



"""simple docstring"""
import unittest

import numpy as np

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

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 DPTImageProcessor






class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=7			,     a_=3			,     a_=1_8			,     a_=3_0			,     a_=4_0_0			,     a_=True			,     a_=None			,     a_=True			,     a_=[0.5, 0.5, 0.5]			,     a_=[0.5, 0.5, 0.5]			,     ):
									a_     :      Any 						=     size if size is not None else {"height": 1_8, "width": 1_8}
									a_     :      List[Any] 						=     parent
									a_     :      int 						=     batch_size
									a_     :      Any 						=     num_channels
									a_     :      List[Any] 						=     image_size
									a_     :      int 						=     min_resolution
									a_     :      str 						=     max_resolution
									a_     :      Dict 						=     do_resize
									a_     :      str 						=     size
									a_     :      Optional[int] 						=     do_normalize
									a_     :      Dict 						=     image_mean
									a_     :      int 						=     image_std






							def snake_case_  (   self      ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}






@require_torch
@require_vision
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					DPTImageProcessor if is_vision_available() else None






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     DPTImageProcessingTester(self      )






							@property
							def snake_case_  (   self      ):
									return self.image_processor_tester.prepare_image_processor_dict()






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     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			,     "size"      )      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,     {"height": 1_8, "width": 1_8}      )

									a_     :      Tuple 						=     self.image_processing_class.from_dict(self.image_processor_dict			,     size=4_2      )
									self.assertEqual(image_processor.size			,     {"height": 4_2, "width": 4_2}      )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      str 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									a_     :      Optional[int] 						=     prepare_image_inputs(self.image_processor_tester			,     equal_resolution=__A      )
									for image in image_inputs:
											self.assertIsInstance(__A			,     Image.Image      )

									# Test not batched input
									a_     :      str 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )

									# Test batched
									a_     :      Optional[int] 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      List[Any] 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									a_     :      int 						=     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
									a_     :      str 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )

									# Test batched
									a_     :      str 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )






							def snake_case_  (   self      ):
									# Initialize image_processing
									a_     :      List[str] 						=     self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									a_     :      int 						=     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
									a_     :      Optional[int] 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )

									# Test batched
									a_     :      int 						=     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.size["height"],
									        self.image_processor_tester.size["width"],
									    )			,     )




"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """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

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)