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,
		    BertTokenizerFast,
		    BlipImageProcessor,
		    GPTaTokenizer,
		    InstructBlipProcessor,
		    PreTrainedTokenizerFast,
		)




@require_vision
class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase 			=							tempfile.mkdtemp()

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

							__lowercase 			=							InstructBlipProcessor(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)

							processor.save_pretrained(self.tmpdirname						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


							return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowerCAmelCase__						).tokenizer





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


							return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowerCAmelCase__						).image_processor





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


							return AutoProcessor.from_pretrained(self.tmpdirname      ,   **lowerCAmelCase__						).qformer_tokenizer





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							[np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta						)]

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

							return image_inputs





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()      ,   qformer_tokenizer=self.get_qformer_tokenizer()      ,   )
							processor.save_pretrained(self.tmpdirname						)

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

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

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

							self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor      ,   lowerCAmelCase__						)
							self.assertIsInstance(processor.qformer_tokenizer      ,   lowerCAmelCase__						)





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

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_qformer_tokenizer()

							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__      ,   qformer_tokenizer=lowerCAmelCase__						)

							__lowercase 			=							self.prepare_image_inputs()

							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''np'''						)
							__lowercase 			=							processor(images=lowerCAmelCase__      ,   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						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_qformer_tokenizer()

							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__      ,   qformer_tokenizer=lowerCAmelCase__						)

							__lowercase 			=							'''lower newer'''

							__lowercase 			=							processor(text=lowerCAmelCase__						)

							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   return_token_type_ids=lowerCAmelCase__						)
							__lowercase 			=							qformer_tokenizer(lowerCAmelCase__      ,   return_token_type_ids=lowerCAmelCase__						)

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

							for key in encoded_tokens_qformer.keys():
											self.assertListEqual(encoded_tokens_qformer[key]      ,   encoded_processor['''qformer_''' + key]						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_qformer_tokenizer()

							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__      ,   qformer_tokenizer=lowerCAmelCase__						)

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

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

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

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





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

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_qformer_tokenizer()

							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__      ,   qformer_tokenizer=lowerCAmelCase__						)

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

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

							self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.get_image_processor()
							__lowercase 			=							self.get_tokenizer()
							__lowercase 			=							self.get_qformer_tokenizer()

							__lowercase 			=							InstructBlipProcessor(
							    tokenizer=lowerCAmelCase__      ,   image_processor=lowerCAmelCase__      ,   qformer_tokenizer=lowerCAmelCase__						)

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

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

							self.assertListEqual(
							    list(inputs.keys()						)      ,   ['''input_ids''', '''attention_mask''', '''qformer_input_ids''', '''qformer_attention_mask''', '''pixel_values''']      ,   )
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

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