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 						a__							(       unittest.TestCase		):
			A__    :       Optional[Any]        =      ViTImageProcessor if is_vision_available() else None



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       (3, 3_2, 1_2_8)
								__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(UpperCAmelCase				,						range(len(UpperCAmelCase   )   )   )   )

								__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(UpperCAmelCase   ) + '\n'   )

								__a		 =       {
								    'do_normalize': False,
								    'do_resize': True,
								    'image_processor_type': 'ViTImageProcessor',
								    'resample': 3,
								    'size': {'height': 3_2, 'width': 1_2_8},
								}
								__a		 =       os.path.join(self.tmpdirname				,						UpperCAmelCase   )
								with open(self.image_processor_file				,						'w'				,						encoding='utf-8'   ) as fp:
													json.dump(UpperCAmelCase				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Any:
								return ViTImageProcessor.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								shutil.rmtree(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta   )

								__a		 =       Image.fromarray(np.moveaxis(UpperCAmelCase				,						0				,						-1   )   )

								return image_input



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       self.get_tokenizer()
								__a		 =       self.get_image_processor()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )
								processor.save_pretrained(self.tmpdirname   )
								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.get_tokenizer()
								__a		 =       self.get_image_processor()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )
								processor.save_pretrained(self.tmpdirname   )

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

								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       self.prepare_image_inputs()

								__a		 =       image_processor(UpperCAmelCase				,						return_tensors='np'   )
								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       'test'

								__a		 =       processor(text=UpperCAmelCase   )

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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

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

								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__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(UpperCAmelCase   )
								__a		 =       tokenizer.batch_decode(UpperCAmelCase   )
								__a		 =       [seq.replace(' '				,						''   ) for seq in decoded_tok]

								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       None
								__a		 =       self.prepare_image_inputs()

								__a		 =       processor(text=UpperCAmelCase				,						images=UpperCAmelCase   )

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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_image_processor()
								__a		 =       self.get_tokenizer()

								__a		 =       MgpstrProcessor(tokenizer=UpperCAmelCase				,						image_processor=UpperCAmelCase   )

								__a		 =       torch.randn(1				,						2_7				,						3_8   )
								__a		 =       torch.randn(1				,						2_7				,						5_0_2_5_7   )
								__a		 =       torch.randn(1				,						2_7				,						3_0_5_2_2   )

								__a		 =       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_flax_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""SpeechEncoderDecoderModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




