
"""simple docstring"""





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      ):




			"""simple docstring"""







			snake_case	      =				ViTImageProcessor if is_vision_available() else None




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						(3, 32, 128)
								A_				 =						tempfile.mkdtemp()

								# fmt: off
								A_				 =						["[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
								A_				 =						dict(zip(_snake_case       ,   range(len(_snake_case	)	)	)	)

								A_				 =						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(_snake_case	) + "\n"	)

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




			def      lowerCamelCase__     (				self						:					Tuple       ,   **_snake_case						:					List[str]	)      -> int:







								"""simple docstring"""
								return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   **_snake_case						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								return ViTImageProcessor.from_pretrained(self.tmpdirname       ,   **_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								shutil.rmtree(self.tmpdirname	)




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						np.random.randint(255       ,   size=(3, 30, 400)       ,   dtype=np.uinta	)

								A_				 =						Image.fromarray(np.moveaxis(_snake_case       ,   0       ,   -1	)	)

								return image_input




			def      lowerCamelCase__     (				self						:					str	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						self.get_image_processor()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)
								processor.save_pretrained(self.tmpdirname	)
								A_				 =						MgpstrProcessor.from_pretrained(self.tmpdirname       ,   use_fast=_snake_case	)

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

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




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.get_tokenizer()
								A_				 =						self.get_image_processor()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)
								processor.save_pretrained(self.tmpdirname	)

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

								A_				 =						MgpstrProcessor.from_pretrained(
								    self.tmpdirname       ,   bos_token="(BOS)"       ,   eos_token="(EOS)"       ,   do_normalize=_snake_case       ,   padding_value=1.0	)

								self.assertEqual(processor.char_tokenizer.get_vocab()       ,   tokenizer_add_kwargs.get_vocab()	)
								self.assertIsInstance(processor.char_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      lowerCamelCase__     (				self						:					List[Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						self.prepare_image_inputs()

								A_				 =						image_processor(_snake_case       ,   return_tensors="np"	)
								A_				 =						processor(images=_snake_case       ,   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      lowerCamelCase__     (				self						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						"test"

								A_				 =						processor(text=_snake_case	)

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




			def      lowerCamelCase__     (				self						:					str	)      -> int:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						"test"
								A_				 =						self.prepare_image_inputs()

								A_				 =						processor(text=_snake_case       ,   images=_snake_case	)

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

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




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

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

								A_				 =						processor.char_decode(_snake_case	)
								A_				 =						tokenizer.batch_decode(_snake_case	)
								A_				 =						[seq.replace(" "       ,   ""	) for seq in decoded_tok]

								self.assertListEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						None
								A_				 =						self.prepare_image_inputs()

								A_				 =						processor(text=_snake_case       ,   images=_snake_case	)

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






			def      lowerCamelCase__     (				self						:					Dict	)      -> str:







								"""simple docstring"""
								A_				 =						self.get_image_processor()
								A_				 =						self.get_tokenizer()

								A_				 =						MgpstrProcessor(tokenizer=_snake_case       ,   image_processor=_snake_case	)

								A_				 =						torch.randn(1       ,   27       ,   38	)
								A_				 =						torch.randn(1       ,   27       ,   50_257	)
								A_				 =						torch.randn(1       ,   27       ,   30_522	)

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

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






"""simple docstring"""





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

UpperCamelCase_					:						Union[str, Any]				= random.Random()
def 							A_     (__a				,   __a=1.0				,   __a=None				,   __a=None				):

					'''simple docstring'''





					if rng is None:
										A_				 =						global_rng

					A_				 =						[]
					for batch_idx in range(shape[0]				):
										values.append([]				)
										for _ in range(shape[1]				):
															values[-1].append(rng.random() * scale				)

					return values



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					Union[str, Any]=7       ,   _snake_case						:					Any=400       ,   _snake_case						:					Tuple=2_000       ,   _snake_case						:					str=2_048       ,   _snake_case						:					List[Any]=128       ,   _snake_case						:					List[Any]=1       ,   _snake_case						:					Union[str, Any]=512       ,   _snake_case						:					List[str]=30       ,   _snake_case						:					List[str]=44_100       ,   )      -> Optional[int]:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						min_seq_length
								A_				 =						max_seq_length
								A_				 =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								A_				 =						spectrogram_length
								A_				 =						feature_size
								A_				 =						num_audio_channels
								A_				 =						hop_length
								A_				 =						chunk_length
								A_				 =						sampling_rate




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}






			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					str=False       ,   _snake_case						:					Optional[Any]=False	)      -> List[str]:







								"""simple docstring"""
								def _flatten(_snake_case						:					List[Any]	):
													return list(itertools.chain(*_snake_case	)	)

								if equal_length:
													A_				 =						[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
								else:
													# make sure that inputs increase in size
													A_				 =						[
													    floats_list((x, self.feature_size)	)
													    for x in range(self.min_seq_length       ,   self.max_seq_length       ,   self.seq_length_diff	)
													]
								if numpify:
													A_				 =						[np.asarray(_snake_case	) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				TvltFeatureExtractor




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TvltFeatureExtractionTester(self	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "spectrogram_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "feature_size"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "num_audio_channels"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "hop_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "chunk_length"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "sampling_rate"	)	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						feat_extract_first.save_pretrained(_snake_case	)[0]
													check_json_file_has_correct_format(_snake_case	)
													A_				 =						self.feature_extraction_class.from_pretrained(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								with tempfile.TemporaryDirectory() as tmpdirname:
													A_				 =						os.path.join(_snake_case       ,   "feat_extract.json"	)
													feat_extract_first.to_json_file(_snake_case	)
													A_				 =						self.feature_extraction_class.from_json_file(_snake_case	)

								A_				 =						feat_extract_first.to_dict()
								A_				 =						feat_extract_second.to_dict()
								A_				 =						dict_first.pop("mel_filters"	)
								A_				 =						dict_second.pop("mel_filters"	)
								self.assertTrue(np.allclose(_snake_case       ,   _snake_case	)	)
								self.assertEqual(_snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								# Initialize feature_extractor
								A_				 =						self.feature_extraction_class(**self.feat_extract_dict	)

								# create three inputs of length 800, 1000, and 1200
								A_				 =						[floats_list((1, x)	)[0] for x in range(800       ,   1_400       ,   200	)]
								A_				 =						[np.asarray(_snake_case	) for speech_input in speech_inputs]

								# Test not batched input
								A_				 =						feature_extractor(np_speech_inputs[0]       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test batched
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test audio masking
								A_				 =						feature_extractor(
								    _snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100       ,   mask_audio=_snake_case	).audio_values

								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)

								# Test 2-D numpy arrays are batched.
								A_				 =						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
								A_				 =						np.asarray(_snake_case	)
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="np"       ,   sampling_rate=44_100	).audio_values
								self.assertTrue(encoded_audios.ndim == 4	)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size	)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length	)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,   "clean"       ,   split="validation"	)
								# automatic decoding with librispeech
								A_				 =						ds.sort("id"	).select(range(_snake_case	)	)[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_				 =						self._load_datasamples(1	)
								A_				 =						TvltFeatureExtractor()
								A_				 =						feature_extractor(_snake_case       ,   return_tensors="pt"	).audio_values

								self.assertEquals(audio_values.shape       ,   (1, 1, 192, 128)	)

								A_				 =						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]	)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,   _snake_case       ,   atol=1e-4	)	)




