
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


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





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

					def    lowercase__      (	self					:     Union[str, Any]   ):
												super().setUp()

												# We have a SentencePiece fixture for testing
												lowerCamelCase_							  =					GPTSwaTokenizer(__UpperCamelCase   ,					eos_token="""<unk>"""   ,					bos_token="""<unk>"""   ,					pad_token="""<unk>"""   )

												tokenizer.save_pretrained(self.tmpdirname   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					"""This is a test"""
												lowerCamelCase_							  =					"""This is a test"""
												return input_text, output_text

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					"""<s>"""
												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					:     List[Any]   ):
												lowerCamelCase_							  =					list(self.get_tokenizer().get_vocab().keys()   )

												self.assertEqual(vocab_keys[0]   ,					"""<unk>"""   )
												self.assertEqual(vocab_keys[1]   ,					"""<s>"""   )
												self.assertEqual(vocab_keys[-1]   ,					"""j"""   )
												self.assertEqual(len(__UpperCamelCase   )   ,					2_0_0_0   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												self.assertEqual(self.get_tokenizer().vocab_size   ,					2_0_0_0   )

					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					GPTSwaTokenizer(__UpperCamelCase   )

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

												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase   )   ,					[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]   )

												lowerCamelCase_							  =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
												# fmt: off
												self.assertListEqual(
												    __UpperCamelCase   ,					["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   ,					)
												# fmt: on

												lowerCamelCase_							  =					tokenizer.convert_tokens_to_ids(__UpperCamelCase   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]   ,					)

												lowerCamelCase_							  =					tokenizer.convert_ids_to_tokens(__UpperCamelCase   )
												# fmt: off
												self.assertListEqual(
												    __UpperCamelCase   ,					["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   )
												# fmt: on

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					GPTSwaTokenizer(__UpperCamelCase   )
												lowerCamelCase_							  =					["""This is a test""", """I was born in 92000, and this is falsé."""]
												lowerCamelCase_							  =					[
												    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
												    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
												]

												# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
												for text, expected_ids in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			self.assertListEqual(tokenizer.encode_fast(__UpperCamelCase   )   ,					__UpperCamelCase   )

												# Test that decode_fast returns the input text
												for text, token_ids in zip(__UpperCamelCase   ,					__UpperCamelCase   ):
																			self.assertEqual(tokenizer.decode_fast(__UpperCamelCase   )   ,					__UpperCamelCase   )



					@slow
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					[
												    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
												    """Hey there, how are you doing this fine day?""",
												    """This is a text with a trailing spaces followed by a dot     .""",
												    """Häj sväjs lillebrör! =)""",
												    """Det är inget fel på Mr. Cool""",
												]

												# fmt: off
												lowerCamelCase_							  =					{"""input_ids""": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
												# fmt: on
												self.tokenizer_integration_test_util(
												    expected_encoding=__UpperCamelCase   ,					model_name="""AI-Sweden/gpt-sw3-126m"""   ,					sequences=__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"""   )





