


"""simple docstring"""
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 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Dict   =		tempfile.mkdtemp()

      _lowerCamelCase	:					int   =		BlipImageProcessor()
      _lowerCamelCase	:					str   =		GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""")
      _lowerCamelCase	:					List[Any]   =		BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""")

      _lowerCamelCase	:					str   =		InstructBlipProcessor(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

      processor.save_pretrained(self.tmpdirname)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Tuple:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).tokenizer





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).image_processor





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).qformer_tokenizer





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      shutil.rmtree(self.tmpdirname)





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

      _lowerCamelCase	:					str   =		[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE      ,   0      ,   -1)) for x in image_inputs]

      return image_inputs





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		InstructBlipProcessor(
          tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()      ,   qformer_tokenizer=self.get_qformer_tokenizer()      ,   )
      processor.save_pretrained(self.tmpdirname)

      _lowerCamelCase	:					Dict   =		self.get_tokenizer(bos_token="""(BOS)"""      ,   eos_token="""(EOS)""")
      _lowerCamelCase	:					Dict   =		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      _lowerCamelCase	:					Union[str, Any]   =		InstructBlipProcessor.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)
      self.assertIsInstance(processor.qformer_tokenizer      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					List[Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					int   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					Union[str, Any]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Optional[Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")
      _lowerCamelCase	:					Any   =		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 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					int   =		self.get_tokenizer()
      _lowerCamelCase	:					str   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					Optional[Any]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		"""lower newer"""

      _lowerCamelCase	:					Dict   =		processor(text=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		qformer_tokenizer(SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE)

      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 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		self.get_image_processor()
      _lowerCamelCase	:					List[str]   =		self.get_tokenizer()
      _lowerCamelCase	:					Optional[Any]   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					str   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		"""lower newer"""
      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Optional[Any]   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

      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(SCREAMING_SNAKE_CASE):
         processor()





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					Any   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					List[str]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCamelCase	:					str   =		processor.batch_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE)

      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					str   =		self.get_tokenizer()
      _lowerCamelCase	:					Optional[int]   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					int   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		"""lower newer"""
      _lowerCamelCase	:					str   =		self.prepare_image_inputs()

      _lowerCamelCase	:					List[str]   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

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







import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
