





"""simple docstring"""





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
       from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import PoolFormerImageProcessor
class 		lowercase ( _UpperCAmelCase ):





      def    _snake_case      (	self			)		->					Any:
            lowerCAmelCase  						=	self.config_class(**self.inputs_dict			)
            self.parent.assertTrue(hasattr(lowercase				,						"""hidden_sizes"""			)			)
            self.parent.assertTrue(hasattr(lowercase				,						"""num_encoder_blocks"""			)			)







class 		lowercase :





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=64				,						lowercase=3				,						lowercase=4				,						lowercase=[2, 2, 2, 2]				,						lowercase=[8, 4, 2, 1]				,						lowercase=[16, 32, 64, 128]				,						lowercase=[1, 4, 8, 16]				,						lowercase=False				,						lowercase=True				,						lowercase="gelu"				,						lowercase=0.1				,						lowercase=0.02				,						lowercase=3				,						lowercase=None				,						)		->					Tuple:
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	num_encoder_blocks
            lowerCAmelCase  						=	sr_ratios
            lowerCAmelCase  						=	depths
            lowerCAmelCase  						=	hidden_sizes
            lowerCAmelCase  						=	downsampling_rates
            lowerCAmelCase  						=	is_training
            lowerCAmelCase  						=	use_labels
            lowerCAmelCase  						=	hidden_act
            lowerCAmelCase  						=	hidden_dropout_prob
            lowerCAmelCase  						=	initializer_range
            lowerCAmelCase  						=	num_labels
            lowerCAmelCase  						=	scope





      def    _snake_case      (	self			)		->					Any:
            lowerCAmelCase  						=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

            lowerCAmelCase  						=	None
            if self.use_labels:
                  lowerCAmelCase  						=	ids_tensor([self.batch_size, self.image_size, self.image_size]				,						self.num_labels			)

            lowerCAmelCase  						=	PoolFormerConfig(
                image_size=self.image_size				,						num_channels=self.num_channels				,						num_encoder_blocks=self.num_encoder_blocks				,						depths=self.depths				,						hidden_sizes=self.hidden_sizes				,						hidden_act=self.hidden_act				,						hidden_dropout_prob=self.hidden_dropout_prob				,						initializer_range=self.initializer_range				,						)

            return config, pixel_values, labels





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase			)		->					int:
            lowerCAmelCase  						=	PoolFormerModel(config=lowercase			)
            model.to(lowercase			)
            model.eval()
            lowerCAmelCase  						=	model(lowercase			)
            lowerCAmelCase  						=	lowerCAmelCase  						=	self.image_size // 32.0
            self.parent.assertEqual(
                result.last_hidden_state.shape				,						(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	self.prepare_config_and_inputs()
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	config_and_inputs
            lowerCAmelCase  						=	{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 		lowercase ( _UpperCAmelCase					,  _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE         =				(
          {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	PoolFormerModelTester(self			)
            lowerCAmelCase  						=	PoolFormerConfigTester(self				,						config_class=lowercase			)





      def    _snake_case      (	self			)		->					Union[str, Any]:
            self.config_tester.run_common_tests()





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase			)





      @unittest.skip("""PoolFormer does not use inputs_embeds"""			)
      def    _snake_case      (	self			)		->					str:
            pass





      @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""			)
      def    _snake_case      (	self			)		->					Optional[int]:
            pass





      def    _snake_case      (	self			)		->					int:
            def check_hidden_states_output(lowercase				,						lowercase				,						lowercase			):
                  lowerCAmelCase  						=	model_class(lowercase			)
                  model.to(lowercase			)
                  model.eval()

                  with torch.no_grad():
                        lowerCAmelCase  						=	model(**self._prepare_for_class(lowercase				,						lowercase			)			)

                  lowerCAmelCase  						=	outputs.hidden_states

                  lowerCAmelCase  						=	self.model_tester.num_encoder_blocks
                  self.assertEqual(len(lowercase			)				,						lowercase			)

                  # verify the first hidden states (first block)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-3:]			)				,						[
                          self.model_tester.hidden_sizes[0],
                          self.model_tester.image_size // 4,
                          self.model_tester.image_size // 4,
                      ]				,						)

            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase  						=	True
                  check_hidden_states_output(lowercase				,						lowercase				,						lowercase			)

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

                  check_hidden_states_output(lowercase				,						lowercase				,						lowercase			)





      def    _snake_case      (	self			)		->					Tuple:
            if not self.model_tester.is_training:
                  return

            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase  						=	True

            for model_class in self.all_model_classes:
                  if model_class in get_values(lowercase			):
                        continue
                  lowerCAmelCase  						=	model_class(lowercase			)
                  model.to(lowercase			)
                  model.train()
                  lowerCAmelCase  						=	self._prepare_for_class(lowercase				,						lowercase				,						return_labels=lowercase			)
                  lowerCAmelCase  						=	model(**lowercase			).loss
                  loss.backward()





      def    _snake_case      (	self			)		->					Union[str, Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowerCAmelCase  						=	["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]				,						lowercase			)





      @slow
      def    _snake_case      (	self			)		->					Optional[int]:
            for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase  						=	PoolFormerModel.from_pretrained(lowercase			)
                  self.assertIsNotNone(lowercase			)







def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
      return image






@require_torch
class 		lowercase ( unittest.TestCase ):





      @slow
      def    _snake_case      (	self			)		->					Union[str, Any]:
            lowerCAmelCase  						=	PoolFormerImageProcessor()
            lowerCAmelCase  						=	PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""			).to(lowercase			)

            lowerCAmelCase  						=	image_processor(images=prepare_img()				,						return_tensors="""pt"""			).to(lowercase			)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase  						=	model(**lowercase			)

            # verify the logits
            lowerCAmelCase  						=	torch.Size((1, 1_000)			)
            self.assertEqual(outputs.logits.shape				,						lowercase			)

            lowerCAmelCase  						=	torch.tensor([-0.6_113, 0.1_685, -0.0_492]			).to(lowercase			)
            self.assertTrue(torch.allclose(outputs.logits[0, :3]				,						lowercase				,						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 		lowercase :
      _SCREAMING_SNAKE_CASE         =				MarianConfig
      _SCREAMING_SNAKE_CASE         =				{}
      _SCREAMING_SNAKE_CASE         =				'gelu'





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=7				,						lowercase=True				,						lowercase=False				,						lowercase=99				,						lowercase=32				,						lowercase=2				,						lowercase=4				,						lowercase=37				,						lowercase=0.1				,						lowercase=0.1				,						lowercase=20				,						lowercase=2				,						lowercase=1				,						lowercase=0				,						)		->					int:
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	seq_length
            lowerCAmelCase  						=	is_training
            lowerCAmelCase  						=	use_labels
            lowerCAmelCase  						=	vocab_size
            lowerCAmelCase  						=	hidden_size
            lowerCAmelCase  						=	num_hidden_layers
            lowerCAmelCase  						=	num_attention_heads
            lowerCAmelCase  						=	intermediate_size

            lowerCAmelCase  						=	hidden_dropout_prob
            lowerCAmelCase  						=	attention_probs_dropout_prob
            lowerCAmelCase  						=	max_position_embeddings
            lowerCAmelCase  						=	eos_token_id
            lowerCAmelCase  						=	pad_token_id
            lowerCAmelCase  						=	bos_token_id





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length - 1]				,						self.vocab_size			)
            lowerCAmelCase  						=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)				,						1			)
            lowerCAmelCase  						=	tf.concat([input_ids, eos_tensor]				,						axis=1			)

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

            lowerCAmelCase  						=	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				,						)
            lowerCAmelCase  						=	prepare_marian_inputs_dict(lowercase				,						lowercase				,						lowercase			)
            return config, inputs_dict





      def    _snake_case      (	self				,						lowercase				,						lowercase			)		->					Union[str, Any]:
            lowerCAmelCase  						=	TFMarianModel(config=lowercase			).get_decoder()
            lowerCAmelCase  						=	inputs_dict["""input_ids"""]

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

            # first forward pass
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						head_mask=lowercase				,						use_cache=lowercase			)

            lowerCAmelCase						,		lowerCAmelCase  						=	outputs.to_tuple()

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

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

            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase			)[0]
            lowerCAmelCase  						=	model(lowercase				,						attention_mask=lowercase				,						past_key_values=lowercase			)[0]

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

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

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







def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]							,						SCREAMING_SNAKE_CASE					:						Any=None							,						SCREAMING_SNAKE_CASE					:						str=None							,						SCREAMING_SNAKE_CASE					:						List[str]=None							,						SCREAMING_SNAKE_CASE					:						Tuple=None							,						SCREAMING_SNAKE_CASE					:						Dict=None							,						):







      '''simple docstring'''




      if attention_mask is None:
            lowerCAmelCase  						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE							,						config.pad_token_id      )							,						tf.inta      )
      if decoder_attention_mask is None:
            lowerCAmelCase  						=	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:
            lowerCAmelCase  						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            lowerCAmelCase  						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            lowerCAmelCase  						=	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 ):
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(TFMarianMTModel,) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE         =				(
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE         =				True
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	TFMarianModelTester(self			)
            lowerCAmelCase  						=	ConfigTester(self				,						config_class=lowercase			)





      def    _snake_case      (	self			)		->					str:
            self.config_tester.run_common_tests()





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*lowercase			)







@require_tf
class 		lowercase ( unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				1_000  # show more chars for failing integration tests





      @classmethod
      def    _snake_case      (	cls			)		->					None:
            lowerCAmelCase  						=	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
            return cls





      @cached_property
      def    _snake_case      (	self			)		->					MarianTokenizer:
            return AutoTokenizer.from_pretrained(self.model_name			)





      @property
      def    _snake_case      (	self			)		->					int:
            return self.tokenizer.eos_token_id





      @cached_property
      def    _snake_case      (	self			)		->					Any:
            warnings.simplefilter("""error"""			)
            lowerCAmelCase  						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
            assert isinstance(lowercase				,						lowercase			)
            lowerCAmelCase  						=	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    _snake_case      (	self				,						**lowercase			)		->					Tuple:
            lowerCAmelCase  						=	self.translate_src_text(**lowercase			)
            self.assertListEqual(self.expected_text				,						lowercase			)





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







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'mt'
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      _SCREAMING_SNAKE_CASE         =				['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					List[Any]:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'zh'
      _SCREAMING_SNAKE_CASE         =				['My name is Wolfgang and I live in Berlin']
      _SCREAMING_SNAKE_CASE         =				['我叫沃尔夫冈 我住在柏林']





      @unittest.skip("""Skipping until #12647 is resolved."""			)
      @slow
      def    _snake_case      (	self			)		->					Dict:
            self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'en'
      _SCREAMING_SNAKE_CASE         =				'ROMANCE'
      _SCREAMING_SNAKE_CASE         =				[
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      _SCREAMING_SNAKE_CASE         =				[
          '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    _snake_case      (	self			)		->					List[str]:
            self._assert_generated_batch_equal_expected()





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






