import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image

def     lowercase_       (    _lowerCamelCase							: Any	,			_lowerCamelCase							: List[str]=False	,			_lowerCamelCase							: Union[str, Any]=False	,			_lowerCamelCase							: Union[str, Any]=False):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    lowercase__				:     str					      =       []
    for i in range(image_processor_tester.batch_size):
        if equal_resolution:
            lowercase__				:     Union[str, Any]					      =       image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            lowercase__				:     Dict					      =       image_processor_tester.min_resolution
            if getattr(_lowerCamelCase	,			"size_divisor"	,			_lowerCamelCase):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                lowercase__				:     str					      =       max(image_processor_tester.size_divisor	,			_lowerCamelCase)
            lowercase__      ,     lowercase__				:     Any					      =       np.random.choice(np.arange(_lowerCamelCase	,			image_processor_tester.max_resolution)	,			2)
        image_inputs.append(
            np.random.randint(255	,			size=(image_processor_tester.num_channels, width, height)	,			dtype=np.uinta))

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        lowercase__				:     int					      =       [Image.fromarray(np.moveaxis(_lowerCamelCase	,			0	,			-1)) for image in image_inputs]

    if torchify:
        lowercase__				:     Any					      =       [torch.from_numpy(_lowerCamelCase) for image in image_inputs]

    return image_inputs

def     lowercase_       (    _lowerCamelCase							: Union[str, Any]	,			_lowerCamelCase							: Optional[Any]=10	,			_lowerCamelCase							: Any=10	,			_lowerCamelCase							: List[Any]=False	,			_lowerCamelCase							: Any=False):
    lowercase__				:     Union[str, Any]					      =       []
    for i in range(image_processor_tester.num_frames):
        video.append(np.random.randint(255	,			size=(image_processor_tester.num_channels, width, height)	,			dtype=np.uinta))

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        lowercase__				:     int					      =       [Image.fromarray(np.moveaxis(_lowerCamelCase	,			0	,			-1)) for frame in video]

    if torchify:
        lowercase__				:     Any					      =       [torch.from_numpy(_lowerCamelCase) for frame in video]

    return video

def     lowercase_       (    _lowerCamelCase							: Optional[int]	,			_lowerCamelCase							: str=False	,			_lowerCamelCase							: List[Any]=False	,			_lowerCamelCase							: str=False):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    lowercase__				:     int					      =       []
    for i in range(image_processor_tester.batch_size):
        if equal_resolution:
            lowercase__				:     Optional[Any]					      =       image_processor_tester.max_resolution
        else:
            lowercase__      ,     lowercase__				:     List[str]					      =       np.random.choice(
                np.arange(image_processor_tester.min_resolution	,			image_processor_tester.max_resolution)	,			2)
            lowercase__				:     Dict					      =       prepare_video(
                image_processor_tester=_lowerCamelCase	,			width=_lowerCamelCase	,			height=_lowerCamelCase	,			numpify=_lowerCamelCase	,			torchify=_lowerCamelCase	,			)
        video_inputs.append(_lowerCamelCase)

    return video_inputs



class 						snake_case_ :
   __A			:	Union[str, Any]        =       None






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		List[Any]:
       lowercase__				:     List[str]					      =       self.image_processing_class(**self.image_processor_dict						)
       lowercase__				:     str					      =       json.loads(image_processor.to_json_string()						)
       for key, value in self.image_processor_dict.items():
           self.assertEqual(obj[key]			,		lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		str:
       lowercase__				:     Union[str, Any]					      =       self.image_processing_class(**self.image_processor_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__				:     Any					      =       os.path.join(lowercase_			,		"image_processor.json"						)
           image_processor_first.to_json_file(lowercase_						)
           lowercase__				:     List[str]					      =       self.image_processing_class.from_json_file(lowercase_						)

       self.assertEqual(image_processor_second.to_dict()			,		image_processor_first.to_dict()						)






   def    __UpperCamelCase  (   self       :  Dict						)      ->		Optional[int]:
       lowercase__				:     Optional[Any]					      =       self.image_processing_class(**self.image_processor_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__				:     Optional[int]					      =       image_processor_first.save_pretrained(lowercase_						)[0]
           check_json_file_has_correct_format(lowercase_						)
           lowercase__				:     Tuple					      =       self.image_processing_class.from_pretrained(lowercase_						)

       self.assertEqual(image_processor_second.to_dict()			,		image_processor_first.to_dict()						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Dict:
       lowercase__				:     Any					      =       self.image_processing_class()
       self.assertIsNotNone(lowercase_						)







   @require_torch
   @require_vision
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Dict:
       if self.test_cast_dtype is not None:
           # Initialize image_processor
           lowercase__				:     List[str]					      =       self.image_processing_class(**self.image_processor_dict						)

           # create random PyTorch tensors
           lowercase__				:     Dict					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_			,		torchify=lowercase_						)

           lowercase__				:     Union[str, Any]					      =       image_processor(lowercase_			,		return_tensors="pt"						)
           # for layoutLM compatiblity
           self.assertEqual(encoding.pixel_values.device			,		torch.device("cpu"						)						)
           self.assertEqual(encoding.pixel_values.dtype			,		torch.floataa						)

           lowercase__				:     Optional[int]					      =       image_processor(lowercase_			,		return_tensors="pt"						).to(torch.floataa						)
           self.assertEqual(encoding.pixel_values.device			,		torch.device("cpu"						)						)
           self.assertEqual(encoding.pixel_values.dtype			,		torch.floataa						)

           lowercase__				:     str					      =       image_processor(lowercase_			,		return_tensors="pt"						).to("cpu"			,		torch.bfloataa						)
           self.assertEqual(encoding.pixel_values.device			,		torch.device("cpu"						)						)
           self.assertEqual(encoding.pixel_values.dtype			,		torch.bfloataa						)

           with self.assertRaises(lowercase_						):
               lowercase__				:     Any					      =       image_processor(lowercase_			,		return_tensors="pt"						).to(torch.bfloataa			,		"cpu"						)

           # Try with text + image feature
           lowercase__				:     int					      =       image_processor(lowercase_			,		return_tensors="pt"						)
           encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
           lowercase__				:     Optional[Any]					      =       encoding.to(torch.floataa						)

           self.assertEqual(encoding.pixel_values.device			,		torch.device("cpu"						)						)
           self.assertEqual(encoding.pixel_values.dtype			,		torch.floataa						)
           self.assertEqual(encoding.input_ids.dtype			,		torch.long						)




import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 						snake_case_ (	unittest.TestCase						,__A ):






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		List[Any]:
       lowercase__				:     List[Any]					      =       load_tool("document-question-answering"						)
       self.tool.setup()
       lowercase__				:     Optional[int]					      =       load_tool("document-question-answering"			,		remote=lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[int]:
       lowercase__				:     List[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[Any]					      =       dataset[0]["image"]

       lowercase__				:     int					      =       self.tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     str					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     str					      =       dataset[0]["image"]

       lowercase__				:     List[str]					      =       self.remote_tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Dict:
       lowercase__				:     List[str]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[str]					      =       dataset[0]["image"]

       self.tool(document=lowercase_			,		question="When is the coffee break?"						)







   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     int					      =       dataset[0]["image"]

       lowercase__				:     Tuple					      =       self.remote_tool(document=lowercase_			,		question="When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)



