
"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor

class 		a__						(      unittest.TestCase						):
   def __init__(			self	,    _a	,    _a=7	,    _a=3	,    _a=30	,    _a=400	,    _a=True	,    _a=None	,    _a=True	,    _a=[0.5, 0.5, 0.5]	,    _a=[0.5, 0.5, 0.5]	,    _a=True	,    _a=1 / 255	,    _a=True	,    ):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          lowercase    :				str       		=							size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
          lowercase    :				Any       		=							parent
          lowercase    :				Union[str, Any]       		=							batch_size
          lowercase    :				Tuple       		=							num_channels
          lowercase    :				Union[str, Any]       		=							min_resolution
          lowercase    :				List[Any]       		=							max_resolution
          lowercase    :				Tuple       		=							do_resize
          lowercase    :				Union[str, Any]       		=							size
          lowercase    :				List[Any]       		=							do_normalize
          lowercase    :				Union[str, Any]       		=							image_mean
          lowercase    :				Optional[Any]       		=							image_std
          lowercase    :				Any       		=							do_rescale
          lowercase    :				Optional[Any]       		=							rescale_factor
          lowercase    :				int       		=							do_pad
   def 			__magic_name__			(			self				):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }

   def 			__magic_name__			(			self	,    _a	,    _a=False				):
          if not batched:
                 lowercase    :				List[Any]       		=							image_inputs[0]
                 if isinstance(_a	,    Image.Image				):
                        lowercase		,    lowercase    :				Dict       		=							image.size
                 else:
                        lowercase		,    lowercase    :				str       		=							image.shape[1], image.shape[2]
                 if w < h:
                        lowercase    :				Optional[int]       		=							int(self.size["shortest_edge"] * h / w				)
                        lowercase    :				Dict       		=							self.size["shortest_edge"]
                 elif w > h:
                        lowercase    :				Dict       		=							self.size["shortest_edge"]
                        lowercase    :				List[str]       		=							int(self.size["shortest_edge"] * w / h				)
                 else:
                        lowercase    :				str       		=							self.size["shortest_edge"]
                        lowercase    :				Tuple       		=							self.size["shortest_edge"]

          else:
                 lowercase    :				Optional[Any]       		=							[]
                 for image in image_inputs:
                        lowercase		,    lowercase    :				Tuple       		=							self.get_expected_values([image]				)
                        expected_values.append((expected_height, expected_width)				)
                 lowercase    :				List[Any]       		=							max(_a	,    key=lambda _a				: item[0]				)[0]
                 lowercase    :				Optional[int]       		=							max(_a	,    key=lambda _a				: item[1]				)[1]

          return expected_height, expected_width


@require_torch
@require_vision
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				DetaImageProcessor if is_vision_available() else None
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							DetaImageProcessingTester(self				)
   @property
   def 			__magic_name__			(			self				):
          return self.image_processor_tester.prepare_image_processor_dict()
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(_a	,    "image_mean"				)				)
          self.assertTrue(hasattr(_a	,    "image_std"				)				)
          self.assertTrue(hasattr(_a	,    "do_normalize"				)				)
          self.assertTrue(hasattr(_a	,    "do_resize"				)				)
          self.assertTrue(hasattr(_a	,    "do_rescale"				)				)
          self.assertTrue(hasattr(_a	,    "do_pad"				)				)
          self.assertTrue(hasattr(_a	,    "size"				)				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.image_processing_class.from_dict(self.image_processor_dict				)
          self.assertEqual(image_processor.size	,    {"shortest_edge": 18, "longest_edge": 1_333}				)
          self.assertEqual(image_processor.do_pad	,    _a				)
   def 			__magic_name__			(			self				):
          pass
   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				List[Any]       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PIL images
          lowercase    :				Tuple       		=							prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_a				)
          for image in image_inputs:
                 self.assertIsInstance(_a	,    Image.Image				)

          # Test not batched input
          lowercase    :				Dict       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Optional[Any]       		=							self.image_processor_tester.get_expected_values(_a				)

          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase		,    lowercase    :				Optional[int]       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)

          lowercase    :				Tuple       		=							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,
                  expected_height,
                  expected_width,
              )	,    )
   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				int       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random numpy tensors
          lowercase    :				List[str]       		=							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
          lowercase    :				List[str]       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Optional[int]       		=							self.image_processor_tester.get_expected_values(_a				)

          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase    :				Optional[Any]       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Tuple       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)

          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,    )
   def 			__magic_name__			(			self				):
          # Initialize image_processing
          lowercase    :				int       		=							self.image_processing_class(**self.image_processor_dict				)
          # create random PyTorch tensors
          lowercase    :				Any       		=							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
          lowercase    :				List[str]       		=							image_processing(image_inputs[0]	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				List[Any]       		=							self.image_processor_tester.get_expected_values(_a				)

          self.assertEqual(
              encoded_images.shape	,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,    )

          # Test batched
          lowercase    :				Tuple       		=							image_processing(_a	,    return_tensors="pt"				).pixel_values

          lowercase		,    lowercase    :				Optional[int]       		=							self.image_processor_tester.get_expected_values(_a	,    batched=_a				)

          self.assertEqual(
              encoded_images.shape	,    (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )	,    )
   @slow
   def 			__magic_name__			(			self				):
          # prepare image and target
          lowercase    :				List[Any]       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,    "r"				) as f:
                 lowercase    :				Optional[Any]       		=							json.loads(f.read()				)

          lowercase    :				Optional[Any]       		=							{"image_id": 39_769, "annotations": target}

          # encode them
          lowercase    :				Dict       		=							DetaImageProcessor()
          lowercase    :				str       		=							image_processing(images=_a	,    annotations=_a	,    return_tensors="pt"				)

          # verify pixel values
          lowercase    :				str       		=							torch.Size([1, 3, 800, 1_066]				)
          self.assertEqual(encoding["pixel_values"].shape	,    _a				)

          lowercase    :				Union[str, Any]       		=							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,    _a	,    atol=1E-4				)				)

          # verify area
          lowercase    :				Tuple       		=							torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,    _a				)				)
          # verify boxes
          lowercase    :				Union[str, Any]       		=							torch.Size([6, 4]				)
          self.assertEqual(encoding["labels"][0]["boxes"].shape	,    _a				)
          lowercase    :				int       		=							torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,    _a	,    atol=1E-3				)				)
          # verify image_id
          lowercase    :				Any       		=							torch.tensor([39_769]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,    _a				)				)
          # verify is_crowd
          lowercase    :				Dict       		=							torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,    _a				)				)
          # verify class_labels
          lowercase    :				List[str]       		=							torch.tensor([75, 75, 63, 65, 17, 17]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,    _a				)				)
          # verify orig_size
          lowercase    :				List[Any]       		=							torch.tensor([480, 640]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,    _a				)				)
          # verify size
          lowercase    :				Optional[int]       		=							torch.tensor([800, 1_066]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,    _a				)				)

   @slow
   def 			__magic_name__			(			self				):
          # prepare image, target and masks_path
          lowercase    :				str       		=							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
          with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,    "r"				) as f:
                 lowercase    :				Tuple       		=							json.loads(f.read()				)

          lowercase    :				int       		=							{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

          lowercase    :				Dict       		=							pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

          # encode them
          lowercase    :				Tuple       		=							DetaImageProcessor(format="coco_panoptic"				)
          lowercase    :				int       		=							image_processing(images=_a	,    annotations=_a	,    masks_path=_a	,    return_tensors="pt"				)

          # verify pixel values
          lowercase    :				Dict       		=							torch.Size([1, 3, 800, 1_066]				)
          self.assertEqual(encoding["pixel_values"].shape	,    _a				)

          lowercase    :				str       		=							torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
          self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,    _a	,    atol=1E-4				)				)

          # verify area
          lowercase    :				Union[str, Any]       		=							torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,    _a				)				)
          # verify boxes
          lowercase    :				Optional[Any]       		=							torch.Size([6, 4]				)
          self.assertEqual(encoding["labels"][0]["boxes"].shape	,    _a				)
          lowercase    :				Union[str, Any]       		=							torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,    _a	,    atol=1E-3				)				)
          # verify image_id
          lowercase    :				List[str]       		=							torch.tensor([39_769]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,    _a				)				)
          # verify is_crowd
          lowercase    :				Any       		=							torch.tensor([0, 0, 0, 0, 0, 0]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,    _a				)				)
          # verify class_labels
          lowercase    :				str       		=							torch.tensor([17, 17, 63, 75, 75, 93]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,    _a				)				)
          # verify masks
          lowercase    :				Dict       		=							822_873
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,    _a				)
          # verify orig_size
          lowercase    :				List[Any]       		=							torch.tensor([480, 640]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,    _a				)				)
          # verify size
          lowercase    :				Optional[int]       		=							torch.tensor([800, 1_066]				)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,    _a				)				)




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A			:       Optional[int]   		=       {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Union[str, Any]   		=       ["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       Dict   		=       ["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			:       int   		=       [
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

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


