
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase        =  get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
@require_tokenizers
class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    XGLMTokenizer
					SCREAMING_SNAKE_CASE			    =    XGLMTokenizerFast
					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    True

					def    lowercase__      (	self					:     int   ):
												super().setUp()

												# We have a SentencePiece fixture for testing
												lowerCamelCase_							  =					XGLMTokenizer(__UpperCamelCase   ,					keep_accents=__UpperCamelCase   )
												tokenizer.save_pretrained(self.tmpdirname   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""<pad>"""
												lowerCamelCase_							  =					1

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase   )   ,					__UpperCamelCase   )
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase   )   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					list(self.get_tokenizer().get_vocab().keys()   )

												self.assertEqual(vocab_keys[0]   ,					"""<s>"""   )
												self.assertEqual(vocab_keys[1]   ,					"""<pad>"""   )
												self.assertEqual(len(__UpperCamelCase   )   ,					1_0_0_8   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												self.assertEqual(self.get_tokenizer().vocab_size   ,					1_0_0_8   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					XGLMTokenizer(__UpperCamelCase   ,					keep_accents=__UpperCamelCase   )

												lowerCamelCase_							  =					tokenizer.tokenize("""This is a test"""   )
												self.assertListEqual(__UpperCamelCase   ,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(__UpperCamelCase   )   ,					[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]   ,					)

												lowerCamelCase_							  =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """9""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """é""",
												        """.""",
												    ]   ,					)
												lowerCamelCase_							  =					tokenizer.convert_tokens_to_ids(__UpperCamelCase   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[
												        value + tokenizer.fairseq_offset
												        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
												    ]   ,					)

												lowerCamelCase_							  =					tokenizer.convert_ids_to_tokens(__UpperCamelCase   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[
												        SPIECE_UNDERLINE + """I""",
												        SPIECE_UNDERLINE + """was""",
												        SPIECE_UNDERLINE + """b""",
												        """or""",
												        """n""",
												        SPIECE_UNDERLINE + """in""",
												        SPIECE_UNDERLINE + """""",
												        """<unk>""",
												        """2""",
												        """0""",
												        """0""",
												        """0""",
												        """,""",
												        SPIECE_UNDERLINE + """and""",
												        SPIECE_UNDERLINE + """this""",
												        SPIECE_UNDERLINE + """is""",
												        SPIECE_UNDERLINE + """f""",
												        """al""",
												        """s""",
												        """<unk>""",
												        """.""",
												    ]   ,					)

					@cached_property
					def    lowercase__      (	self					:     List[Any]   ):
												return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""   )

					def    lowercase__      (	self					:     Dict   ):
												with tempfile.NamedTemporaryFile() as f:
																			shutil.copyfile(__UpperCamelCase   ,					f.name   )
																			lowerCamelCase_							  =					XGLMTokenizer(f.name   ,					keep_accents=__UpperCamelCase   )
																			lowerCamelCase_							  =					pickle.dumps(__UpperCamelCase   )
												pickle.loads(__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												if not self.test_rust_tokenizer:
																			return

												lowerCamelCase_							  =					self.get_tokenizer()
												lowerCamelCase_							  =					self.get_rust_tokenizer()

												lowerCamelCase_							  =					"""I was born in 92000, and this is falsé."""

												lowerCamelCase_							  =					tokenizer.tokenize(__UpperCamelCase   )
												lowerCamelCase_							  =					rust_tokenizer.tokenize(__UpperCamelCase   )
												self.assertListEqual(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					tokenizer.encode(__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   )
												lowerCamelCase_							  =					rust_tokenizer.encode(__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   )
												self.assertListEqual(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.get_rust_tokenizer()
												lowerCamelCase_							  =					tokenizer.encode(__UpperCamelCase   )
												lowerCamelCase_							  =					rust_tokenizer.encode(__UpperCamelCase   )
												self.assertListEqual(__UpperCamelCase   ,					__UpperCamelCase   )

					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					"""Hello World!"""
												lowerCamelCase_							  =					[2, 3_1_2_2_7, 4_4_4_7, 3_5]

												self.assertListEqual(__UpperCamelCase   ,					self.big_tokenizer.encode(__UpperCamelCase   )   )

					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					(
												    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
												    """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
												)
												# fmt: off
												lowerCamelCase_							  =					[2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
												# fmt: on

												self.assertListEqual(__UpperCamelCase   ,					self.big_tokenizer.encode(__UpperCamelCase   )   )



					@slow
					def    lowercase__      (	self					:     int   ):
												# fmt: off
												lowerCamelCase_							  =					{
												    """input_ids""": [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
												    """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
												}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=__UpperCamelCase   ,					model_name="""facebook/xglm-564M"""   ,					padding=__UpperCamelCase   ,					)







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:





		class       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					DepthEstimationPipeline(model=__UpperCamelCase   ,					image_processor=__UpperCamelCase   )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
												self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}   ,					__UpperCamelCase   )
												import datasets

												lowerCamelCase_							  =					datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,					"""image"""   ,					split="""test"""   )
												lowerCamelCase_							  =					depth_estimator(
												    [
												        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   ),
												        """http://images.cocodataset.org/val2017/000000039769.jpg""",
												        # RGBA
												        dataset[0]["""file"""],
												        # LA
												        dataset[1]["""file"""],
												        # L
												        dataset[2]["""file"""],
												    ]   )
												self.assertEqual(
												    [
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												        {"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )},
												    ]   ,					__UpperCamelCase   ,					)

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					hashimage(outputs["""depth"""]   )

												# This seems flaky.
												# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()   )   ,					29.304   )
												self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()   )   ,					2.662   )



					@require_torch
					def    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





