



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 :
				'''simple docstring'''







				lowercase__     =    MarianConfig
				lowercase__     =    {}
				lowercase__     =    """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  ,			):

									'''simple docstring'''


									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     ):

									'''simple docstring'''


									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_marian_inputs_dict(snake_case__  ,			snake_case__  ,			snake_case__     )
									return config, inputs_dict

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= TFMarianModel(config=snake_case__     ).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(snake_case__  ,			attention_mask=snake_case__  ,			head_mask=snake_case__  ,			use_cache=snake_case__     )

									UpperCamelCase_	,     UpperCamelCase_			= outputs.to_tuple()

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

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

									UpperCamelCase_			= model(snake_case__  ,			attention_mask=snake_case__     )[0]
									UpperCamelCase_			= 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
									UpperCamelCase_			= int(ids_tensor((1,)  ,			output_from_past.shape[-1]     )     )
									UpperCamelCase_			= output_from_no_past[:, -3:, random_slice_idx]
									UpperCamelCase_			= 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     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						):
					if attention_mask is None:
										UpperCamelCase_			= tf.cast(tf.math.not_equal(_lowerCAmelCase ,						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 (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= TFMarianModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self.config_tester.run_common_tests()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*snake_case__     )




@require_tf
class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







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

				@classmethod
				def    _lowerCamelCase       (			cls     ):

									'''simple docstring'''


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

				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name     )

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return self.tokenizer.eos_token_id

				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									warnings.simplefilter("error"     )
									UpperCamelCase_			= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
									assert isinstance(snake_case__  ,			snake_case__     )
									UpperCamelCase_			= 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    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.translate_src_text(**snake_case__     )
									self.assertListEqual(self.expected_text  ,			snake_case__     )

				def    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.tokenizer(self.src_text  ,			**snake_case__  ,			padding=snake_case__  ,			return_tensors="tf"     )
									UpperCamelCase_			= self.model.generate(
									    model_inputs.input_ids  ,			attention_mask=model_inputs.attention_mask  ,			num_beams=2  ,			max_length=128     )
									UpperCamelCase_			= self.tokenizer.batch_decode(generated_ids.numpy()  ,			skip_special_tokens=snake_case__     )
									return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







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

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

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







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

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

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    """en"""
				lowercase__     =    """ROMANCE"""
				lowercase__     =    [
				    """>>fr<< Don't spend so much time watching TV.""",
				    """>>pt<< Your message has been sent.""",
				    """>>es<< He's two years older than me.""",
				]
				lowercase__     =    [
				    """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    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()

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

									'''simple docstring'''


									UpperCamelCase_			= TranslationPipeline(self.model  ,			self.tokenizer  ,			framework="tf"     )
									UpperCamelCase_			= 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:
     A							     = 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 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				PixaStructImageProcessor()
        snake_case_    							=				TaTokenizer.from_pretrained("t5-small"     )

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

        snake_case_    							=				[Image.fromarray(np.moveaxis(lowerCAmelCase_						,			0						,			-1     )     ) for x in image_inputs]

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_image_processor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				PixaStructProcessor.from_pretrained(
            self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

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

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

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(images=lowerCAmelCase_						,			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 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_						,			return_token_type_ids=lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        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(lowerCAmelCase_     ):
              processor()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        snake_case_    							=				[5_12, 10_24, 20_48, 40_96]
        snake_case_    							=				[7_70, 7_70, 7_70, 7_70]
        # with text
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

        # without text input
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				processor(images=lowerCAmelCase_						,			max_patches=lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[0]						,			lowerCAmelCase_     )
              self.assertEqual(inputs["flattened_patches"][0].shape[1]						,			expected_hidden_size[i]     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        # 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_    							=				processor(text=lowerCAmelCase_     )

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

