


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class 							lowercase  (						unittest.TestCase			):
     lowerCamelCase			:			Optional[Any]      = ViTImageProcessor if is_vision_available() else None







     @property
     def 							lowercase__       (						self :						int      ):
           return self.image_processor_tester.prepare_image_processor_dict()







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		(3, 32, 1_28)
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		tempfile.mkdtemp()

           # fmt: off
           SCREAMING_SNAKE_CASE__       :  int						     =		["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
           # fmt: on
           SCREAMING_SNAKE_CASE__       :  Dict						     =		dict(zip(__UpperCAmelCase   ,   range(len(__UpperCAmelCase      )      )      )      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES['''vocab_file''']      )
           with open(self.vocab_file   ,   '''w'''   ,   encoding='''utf-8'''      ) as fp:
                 fp.write(json.dumps(__UpperCAmelCase      ) + '''\n'''      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		{
               "do_normalize": False,
               "do_resize": True,
               "image_processor_type": "ViTImageProcessor",
               "resample": 3,
               "size": {"height": 32, "width": 1_28},
           }
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		os.path.join(self.tmpdirname   ,   __UpperCAmelCase      )
           with open(self.image_processor_file   ,   '''w'''   ,   encoding='''utf-8'''      ) as fp:
                 json.dump(__UpperCAmelCase   ,   __UpperCAmelCase      )







     def 							lowercase__       (						self :						Tuple   ,   **_lowercase :						Optional[Any]      ):
           return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,   **__UpperCAmelCase      )







     def 							lowercase__       (						self :						Optional[int]   ,   **_lowercase :						Any      ):
           return ViTImageProcessor.from_pretrained(self.tmpdirname   ,   **__UpperCAmelCase      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           shutil.rmtree(self.tmpdirname      )







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		np.random.randint(2_55   ,   size=(3, 30, 4_00)   ,   dtype=np.uinta      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		Image.fromarray(np.moveaxis(__UpperCAmelCase   ,   0   ,   -1      )      )

           return image_input







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.get_tokenizer()
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.get_image_processor()

           SCREAMING_SNAKE_CASE__       :  int						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )
           processor.save_pretrained(self.tmpdirname      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		MgpstrProcessor.from_pretrained(self.tmpdirname   ,   use_fast=__UpperCAmelCase      )

           self.assertEqual(processor.char_tokenizer.get_vocab()   ,   tokenizer.get_vocab()      )
           self.assertIsInstance(processor.char_tokenizer   ,   __UpperCAmelCase      )

           self.assertEqual(processor.image_processor.to_json_string()   ,   image_processor.to_json_string()      )
           self.assertIsInstance(processor.image_processor   ,   __UpperCAmelCase      )







     def 							lowercase__       (						self :						List[str]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.get_tokenizer()
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_image_processor()

           SCREAMING_SNAKE_CASE__       :  Dict						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )
           processor.save_pretrained(self.tmpdirname      )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.get_tokenizer(bos_token='''(BOS)'''   ,   eos_token='''(EOS)'''      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.get_image_processor(do_normalize=__UpperCAmelCase   ,   padding_value=1.0      )

           SCREAMING_SNAKE_CASE__       :  Any						     =		MgpstrProcessor.from_pretrained(
               self.tmpdirname   ,   bos_token='''(BOS)'''   ,   eos_token='''(EOS)'''   ,   do_normalize=__UpperCAmelCase   ,   padding_value=1.0      )

           self.assertEqual(processor.char_tokenizer.get_vocab()   ,   tokenizer_add_kwargs.get_vocab()      )
           self.assertIsInstance(processor.char_tokenizer   ,   __UpperCAmelCase      )

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







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  int						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  Dict						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		image_processor(__UpperCAmelCase   ,   return_tensors='''np'''      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		processor(images=__UpperCAmelCase   ,   return_tensors='''np'''      )

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







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		"test"

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		processor(text=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		tokenizer(__UpperCAmelCase      )
           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]   ,   encoded_processor[key]      )







     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  int						     =		"test"
           SCREAMING_SNAKE_CASE__       :  int						     =		self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__       :  Dict						     =		processor(text=__UpperCAmelCase   ,   images=__UpperCAmelCase      )

           self.assertListEqual(list(inputs.keys()      )   ,   ['''pixel_values''', '''labels''']      )

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







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE__       :  int						     =		processor.char_decode(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		tokenizer.batch_decode(__UpperCAmelCase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[seq.replace(''' '''   ,   ''''''      ) for seq in decoded_tok]

           self.assertListEqual(__UpperCAmelCase   ,   __UpperCAmelCase      )







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		None
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		processor(text=__UpperCAmelCase   ,   images=__UpperCAmelCase      )

           self.assertListEqual(list(inputs.keys()      )   ,   processor.model_input_names      )







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		self.get_image_processor()
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self.get_tokenizer()

           SCREAMING_SNAKE_CASE__       :  str						     =		MgpstrProcessor(tokenizer=__UpperCAmelCase   ,   image_processor=__UpperCAmelCase      )

           SCREAMING_SNAKE_CASE__       :  int						     =		torch.randn(1   ,   27   ,   38      )
           SCREAMING_SNAKE_CASE__       :  str						     =		torch.randn(1   ,   27   ,   5_02_57      )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.randn(1   ,   27   ,   3_05_22      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		processor.batch_decode([char_input, bpe_input, wp_input]      )

           self.assertListEqual(list(results.keys()      )   ,   ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']      )














import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





