

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


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

if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase      				=     2_5_0_0_0_4
lowerCamelCase      				=     2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class     snake_case_			(				_a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MBartaaTokenizer
							__UpperCAmelCase       =MBartaaTokenizerFast
							__UpperCAmelCase       =True
							__UpperCAmelCase       =True




							def        A__  ( self							):
												super().setUp()

												# We have a SentencePiece fixture for testing
												__lowerCAmelCase					=							MBartaaTokenizer(_A					,						src_lang='en_XX'					,						tgt_lang='ro_RO'					,						keep_accents=_A							)
												tokenizer.save_pretrained(self.tmpdirname							)




							def        A__  ( self							):
												__lowerCAmelCase					=							'<s>'
												__lowerCAmelCase					=							0

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




							def        A__  ( self							):
												__lowerCAmelCase					=							list(self.get_tokenizer().get_vocab().keys()							)

												self.assertEqual(vocab_keys[0]					,						'<s>'							)
												self.assertEqual(vocab_keys[1]					,						'<pad>'							)
												self.assertEqual(vocab_keys[-1]					,						'<mask>'							)
												self.assertEqual(len(_A							)					,						1_0_5_4							)




							def        A__  ( self							):
												self.assertEqual(self.get_tokenizer().vocab_size					,						1_0_5_4							)




							def        A__  ( self							):
												__lowerCAmelCase					=							MBartaaTokenizer(_A					,						src_lang='en_XX'					,						tgt_lang='ro_RO'					,						keep_accents=_A							)

												__lowerCAmelCase					=							tokenizer.tokenize('This is a test'							)
												self.assertListEqual(_A					,						['▁This', '▁is', '▁a', '▁t', 'est']							)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_A							)					,						[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(
												    _A					,						[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(_A							)
												self.assertListEqual(
												    _A					,						[
												        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(_A							)
												self.assertListEqual(
												    _A					,						[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>', '.']					,						)




							@slow
							def        A__  ( self							):
												# fmt: off
												__lowerCAmelCase					=							{'input_ids': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 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]], '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, 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], [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, 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]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=_A					,						model_name='facebook/mbart-large-50'					,						revision='d3913889c59cd5c9e456b269c376325eabad57e2'					,						)




							def        A__  ( self							):
												if not self.test_slow_tokenizer:
																	# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																	return

												__lowerCAmelCase					=							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						__lowerCAmelCase					=							self.rust_tokenizer_class.from_pretrained(_A					,						**_A							)
																						__lowerCAmelCase					=							self.tokenizer_class.from_pretrained(_A					,						**_A							)

																						__lowerCAmelCase					=							tempfile.mkdtemp()

																						__lowerCAmelCase					=							tokenizer_r.save_pretrained(_A							)
																						__lowerCAmelCase					=							tokenizer_p.save_pretrained(_A							)

																						# Checks it save with the same files + the tokenizer.json file for the fast one
																						self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files							)							)
																						__lowerCAmelCase					=							tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f							)
																						self.assertSequenceEqual(_A					,						_A							)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase					=							tokenizer_r.from_pretrained(_A							)
																						__lowerCAmelCase					=							tokenizer_p.from_pretrained(_A							)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_A					,						_A							)							)
																											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																						shutil.rmtree(_A							)

																						# Save tokenizer rust, legacy_format=True
																						__lowerCAmelCase					=							tempfile.mkdtemp()

																						__lowerCAmelCase					=							tokenizer_r.save_pretrained(_A					,						legacy_format=_A							)
																						__lowerCAmelCase					=							tokenizer_p.save_pretrained(_A							)

																						# Checks it save with the same files
																						self.assertSequenceEqual(_A					,						_A							)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase					=							tokenizer_r.from_pretrained(_A							)
																						__lowerCAmelCase					=							tokenizer_p.from_pretrained(_A							)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_A					,						_A							)							)

																						shutil.rmtree(_A							)

																						# Save tokenizer rust, legacy_format=False
																						__lowerCAmelCase					=							tempfile.mkdtemp()

																						__lowerCAmelCase					=							tokenizer_r.save_pretrained(_A					,						legacy_format=_A							)
																						__lowerCAmelCase					=							tokenizer_p.save_pretrained(_A							)

																						# Checks it saved the tokenizer.json file
																						self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files							)							)

																						# Checks everything loads correctly in the same way
																						__lowerCAmelCase					=							tokenizer_r.from_pretrained(_A							)
																						__lowerCAmelCase					=							tokenizer_p.from_pretrained(_A							)

																						# Check special tokens are set accordingly on Rust and Python
																						for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_A					,						_A							)							)

																						shutil.rmtree(_A							)





@require_torch
@require_sentencepiece
@require_tokenizers
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       ="""facebook/mbart-large-50-one-to-many-mmt"""
							__UpperCAmelCase       =[
							    """ UN Chief Says There Is No Military Solution in Syria""",
							    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
							]
							__UpperCAmelCase       =[
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
							    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
							    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
							]
							__UpperCAmelCase       =[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]




							@classmethod
							def        A__  ( cls							):
												__lowerCAmelCase					=							MBartaaTokenizer.from_pretrained(
												    cls.checkpoint_name					,						src_lang='en_XX'					,						tgt_lang='ro_RO'							)
												__lowerCAmelCase					=							1
												return cls




							def        A__  ( self							):
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']					,						2_5_0_0_0_1							)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']					,						2_5_0_0_0_4							)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']					,						2_5_0_0_2_0							)
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']					,						2_5_0_0_3_8							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
												self.assertListEqual(self.expected_src_tokens					,						_A							)




							def        A__  ( self							):
												self.assertIn(_A					,						self.tokenizer.all_special_ids							)
												__lowerCAmelCase					=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
												__lowerCAmelCase					=							self.tokenizer.decode(_A					,						skip_special_tokens=_A							)
												__lowerCAmelCase					=							self.tokenizer.decode(generated_ids[1:]					,						skip_special_tokens=_A							)
												self.assertEqual(_A					,						_A							)
												self.assertNotIn(self.tokenizer.eos_token					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase					=							['this is gunna be a long sentence ' * 2_0]
												assert isinstance(src_text[0]					,						_A							)
												__lowerCAmelCase					=							1_0
												__lowerCAmelCase					=							self.tokenizer(_A					,						max_length=_A					,						truncation=_A							).input_ids[0]
												self.assertEqual(ids[0]					,						_A							)
												self.assertEqual(ids[-1]					,						2							)
												self.assertEqual(len(_A							)					,						_A							)




							def        A__  ( self							):
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']							)					,						[2_5_0_0_5_3, 2_5_0_0_0_1]							)




							def        A__  ( self							):
												__lowerCAmelCase					=							tempfile.mkdtemp()
												__lowerCAmelCase					=							self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(_A							)
												__lowerCAmelCase					=							MBartaaTokenizer.from_pretrained(_A							)
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,						_A							)




							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer(self.src_text					,						text_target=self.tgt_text					,						padding=_A					,						return_tensors='pt'							)
												__lowerCAmelCase					=							shift_tokens_right(batch['labels']					,						self.tokenizer.pad_token_id							)

												# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
												assert batch.input_ids[1][0] == EN_CODE
												assert batch.input_ids[1][-1] == 2
												assert batch.labels[1][0] == RO_CODE
												assert batch.labels[1][-1] == 2
												assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer(
												    self.src_text					,						text_target=self.tgt_text					,						padding=_A					,						truncation=_A					,						max_length=len(self.expected_src_tokens							)					,						return_tensors='pt'					,						)
												__lowerCAmelCase					=							shift_tokens_right(batch['labels']					,						self.tokenizer.pad_token_id							)

												self.assertIsInstance(_A					,						_A							)

												self.assertEqual((2, 1_4)					,						batch.input_ids.shape							)
												self.assertEqual((2, 1_4)					,						batch.attention_mask.shape							)
												__lowerCAmelCase					=							batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens					,						_A							)
												self.assertEqual(2					,						batch.decoder_input_ids[0, 0]							)  # decoder_start_token_id
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens					,						[EN_CODE]							)
												self.assertEqual(self.tokenizer.suffix_tokens					,						[self.tokenizer.eos_token_id]							)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer(self.src_text					,						padding=_A					,						truncation=_A					,						max_length=3					,						return_tensors='pt'							)
												__lowerCAmelCase					=							self.tokenizer(
												    text_target=self.tgt_text					,						padding=_A					,						truncation=_A					,						max_length=1_0					,						return_tensors='pt'							)
												__lowerCAmelCase					=							targets['input_ids']
												__lowerCAmelCase					=							shift_tokens_right(_A					,						self.tokenizer.pad_token_id							)

												self.assertEqual(batch.input_ids.shape[1]					,						3							)
												self.assertEqual(batch.decoder_input_ids.shape[1]					,						1_0							)




							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer._build_translation_inputs(
												    'A test'					,						return_tensors='pt'					,						src_lang='en_XX'					,						tgt_lang='ar_AR'							)

												self.assertEqual(
												    nested_simplify(_A							)					,						{
												        # en_XX, A, test, EOS
												        'input_ids': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
												        'attention_mask': [[1, 1, 1, 1]],
												        # ar_AR
												        'forced_bos_token_id': 2_5_0_0_0_1,
												    }					,						)









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     snake_case_			:


									"""simple docstring"""





									@staticmethod
									def        A__  ( *_A					,						**_A							):
														pass


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							hashlib.mda(image.tobytes()      )
					return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =MODEL_FOR_DEPTH_ESTIMATION_MAPPING




							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							DepthEstimationPipeline(model=_A					,						image_processor=_A							)
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
												self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}					,						_A							)
												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							)},
												    ]					,						_A					,						)




							@require_tf
							@unittest.skip('Depth estimation is not implemented in TF'							)
							def        A__  ( self							):
												pass




							@slow
							@require_torch
							def        A__  ( self							):
												__lowerCAmelCase					=							'Intel/dpt-large'
												__lowerCAmelCase					=							pipeline('depth-estimation'					,						model=_A							)
												__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        A__  ( self							):
												# This is highly irregular to have no small tests.
												self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)






