
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 A_      (      self			):
          snake_case__    =					tempfile.mkdtemp()

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

          snake_case__    =					BlipaProcessor(lowerCamelCase		,						lowerCamelCase			)

          processor.save_pretrained(self.tmpdirname			)
      def A_      (      self		,						**lowerCamelCase			):
          return AutoProcessor.from_pretrained(self.tmpdirname		,						**lowerCamelCase			).tokenizer
      def A_      (      self		,						**lowerCamelCase			):
          return AutoProcessor.from_pretrained(self.tmpdirname		,						**lowerCamelCase			).image_processor
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					[np.random.randint(2_55		,						size=(3, 30, 4_00)		,						dtype=np.uinta			)]

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

          return image_inputs
      def A_      (      self			):
          snake_case__    =					BlipaProcessor(tokenizer=self.get_tokenizer()		,						image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

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

          snake_case__    =					BlipaProcessor.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			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()

          snake_case__    =					BlipaProcessor(tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase			)

          snake_case__    =					self.prepare_image_inputs()

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					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 A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()

          snake_case__    =					BlipaProcessor(tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase			)

          snake_case__    =					"lower newer"

          snake_case__    =					processor(text=lowerCamelCase			)

          snake_case__    =					tokenizer(lowerCamelCase		,						return_token_type_ids=lowerCamelCase			)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]		,						encoded_processor[key]			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()

          snake_case__    =					BlipaProcessor(tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase			)

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

          snake_case__    =					processor(text=lowerCamelCase		,						images=lowerCamelCase			)

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

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()

          snake_case__    =					BlipaProcessor(tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase			)

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

          snake_case__    =					processor.batch_decode(lowerCamelCase			)
          snake_case__    =					tokenizer.batch_decode(lowerCamelCase			)

          self.assertListEqual(lowerCamelCase		,						lowerCamelCase			)



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_tokenizer()

          snake_case__    =					BlipaProcessor(tokenizer=lowerCamelCase		,						image_processor=lowerCamelCase			)

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

          snake_case__    =					processor(text=lowerCamelCase		,						images=lowerCamelCase			)

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



