






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class  lowerCAmelCase_       (				unittest.TestCase					):




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						tempfile.mkdtemp()

											lowerCAmelCase__      			=						BlipImageProcessor()
											lowerCAmelCase__      			=						GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''	)

											lowerCAmelCase__      			=						BlipaProcessor(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)

											processor.save_pretrained(self.tmpdirname	)




							def 						__snake_case					(   self     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   int	):
											return AutoProcessor.from_pretrained(self.tmpdirname ,							**SCREAMING_SNAKE_CASE_	).tokenizer




							def 						__snake_case					(   self     :   List[Any] ,							**SCREAMING_SNAKE_CASE_     :   Any	):
											return AutoProcessor.from_pretrained(self.tmpdirname ,							**SCREAMING_SNAKE_CASE_	).image_processor




							def 						__snake_case					(   self     :   Union[str, Any]	):
											shutil.rmtree(self.tmpdirname	)




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

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

											return image_inputs




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						BlipaProcessor(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=SCREAMING_SNAKE_CASE_ ,							padding_value=1.0	)

											lowerCAmelCase__      			=						BlipaProcessor.from_pretrained(
											    self.tmpdirname ,							bos_token='''(BOS)''' ,							eos_token='''(EOS)''' ,							do_normalize=SCREAMING_SNAKE_CASE_ ,							padding_value=1.0	)

											self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer_add_kwargs.get_vocab()	)
											self.assertIsInstance(processor.tokenizer ,							SCREAMING_SNAKE_CASE_	)

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




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self.get_image_processor()
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						self.prepare_image_inputs()

											lowerCAmelCase__      			=						image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors='''np'''	)
											lowerCAmelCase__      			=						processor(images=SCREAMING_SNAKE_CASE_ ,							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 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						self.get_image_processor()
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						'''lower newer'''

											lowerCAmelCase__      			=						processor(text=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						tokenizer(SCREAMING_SNAKE_CASE_ ,							return_token_type_ids=SCREAMING_SNAKE_CASE_	)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key] ,							encoded_processor[key]	)




							def 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						self.get_image_processor()
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						'''lower newer'''
											lowerCAmelCase__      			=						self.prepare_image_inputs()

											lowerCAmelCase__      			=						processor(text=SCREAMING_SNAKE_CASE_ ,							images=SCREAMING_SNAKE_CASE_	)

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

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




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						self.get_image_processor()
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_	)

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

											lowerCAmelCase__      			=						processor.batch_decode(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

											self.assertListEqual(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						self.get_image_processor()
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE_ ,							image_processor=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						'''lower newer'''
											lowerCAmelCase__      			=						self.prepare_image_inputs()

											lowerCAmelCase__      			=						processor(text=SCREAMING_SNAKE_CASE_ ,							images=SCREAMING_SNAKE_CASE_	)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys()	) ,							['''pixel_values''', '''input_ids''', '''attention_mask''']	)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:		List[str]   				=    {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    ["ElectraTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[Any]   				=    [
			    "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ElectraForCausalLM",
			    "ElectraForMaskedLM",
			    "ElectraForMultipleChoice",
			    "ElectraForPreTraining",
			    "ElectraForQuestionAnswering",
			    "ElectraForSequenceClassification",
			    "ElectraForTokenClassification",
			    "ElectraModel",
			    "ElectraPreTrainedModel",
			    "load_tf_weights_in_electra",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFElectraForMaskedLM",
			    "TFElectraForMultipleChoice",
			    "TFElectraForPreTraining",
			    "TFElectraForQuestionAnswering",
			    "TFElectraForSequenceClassification",
			    "TFElectraForTokenClassification",
			    "TFElectraModel",
			    "TFElectraPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[int]   				=    [
			    "FlaxElectraForCausalLM",
			    "FlaxElectraForMaskedLM",
			    "FlaxElectraForMultipleChoice",
			    "FlaxElectraForPreTraining",
			    "FlaxElectraForQuestionAnswering",
			    "FlaxElectraForSequenceClassification",
			    "FlaxElectraForTokenClassification",
			    "FlaxElectraModel",
			    "FlaxElectraPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
			from .tokenization_electra import ElectraTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_electra_fast import ElectraTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_electra import (
						    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ElectraForCausalLM,
						    ElectraForMaskedLM,
						    ElectraForMultipleChoice,
						    ElectraForPreTraining,
						    ElectraForQuestionAnswering,
						    ElectraForSequenceClassification,
						    ElectraForTokenClassification,
						    ElectraModel,
						    ElectraPreTrainedModel,
						    load_tf_weights_in_electra,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_electra import (
						    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFElectraForMaskedLM,
						    TFElectraForMultipleChoice,
						    TFElectraForPreTraining,
						    TFElectraForQuestionAnswering,
						    TFElectraForSequenceClassification,
						    TFElectraForTokenClassification,
						    TFElectraModel,
						    TFElectraPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						    FlaxElectraPreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Any   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





