
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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class     __lowerCAmelCase						(						unittest.TestCase   ):



      """simple docstring"""







      def 		snake_case_      (   self		:							Any					):
          __lowercase		:      Dict						     =							tempfile.mkdtemp()

          __lowercase		:      List[str]						     =							BlipImageProcessor()
          __lowercase		:      Union[str, Any]						     =							BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''					)

          __lowercase		:      Optional[int]						     =							BlipProcessor(_snake_case					,							_snake_case					)

          processor.save_pretrained(self.tmpdirname					)




      def 		snake_case_      (   self		:							List[str]					,							**_snake_case		:							str					):
          return AutoProcessor.from_pretrained(self.tmpdirname					,							**_snake_case					).tokenizer




      def 		snake_case_      (   self		:							int					,							**_snake_case		:							int					):
          return AutoProcessor.from_pretrained(self.tmpdirname					,							**_snake_case					).image_processor




      def 		snake_case_      (   self		:							Optional[Any]					):
          shutil.rmtree(self.tmpdirname					)




      def 		snake_case_      (   self		:							Dict					):
          __lowercase		:      Optional[Any]						     =							[np.random.randint(255					,							size=(3, 30, 400)					,							dtype=np.uinta					)]

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

          return image_inputs




      def 		snake_case_      (   self		:							List[str]					):
          __lowercase		:      int						     =							BlipProcessor(tokenizer=self.get_tokenizer()					,							image_processor=self.get_image_processor()					)
          processor.save_pretrained(self.tmpdirname					)

          __lowercase		:      List[str]						     =							self.get_tokenizer(bos_token='''(BOS)'''					,							eos_token='''(EOS)'''					)
          __lowercase		:      int						     =							self.get_image_processor(do_normalize=_snake_case					,							padding_value=1.0					)

          __lowercase		:      Dict						     =							BlipProcessor.from_pretrained(
              self.tmpdirname					,							bos_token='''(BOS)'''					,							eos_token='''(EOS)'''					,							do_normalize=_snake_case					,							padding_value=1.0					)

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

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




      def 		snake_case_      (   self		:							Dict					):
          __lowercase		:      Optional[Any]						     =							self.get_image_processor()
          __lowercase		:      Union[str, Any]						     =							self.get_tokenizer()

          __lowercase		:      Union[str, Any]						     =							BlipProcessor(tokenizer=_snake_case					,							image_processor=_snake_case					)

          __lowercase		:      Any						     =							self.prepare_image_inputs()

          __lowercase		:      List[Any]						     =							image_processor(_snake_case					,							return_tensors='''np'''					)
          __lowercase		:      List[Any]						     =							processor(images=_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]					):
          __lowercase		:      List[str]						     =							self.get_image_processor()
          __lowercase		:      Optional[int]						     =							self.get_tokenizer()

          __lowercase		:      Any						     =							BlipProcessor(tokenizer=_snake_case					,							image_processor=_snake_case					)

          __lowercase		:      Dict						     =							"lower newer"

          __lowercase		:      str						     =							processor(text=_snake_case					)

          __lowercase		:      Any						     =							tokenizer(_snake_case					,							return_token_type_ids=_snake_case					)

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




      def 		snake_case_      (   self		:							Union[str, Any]					):
          __lowercase		:      Tuple						     =							self.get_image_processor()
          __lowercase		:      List[str]						     =							self.get_tokenizer()

          __lowercase		:      Dict						     =							BlipProcessor(tokenizer=_snake_case					,							image_processor=_snake_case					)

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

          __lowercase		:      Optional[Any]						     =							processor(text=_snake_case					,							images=_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(_snake_case					):
              processor()




      def 		snake_case_      (   self		:							Union[str, Any]					):
          __lowercase		:      Tuple						     =							self.get_image_processor()
          __lowercase		:      Union[str, Any]						     =							self.get_tokenizer()

          __lowercase		:      Dict						     =							BlipProcessor(tokenizer=_snake_case					,							image_processor=_snake_case					)

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

          __lowercase		:      Any						     =							processor.batch_decode(_snake_case					)
          __lowercase		:      Union[str, Any]						     =							tokenizer.batch_decode(_snake_case					)

          self.assertListEqual(_snake_case					,							_snake_case					)




      def 		snake_case_      (   self		:							int					):
          __lowercase		:      Dict						     =							self.get_image_processor()
          __lowercase		:      Dict						     =							self.get_tokenizer()

          __lowercase		:      Dict						     =							BlipProcessor(tokenizer=_snake_case					,							image_processor=_snake_case					)

          __lowercase		:      Tuple						     =							"lower newer"
          __lowercase		:      Optional[int]						     =							self.prepare_image_inputs()

          __lowercase		:      Union[str, Any]						     =							processor(text=_snake_case					,							images=_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 argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     lowercase	(    a ):



		'''simple docstring'''




		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(a ):
						return ext

		raise Exception(
		    F"Unable to determine file format from file extension {path}. "
		    F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}" )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						pipeline(
		    task=args.task     , model=args.model if args.model else None     , config=args.config     , tokenizer=args.tokenizer     , device=args.device     , )
		SCREAMING_SNAKE_CASE_				:Any				     =						try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
		SCREAMING_SNAKE_CASE_				:str				     =						PipelineDataFormat.from_str(
		    format=a     , output_path=args.output     , input_path=args.input     , column=args.column if args.column else nlp.default_input_names     , overwrite=args.overwrite     , )
		return RunCommand(a     , a )



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  UpperCAmelCase				:     Pipeline   ,  UpperCAmelCase				:     PipelineDataFormat):
				SCREAMING_SNAKE_CASE_				:int				     =						nlp
				SCREAMING_SNAKE_CASE_				:Any				     =						reader






		@staticmethod
		def _snake_case		(						UpperCAmelCase				:     ArgumentParser):
				SCREAMING_SNAKE_CASE_				:Any				     =						parser.add_parser("run"   ,  help="Run a pipeline through the CLI")
				run_parser.add_argument("--task"   ,  choices=get_supported_tasks()   ,  help="Task to run")
				run_parser.add_argument("--input"   ,  type=UpperCAmelCase   ,  help="Path to the file to use for inference")
				run_parser.add_argument("--output"   ,  type=UpperCAmelCase   ,  help="Path to the file that will be used post to write results.")
				run_parser.add_argument("--model"   ,  type=UpperCAmelCase   ,  help="Name or path to the model to instantiate.")
				run_parser.add_argument("--config"   ,  type=UpperCAmelCase   ,  help="Name or path to the model's config to instantiate.")
				run_parser.add_argument(
				    "--tokenizer"   ,  type=UpperCAmelCase   ,  help="Name of the tokenizer to use. (default: same as the model name)")
				run_parser.add_argument(
				    "--column"   ,  type=UpperCAmelCase   ,  help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"   ,  )
				run_parser.add_argument(
				    "--format"   ,  type=UpperCAmelCase   ,  default="infer"   ,  choices=PipelineDataFormat.SUPPORTED_FORMATS   ,  help="Input format to read from"   ,  )
				run_parser.add_argument(
				    "--device"   ,  type=UpperCAmelCase   ,  default=-1   ,  help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"   ,  )
				run_parser.add_argument("--overwrite"   ,  action="store_true"   ,  help="Allow overwriting the output file.")
				run_parser.set_defaults(func=UpperCAmelCase)







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._nlp, []

				for entry in self._reader:
						SCREAMING_SNAKE_CASE_				:int				     =						nlp(**UpperCAmelCase) if self._reader.is_multi_columns else nlp(UpperCAmelCase)
						if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
								outputs.append(UpperCAmelCase)
						else:
								outputs += output

        # Saving data
				if self._nlp.binary_output:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self._reader.save_binary(UpperCAmelCase)
						logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}")
				else:
						self._reader.save(UpperCAmelCase)






