"""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    __UpperCAmelCase						(	unittest.TestCase	):

				def 		_a	(      self      ):
										lowerCamelCase__					=tempfile.mkdtemp()

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

										lowerCamelCase__					=PixaStructProcessor(_lowerCamelCase	, _lowerCamelCase      )

										processor.save_pretrained(self.tmpdirname      )

				def 		_a	(      self	, **_lowerCamelCase      ):
										return AutoProcessor.from_pretrained(self.tmpdirname	, **_lowerCamelCase      ).tokenizer

				def 		_a	(      self	, **_lowerCamelCase      ):
										return AutoProcessor.from_pretrained(self.tmpdirname	, **_lowerCamelCase      ).image_processor

				def 		_a	(      self      ):
										shutil.rmtree(self.tmpdirname      )

				def 		_a	(      self      ):
										lowerCamelCase__					=[np.random.randint(255	, size=(3, 30, 400)	, dtype=np.uinta      )]

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

										return image_inputs

				def 		_a	(      self      ):
										lowerCamelCase__					=PixaStructProcessor(tokenizer=self.get_tokenizer()	, image_processor=self.get_image_processor()      )
										processor.save_pretrained(self.tmpdirname      )

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

										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

										lowerCamelCase__					=self.prepare_image_inputs()

										lowerCamelCase__					=image_processor(_lowerCamelCase	, return_tensors="np"      )
										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

										lowerCamelCase__					="lower newer"

										lowerCamelCase__					=processor(text=_lowerCamelCase      )

										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

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

										lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

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

										lowerCamelCase__					=processor(text=_lowerCamelCase	, images=_lowerCamelCase      )

										lowerCamelCase__					=[512, 1024, 2048, 4096]
										lowerCamelCase__					=[770, 770, 770, 770]
										# with text
										for i, max_patch in enumerate(_lowerCamelCase      ):
																lowerCamelCase__					=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      ):
																lowerCamelCase__					=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 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

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

										lowerCamelCase__					=processor.batch_decode(_lowerCamelCase      )
										lowerCamelCase__					=tokenizer.batch_decode(_lowerCamelCase      )

										self.assertListEqual(_lowerCamelCase	, _lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=self.get_image_processor()
										lowerCamelCase__					=self.get_tokenizer()

										lowerCamelCase__					=PixaStructProcessor(tokenizer=_lowerCamelCase	, image_processor=_lowerCamelCase      )

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

										lowerCamelCase__					=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"]      )

										lowerCamelCase__					=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"]      )



"""simple docstring"""


import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY






@is_pipeline_test
class    __UpperCAmelCase						(	unittest.TestCase	):
				A__      :   Optional[Any]  							=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				A__      :   int  							=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase	, _lowerCamelCase      ):
										if isinstance(model.config	, _lowerCamelCase      ):
																lowerCamelCase__					,      lowerCamelCase__					=list(tokenizer.lang_code_to_id.keys()      )[:2]
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang=_lowerCamelCase	, tgt_lang=_lowerCamelCase      )
										else:
																lowerCamelCase__					=TranslationPipeline(model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										return translator, ["Some string", "Some other text"]

				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=translator("Some string"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}]      )

										lowerCamelCase__					=translator(["Some string", "other string"]      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": ANY(_lowerCamelCase      )}, {"translation_text": ANY(_lowerCamelCase      )}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_ro"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
										                " Beide Beide"
										            )
										        }
										    ]	, )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="pt"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






				@require_tf
				def 		_a	(      self      ):
										lowerCamelCase__					=pipeline("translation_en_to_de"	, model="patrickvonplaten/t5-tiny-random"	, framework="tf"      )
										lowerCamelCase__					=translator("This is a test string"	, max_length=20      )
										self.assertEqual(
										    _lowerCamelCase	, [
										        {
										            "translation_text": (
										                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
										                " urine urine urine urine urine urine urine"
										            )
										        }
										    ]	, )






class    __UpperCAmelCase						(	unittest.TestCase	):

				@require_torch
				@slow
				def 		_a	(      self      ):
										# We don't provide a default for this pair
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation_cn_to_ar"      )

										# but we do for this one
										lowerCamelCase__					=pipeline(task="translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )

				@require_torch
				@slow
				def 		_a	(      self      ):
										lowerCamelCase__					=MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )
										lowerCamelCase__					=MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"      )

										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase      )
										# Missing src_lang, tgt_lang
										with self.assertRaises(_lowerCamelCase      ):
																translator("This is a test"      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

										lowerCamelCase__					=translator("This is a test"	, src_lang="en_XX"	, tgt_lang="hi_IN"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "यह एक परीक्षण है"}]      )

										# src_lang, tgt_lang can be defined at pipeline call time
										lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase	, tokenizer=_lowerCamelCase	, src_lang="en_XX"	, tgt_lang="ar_AR"      )
										lowerCamelCase__					=translator("This is a test"      )
										self.assertEqual(_lowerCamelCase	, [{"translation_text": "هذا إختبار"}]      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										lowerCamelCase__					=pipeline(task="translation_cn_to_ar"	, model=_lowerCamelCase      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "cn"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "ar"      )

				@require_torch
				def 		_a	(      self      ):
										lowerCamelCase__					="patrickvonplaten/t5-tiny-random"
										with pytest.warns(_lowerCamelCase	, match=r".*translation_en_to_de.*"      ):
																lowerCamelCase__					=pipeline(task="translation"	, model=_lowerCamelCase      )
										self.assertEqual(translator.task	, "translation_en_to_de"      )
										self.assertEqual(translator._preprocess_params["src_lang"]	, "en"      )
										self.assertEqual(translator._preprocess_params["tgt_lang"]	, "de"      )






				@require_torch
				def 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ):
																pipeline(task="translation"      )


