
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			tempfile.mkdtemp()

					lowerCamelCase_						=			BlipImageProcessor()
					lowerCamelCase_						=			GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model" )

					lowerCamelCase_						=			BlipaProcessor(lowercase						,  lowercase )

					processor.save_pretrained(self.tmpdirname )



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       Union[str, Any]:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).tokenizer



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       str:
					return AutoProcessor.from_pretrained(self.tmpdirname						,  **lowercase ).image_processor



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



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			[np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta )]

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

					return image_inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[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=lowercase						,  padding_value=1.0 )

					lowerCamelCase_						=			BlipaProcessor.from_pretrained(
					    self.tmpdirname						,  bos_token="(BOS)"						,  eos_token="(EOS)"						,  do_normalize=lowercase						,  padding_value=1.0 )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			image_processor(lowercase						,  return_tensors="np" )
					lowerCamelCase_						=			processor(images=lowercase						,  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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"

					lowerCamelCase_						=			processor(text=lowercase )

					lowerCamelCase_						=			tokenizer(lowercase						,  return_token_type_ids=lowercase )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"
					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

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

					lowerCamelCase_						=			processor.batch_decode(lowercase )
					lowerCamelCase_						=			tokenizer.batch_decode(lowercase )

					self.assertListEqual(lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					lowerCamelCase_						=			self.get_image_processor()
					lowerCamelCase_						=			self.get_tokenizer()

					lowerCamelCase_						=			BlipaProcessor(tokenizer=lowercase						,  image_processor=lowercase )

					lowerCamelCase_						=			"lower newer"
					lowerCamelCase_						=			self.prepare_image_inputs()

					lowerCamelCase_						=			processor(text=lowercase						,  images=lowercase )

					# 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,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)