

'''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 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[int]    =				MarianConfig
      SCREAMING_SNAKE_CASE__ :				Any    =				{}
      SCREAMING_SNAKE_CASE__ :				List[str]    =				'''gelu'''




      def __init__(					self :		Dict     ,					snake_case :		Union[str, Any]     ,					snake_case :		Any=13     ,					snake_case :		int=7     ,					snake_case :		Optional[int]=True     ,					snake_case :		str=False     ,					snake_case :		List[Any]=99     ,					snake_case :		Optional[int]=32     ,					snake_case :		Union[str, Any]=2     ,					snake_case :		Tuple=4     ,					snake_case :		Union[str, Any]=37     ,					snake_case :		Any=0.1     ,					snake_case :		str=0.1     ,					snake_case :		Union[str, Any]=20     ,					snake_case :		int=2     ,					snake_case :		Optional[Any]=1     ,					snake_case :		int=0     ,					):



          """simple docstring"""





          _snake_case : Optional[int]												=		parent
          _snake_case : Tuple												=		batch_size
          _snake_case : List[Any]												=		seq_length
          _snake_case : Any												=		is_training
          _snake_case : Optional[int]												=		use_labels
          _snake_case : Tuple												=		vocab_size
          _snake_case : List[str]												=		hidden_size
          _snake_case : Union[str, Any]												=		num_hidden_layers
          _snake_case : Dict												=		num_attention_heads
          _snake_case : int												=		intermediate_size

          _snake_case : str												=		hidden_dropout_prob
          _snake_case : List[Any]												=		attention_probs_dropout_prob
          _snake_case : Optional[int]												=		max_position_embeddings
          _snake_case : List[Any]												=		eos_token_id
          _snake_case : List[str]												=		pad_token_id
          _snake_case : Tuple												=		bos_token_id




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Optional[int]												=		ids_tensor([self.batch_size, self.seq_length - 1]     ,					self.vocab_size   )
          _snake_case : Dict												=		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )     ,					1   )
          _snake_case : List[Any]												=		tf.concat([input_ids, eos_tensor]     ,					axis=1   )

          _snake_case : List[str]												=		ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size   )

          _snake_case : Dict												=		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     ,					)
          _snake_case : Optional[Any]												=		prepare_marian_inputs_dict(snake_case     ,					snake_case     ,					snake_case   )
          return config, inputs_dict




      def 	__UpperCAmelCase			(					self :		Optional[Any]     ,					snake_case :		Optional[Any]     ,					snake_case :		int   ):



          """simple docstring"""





          _snake_case : List[str]												=		TFMarianModel(config=snake_case   ).get_decoder()
          _snake_case : Optional[int]												=		inputs_dict['input_ids']

          _snake_case : Optional[Any]												=		input_ids[:1, :]
          _snake_case : List[Any]												=		inputs_dict['attention_mask'][:1, :]
          _snake_case : Tuple												=		inputs_dict['head_mask']
          _snake_case : Union[str, Any]												=		1

          # first forward pass
          _snake_case : Tuple												=		model(snake_case     ,					attention_mask=snake_case     ,					head_mask=snake_case     ,					use_cache=snake_case   )

          _snake_case					,							_snake_case : Union[str, Any]												=		outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          _snake_case : Optional[int]												=		ids_tensor((self.batch_size, 3)     ,					config.vocab_size   )
          _snake_case : Dict												=		tf.cast(ids_tensor((self.batch_size, 3)     ,					2   )     ,					tf.inta   )

          # append to next input_ids and
          _snake_case : Dict												=		tf.concat([input_ids, next_tokens]     ,					axis=-1   )
          _snake_case : Optional[Any]												=		tf.concat([attention_mask, next_attn_mask]     ,					axis=-1   )

          _snake_case : str												=		model(snake_case     ,					attention_mask=snake_case   )[0]
          _snake_case : int												=		model(snake_case     ,					attention_mask=snake_case     ,					past_key_values=snake_case   )[0]

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

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

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



def   lowerCamelCase__       (      a__			,      a__			,      a__			,      a__=None			,      a__=None			,      a__=None			,      a__=None			,      a__=None			,      )		->    Optional[Any]:



    """simple docstring"""







    if attention_mask is None:
        _snake_case : Optional[int]												=		tf.cast(tf.math.not_equal(a__			,      config.pad_token_id)			,      tf.inta)
    if decoder_attention_mask is None:
        _snake_case : Optional[Any]												=		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:
        _snake_case : List[Any]												=		tf.ones((config.encoder_layers, config.encoder_attention_heads))
    if decoder_head_mask is None:
        _snake_case : str												=		tf.ones((config.decoder_layers, config.decoder_attention_heads))
    if cross_attn_head_mask is None:
        _snake_case : Optional[int]												=		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 			SCREAMING_SNAKE_CASE					(							lowercase_      ,lowercase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Tuple    =				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      SCREAMING_SNAKE_CASE__ :				List[str]    =				(TFMarianMTModel,) if is_tf_available() else ()
      SCREAMING_SNAKE_CASE__ :				Any    =				(
          {
              '''conversational''': TFMarianMTModel,
              '''feature-extraction''': TFMarianModel,
              '''summarization''': TFMarianMTModel,
              '''text2text-generation''': TFMarianMTModel,
              '''translation''': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      SCREAMING_SNAKE_CASE__ :				str    =				True
      SCREAMING_SNAKE_CASE__ :				Dict    =				False
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				False




      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          _snake_case : Any												=		TFMarianModelTester(self   )
          _snake_case : Tuple												=		ConfigTester(self     ,					config_class=snake_case   )




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          self.config_tester.run_common_tests()




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          _snake_case : str												=		self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*snake_case   )



@require_tf
class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Dict    =				1_000  # show more chars for failing integration tests




      @classmethod
      def 	__UpperCAmelCase			(					cls :		str   ):



          """simple docstring"""





          _snake_case : Dict												=		F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
          return cls




      @cached_property
      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          return AutoTokenizer.from_pretrained(self.model_name   )




      @property
      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          return self.tokenizer.eos_token_id




      @cached_property
      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          warnings.simplefilter('error'   )
          _snake_case : TFMarianMTModel												=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
          assert isinstance(snake_case     ,					snake_case   )
          _snake_case : Optional[int]												=		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 	__UpperCAmelCase			(					self :		Dict     ,					**snake_case :		Dict   ):



          """simple docstring"""





          _snake_case : Optional[int]												=		self.translate_src_text(**snake_case   )
          self.assertListEqual(self.expected_text     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]     ,					**snake_case :		Tuple   ):



          """simple docstring"""





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



@require_sentencepiece
@require_tokenizers
@require_tf
class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






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




      @unittest.skip('Skipping until #12647 is resolved.'   )
      @slow
      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				str    =				'''en'''
      SCREAMING_SNAKE_CASE__ :				str    =				'''zh'''
      SCREAMING_SNAKE_CASE__ :				Optional[int]    =				['''My name is Wolfgang and I live in Berlin''']
      SCREAMING_SNAKE_CASE__ :				Dict    =				['''我叫沃尔夫冈 我住在柏林''']




      @unittest.skip('Skipping until #12647 is resolved.'   )
      @slow
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				List[str]    =				'''en'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''ROMANCE'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				[
          '''>>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__ :				Optional[int]    =				[
          '''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 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          self._assert_generated_batch_equal_expected()




      @unittest.skip('Skipping until #12647 is resolved.'   )
      @slow
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          _snake_case : int												=		TranslationPipeline(self.model     ,					self.tokenizer     ,					framework='tf'   )
          _snake_case : Union[str, Any]												=		pipeline(self.src_text   )
          self.assertEqual(self.expected_text     ,					[x['translation_text'] for x in output]   )






'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   SCREAMING_SNAKE_CASE_    =   False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''      ,)
@require_vision
@require_torch
class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : int												=		tempfile.mkdtemp()

          _snake_case : Optional[int]												=		PixaStructImageProcessor()
          _snake_case : Dict												=		TaTokenizer.from_pretrained('t5-small'   )

          _snake_case : Optional[Any]												=		PixaStructProcessor(snake_case     ,					snake_case   )

          processor.save_pretrained(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]     ,					**snake_case :		Any   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).tokenizer




      def 	__UpperCAmelCase			(					self :		int     ,					**snake_case :		Optional[int]   ):



          """simple docstring"""





          return AutoProcessor.from_pretrained(self.tmpdirname     ,					**snake_case   ).image_processor




      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          shutil.rmtree(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Tuple   ):



          """simple docstring"""





          _snake_case : List[Any]												=		[np.random.randint(255     ,					size=(3, 30, 400)     ,					dtype=np.uinta   )]

          _snake_case : List[str]												=		[Image.fromarray(np.moveaxis(snake_case     ,					0     ,					-1   )   ) for x in image_inputs]

          return image_inputs




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=self.get_tokenizer()     ,					image_processor=self.get_image_processor()   )
          processor.save_pretrained(self.tmpdirname   )

          _snake_case : Tuple												=		self.get_tokenizer(bos_token='(BOS)'     ,					eos_token='(EOS)'   )
          _snake_case : Dict												=		self.get_image_processor(do_normalize=snake_case     ,					padding_value=1.0   )

          _snake_case : int												=		PixaStructProcessor.from_pretrained(
              self.tmpdirname     ,					bos_token='(BOS)'     ,					eos_token='(EOS)'     ,					do_normalize=snake_case     ,					padding_value=1.0   )

          self.assertEqual(processor.tokenizer.get_vocab()     ,					tokenizer_add_kwargs.get_vocab()   )
          self.assertIsInstance(processor.tokenizer     ,					snake_case   )

          self.assertEqual(processor.image_processor.to_json_string()     ,					image_processor_add_kwargs.to_json_string()   )
          self.assertIsInstance(processor.image_processor     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : Optional[Any]												=		self.get_tokenizer()

          _snake_case : Optional[int]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		image_processor(snake_case     ,					return_tensors='np'   )
          _snake_case : Tuple												=		processor(images=snake_case     ,					return_tensors='np'   )

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,					input_processor[key].sum()     ,					delta=1e-2   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : Optional[Any]												=		self.get_image_processor()
          _snake_case : Union[str, Any]												=		self.get_tokenizer()

          _snake_case : str												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[str]												=		'lower newer'

          _snake_case : Dict												=		processor(text=snake_case   )

          _snake_case : Any												=		tokenizer(snake_case     ,					return_token_type_ids=snake_case     ,					add_special_tokens=snake_case   )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,					encoded_processor[key]   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : int												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : Any												=		self.prepare_image_inputs()

          _snake_case : Any												=		processor(text=snake_case     ,					images=snake_case   )

          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          # test if it raises when no input is passed
          with pytest.raises(snake_case   ):
              processor()




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : int												=		self.get_image_processor()
          _snake_case : List[str]												=		self.get_tokenizer()

          _snake_case : Optional[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Optional[Any]												=		'lower newer'
          _snake_case : str												=		self.prepare_image_inputs()

          _snake_case : Optional[Any]												=		processor(text=snake_case     ,					images=snake_case   )

          _snake_case : Tuple												=		[512, 1024, 2048, 4096]
          _snake_case : str												=		[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : List[Any]												=		processor(text=snake_case     ,					images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )

          # without text input
          for i, max_patch in enumerate(snake_case   ):
              _snake_case : Union[str, Any]												=		processor(images=snake_case     ,					max_patches=snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[0]     ,					snake_case   )
              self.assertEqual(inputs['flattened_patches'][0].shape[1]     ,					expected_hidden_size[i]   )




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.get_image_processor()
          _snake_case : List[Any]												=		self.get_tokenizer()

          _snake_case : List[Any]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : List[Any]												=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _snake_case : Dict												=		processor.batch_decode(snake_case   )
          _snake_case : List[Any]												=		tokenizer.batch_decode(snake_case   )

          self.assertListEqual(snake_case     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : str												=		self.get_image_processor()
          _snake_case : Optional[int]												=		self.get_tokenizer()

          _snake_case : List[str]												=		PixaStructProcessor(tokenizer=snake_case     ,					image_processor=snake_case   )

          _snake_case : Tuple												=		'lower newer'
          _snake_case : List[Any]												=		self.prepare_image_inputs()

          _snake_case : Tuple												=		processor(text=snake_case     ,					images=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(
              list(inputs.keys()   )     ,					['flattened_patches', 'attention_mask', 'decoder_attention_mask', 'decoder_input_ids']   )

          _snake_case : str												=		processor(text=snake_case   )

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(list(inputs.keys()   )     ,					['input_ids', 'attention_mask']   )



