelCase   )

			# verify the logits
			A					=   tf.TensorShape((1, 1000)   )
			self.assertEqual(outputs.logits.shape   ,   _lowerCAmelCase   )
			A					=   tf.constant([-0.0_948, -0.6_454, -0.0_921]   )
			self.assertTrue(np.allclose(outputs.logits[0, :3]   ,   _lowerCAmelCase   ,   atol=1e-4   )   )








'''simple docstring'''


from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel



@require_tf
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     MarianConfig
		__lowerCAmelCase								=     {}
		__lowerCAmelCase								=     '''gelu'''







		def __init__(self    :   Tuple   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict=13   ,   _lowerCAmelCase    :   Optional[int]=7   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   Optional[int]=False   ,   _lowerCAmelCase    :   Tuple=99   ,   _lowerCAmelCase    :   str=32   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Union[str, Any]=4   ,   _lowerCAmelCase    :   Dict=37   ,   _lowerCAmelCase    :   int=0.1   ,   _lowerCAmelCase    :   List[Any]=0.1   ,   _lowerCAmelCase    :   Tuple=20   ,   _lowerCAmelCase    :   List[str]=2   ,   _lowerCAmelCase    :   Any=1   ,   _lowerCAmelCase    :   int=0   ,   ):
			A					=   parent
			A					=   batch_size
			A					=   seq_length
			A					=   is_training
			A					=   use_labels
			A					=   vocab_size
			A					=   hidden_size
			A					=   num_hidden_layers
			A					=   num_attention_heads
			A					=   intermediate_size

			A					=   hidden_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   max_position_embeddings
			A					=   eos_token_id
			A					=   pad_token_id
			A					=   bos_token_id







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

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

			A					=   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   ,   )
			A					=   prepare_marian_inputs_dict(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
			return config, inputs_dict







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   TFMarianModel(config=_lowerCAmelCase   ).get_decoder()
			A					=   inputs_dict["""input_ids"""]

			A					=   input_ids[:1, :]
			A					=   inputs_dict["""attention_mask"""][:1, :]
			A					=   inputs_dict["""head_mask"""]
			A					=   1

			# first forward pass
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   head_mask=_lowerCAmelCase   ,   use_cache=_lowerCAmelCase   )

			A	,     A					=   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			A					=   ids_tensor((self.batch_size, 3)   ,   config.vocab_size   )
			A					=   tf.cast(ids_tensor((self.batch_size, 3)   ,   2   )   ,   tf.inta   )

			# append to next input_ids and
			A					=   tf.concat([input_ids, next_tokens]   ,   axis=-1   )
			A					=   tf.concat([attention_mask, next_attn_mask]   ,   axis=-1   )

			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   )[0]
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   past_key_values=_lowerCAmelCase   )[0]

			self.parent.assertEqual(next_tokens.shape[1]   ,   output_from_past.shape[1]   )

			# select random slice
			A					=   int(ids_tensor((1,)   ,   output_from_past.shape[-1]   )   )
			A					=   output_from_no_past[:, -3:, random_slice_idx]
			A					=   output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      )   ->List[str]:



	"""simple docstring"""







	if attention_mask is None:
		A					=   tf.cast(tf.math.not_equal(UpperCAmelCase							,      config.pad_token_id     )							,      tf.inta     )
	if decoder_attention_mask is None:
		A					=   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:
		A					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
	if decoder_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	if cross_attn_head_mask is None:
		A					=   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 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
		__lowerCAmelCase								=     (TFMarianMTModel,) if is_tf_available() else ()
		__lowerCAmelCase								=     (
		    {
		        '''conversational''': TFMarianMTModel,
		        '''feature-extraction''': TFMarianModel,
		        '''summarization''': TFMarianMTModel,
		        '''text2text-generation''': TFMarianMTModel,
		        '''translation''': TFMarianMTModel,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			A					=   TFMarianModelTester(self   )
			A					=   ConfigTester(self   ,   config_class=_lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			self.config_tester.run_common_tests()







		def 							A  (self    :   Any   ):
			A					=   self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase   )






@require_tf
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     10_00  # show more chars for failing integration tests







		@classmethod
		def 							A  (cls    :   Union[str, Any]   ):
			A					=   F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls







		@cached_property
		def 							A  (self    :   str   ):
			return AutoTokenizer.from_pretrained(self.model_name   )







		@property
		def 							A  (self    :   Optional[int]   ):
			return self.tokenizer.eos_token_id







		@cached_property
		def 							A  (self    :   Optional[Any]   ):
			warnings.simplefilter("""error"""   )
			A					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
			assert isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   model.config
			self.assertListEqual(c.bad_words_ids   ,   [[c.pad_token_id]]   )
			self.assertEqual(c.max_length   ,   512   )
			self.assertEqual(c.decoder_start_token_id   ,   c.pad_token_id   )
			return model







		def 							A  (self    :   Optional[int]   ,   **_lowerCAmelCase    :   Any   ):
			A					=   self.translate_src_text(**_lowerCAmelCase   )
			self.assertListEqual(self.expected_text   ,   _lowerCAmelCase   )







		def 							A  (self    :   Union[str, Any]   ,   **_lowerCAmelCase    :   Union[str, Any]   ):
			A					=   self.tokenizer(self.src_text   ,   **_lowerCAmelCase   ,   padding=_lowerCAmelCase   ,   return_tensors="""tf"""   )
			A					=   self.model.generate(
			    model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   max_length=128   )
			A					=   self.tokenizer.batch_decode(generated_ids.numpy()   ,   skip_special_tokens=_lowerCAmelCase   )
			return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''mt'''
		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
		__lowerCAmelCase								=     ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   str   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''zh'''
		__lowerCAmelCase								=     ['''My name is Wolfgang and I live in Berlin''']
		__lowerCAmelCase								=     ['''我叫沃尔夫冈 我住在柏林''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Any   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''ROMANCE'''
		__lowerCAmelCase								=     [
		    '''>>fr<< Don\'t spend so much time watching TV.''',
		    '''>>pt<< Your message has been sent.''',
		    '''>>es<< He\'s two years older than me.''',
		]
		__lowerCAmelCase								=     [
		    '''Ne passez pas autant de temps à regarder la télé.''',
		    '''A sua mensagem foi enviada.''',
		    '''Es dos años más viejo que yo.''',
		]







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			self._assert_generated_batch_equal_expected()







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			A					=   TranslationPipeline(self.model   ,   self.tokenizer   ,   framework="""tf"""   )
			A					=   pipeline(self.src_text   )
			self.assertEqual(self.expected_text   ,   [x["""translation_text"""] for x in output]   )

