




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor
class 			_UpperCamelCase      (				unittest.TestCase		):
      def __init__(			self   :Dict ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :Union[str, Any]=7 ,      lowerCamelCase   :Any=3 ,      lowerCamelCase   :List[str]=18 ,      lowerCamelCase   :Any=30 ,      lowerCamelCase   :Tuple=400 ,      lowerCamelCase   :Any=True ,      lowerCamelCase   :Tuple=None ,      lowerCamelCase   :Optional[Any]=True ,      lowerCamelCase   :str=None ,      lowerCamelCase   :str=True ,      )     ->			Dict:
       UpperCAmelCase__							 =						size if size is not None else {"shortest_edge": 20}
       UpperCAmelCase__							 =						crop_size if crop_size is not None else {"height": 18, "width": 18}
       UpperCAmelCase__							 =						parent
       UpperCAmelCase__							 =						batch_size
       UpperCAmelCase__							 =						num_channels
       UpperCAmelCase__							 =						image_size
       UpperCAmelCase__							 =						min_resolution
       UpperCAmelCase__							 =						max_resolution
       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						do_center_crop
       UpperCAmelCase__							 =						crop_size
       UpperCAmelCase__							 =						do_flip_channel_order







      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Optional[int]:
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }



@require_torch
@require_vision
class 			_UpperCamelCase      (				UpperCamelCase_	,       unittest.TestCase		):
      UpperCAmelCase_        =		MobileViTImageProcessor if is_vision_available() else None
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[Any]:
       UpperCAmelCase__							 =						MobileViTImageProcessingTester(self      )
      @property
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()
      def 	UpperCAmelCase_						(			self   :str      )     ->			Tuple:
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(__A ,      "do_resize"      )      )
       self.assertTrue(hasattr(__A ,      "size"      )      )
       self.assertTrue(hasattr(__A ,      "do_center_crop"      )      )
       self.assertTrue(hasattr(__A ,      "center_crop"      )      )
       self.assertTrue(hasattr(__A ,      "do_flip_channel_order"      )      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			List[str]:
       UpperCAmelCase__							 =						self.image_processing_class.from_dict(self.image_processor_dict      )
       self.assertEqual(image_processor.size ,      {"shortest_edge": 20}      )
       self.assertEqual(image_processor.crop_size ,      {"height": 18, "width": 18}      )

       UpperCAmelCase__							 =						self.image_processing_class.from_dict(self.image_processor_dict ,      size=42 ,      crop_size=84      )
       self.assertEqual(image_processor.size ,      {"shortest_edge": 42}      )
       self.assertEqual(image_processor.crop_size ,      {"height": 84, "width": 84}      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Dict:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			int:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCAmelCase__							 =						prepare_image_inputs(self.image_processor_tester ,      equal_resolution=__A      )
       for image in image_inputs:
        self.assertIsInstance(__A ,      Image.Image      )

       # Test not batched input
       UpperCAmelCase__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )

       # Test batched
       UpperCAmelCase__							 =						image_processing(__A ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCAmelCase__							 =						prepare_image_inputs(self.image_processor_tester ,      equal_resolution=__A ,      numpify=__A      )
       for image in image_inputs:
        self.assertIsInstance(__A ,      np.ndarray      )

       # Test not batched input
       UpperCAmelCase__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )

       # Test batched
       UpperCAmelCase__							 =						image_processing(__A ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			Dict:
       # Initialize image_processing
       UpperCAmelCase__							 =						self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCAmelCase__							 =						prepare_image_inputs(self.image_processor_tester ,      equal_resolution=__A ,      torchify=__A      )
       for image in image_inputs:
        self.assertIsInstance(__A ,      torch.Tensor      )

       # Test not batched input
       UpperCAmelCase__							 =						image_processing(image_inputs[0] ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )

       # Test batched
       UpperCAmelCase__							 =						image_processing(__A ,      return_tensors="pt"      ).pixel_values
       self.assertEqual(
           encoded_images.shape ,      (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,      )











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





