


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      __lowerCamelCase       (unittest.TestCase		):
       _lowercase											=			ViTImageProcessor if is_vision_available() else None







       @property
       def 						snake_case_     (       self:      str ):






            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()







       def 						snake_case_     (       self:      Dict ):






            '''simple docstring'''




            __UpperCamelCase      =       (3, 32, 128)
            __UpperCamelCase      =       tempfile.mkdtemp()

            # fmt: off
            __UpperCamelCase      =       ['[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
            __UpperCamelCase      =       dict(zip(_A,range(len(_A ) ) ) )

            __UpperCamelCase      =       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(_A ) + '\n' )

            __UpperCamelCase      =       {
                'do_normalize': False,
                'do_resize': True,
                'image_processor_type': 'ViTImageProcessor',
                'resample': 3,
                'size': {'height': 32, 'width': 128},
            }
            __UpperCamelCase      =       os.path.join(self.tmpdirname,_A )
            with open(self.image_processor_file,'w',encoding='utf-8' ) as fp:
                 json.dump(_A,_A )







       def 						snake_case_     (       self:      str,**A_:      Any ):






            '''simple docstring'''




            return MgpstrTokenizer.from_pretrained(self.tmpdirname,**_A )







       def 						snake_case_     (       self:      Any,**A_:      Dict ):






            '''simple docstring'''




            return ViTImageProcessor.from_pretrained(self.tmpdirname,**_A )







       def 						snake_case_     (       self:      str ):






            '''simple docstring'''




            shutil.rmtree(self.tmpdirname )







       def 						snake_case_     (       self:      Optional[int] ):






            '''simple docstring'''




            __UpperCamelCase      =       np.random.randint(255,size=(3, 30, 400),dtype=np.uinta )

            __UpperCamelCase      =       Image.fromarray(np.moveaxis(_A,0,-1 ) )

            return image_input







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_tokenizer()
            __UpperCamelCase      =       self.get_image_processor()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )
            processor.save_pretrained(self.tmpdirname )
            __UpperCamelCase      =       MgpstrProcessor.from_pretrained(self.tmpdirname,use_fast=_A )

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

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







       def 						snake_case_     (       self:      Optional[int] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_tokenizer()
            __UpperCamelCase      =       self.get_image_processor()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )
            processor.save_pretrained(self.tmpdirname )

            __UpperCamelCase      =       self.get_tokenizer(bos_token='(BOS)',eos_token='(EOS)' )
            __UpperCamelCase      =       self.get_image_processor(do_normalize=_A,padding_value=1.0 )

            __UpperCamelCase      =       MgpstrProcessor.from_pretrained(
                self.tmpdirname,bos_token='(BOS)',eos_token='(EOS)',do_normalize=_A,padding_value=1.0 )

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

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







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






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

            __UpperCamelCase      =       self.prepare_image_inputs()

            __UpperCamelCase      =       image_processor(_A,return_tensors='np' )
            __UpperCamelCase      =       processor(images=_A,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 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

            __UpperCamelCase      =       'test'

            __UpperCamelCase      =       processor(text=_A )

            __UpperCamelCase      =       tokenizer(_A )
            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key],encoded_processor[key] )







       def 						snake_case_     (       self:      str ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

            __UpperCamelCase      =       'test'
            __UpperCamelCase      =       self.prepare_image_inputs()

            __UpperCamelCase      =       processor(text=_A,images=_A )

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

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







       def 						snake_case_     (       self:      str ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

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

            __UpperCamelCase      =       processor.char_decode(_A )
            __UpperCamelCase      =       tokenizer.batch_decode(_A )
            __UpperCamelCase      =       [seq.replace(' ','' ) for seq in decoded_tok]

            self.assertListEqual(_A,_A )







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

            __UpperCamelCase      =       None
            __UpperCamelCase      =       self.prepare_image_inputs()

            __UpperCamelCase      =       processor(text=_A,images=_A )

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







       def 						snake_case_     (       self:      Dict ):






            '''simple docstring'''




            __UpperCamelCase      =       self.get_image_processor()
            __UpperCamelCase      =       self.get_tokenizer()

            __UpperCamelCase      =       MgpstrProcessor(tokenizer=_A,image_processor=_A )

            __UpperCamelCase      =       torch.randn(1,27,38 )
            __UpperCamelCase      =       torch.randn(1,27,5_0257 )
            __UpperCamelCase      =       torch.randn(1,27,3_0522 )

            __UpperCamelCase      =       processor.batch_decode([char_input, bpe_input, wp_input] )

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__A				:    Union[str, Any]       =   {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2TextForConditionalGeneration",
				    "Speech2TextModel",
				    "Speech2TextPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
				from .processing_speech_to_text import SpeechaTextProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speech_to_text import SpeechaTextTokenizer

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_speech_to_text import (
								    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSpeechaTextForConditionalGeneration,
								    TFSpeechaTextModel,
								    TFSpeechaTextPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text import (
								    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextForConditionalGeneration,
								    SpeechaTextModel,
								    SpeechaTextPreTrainedModel,
								)

else:
				import sys

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



