



import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
	from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.config_class(**self.inputs_dict		)
									self.parent.assertTrue(hasattr(_snake_case						,    """hidden_sizes"""		)		)
									self.parent.assertTrue(hasattr(_snake_case						,    """num_attention_heads"""		)		)
									self.parent.assertTrue(hasattr(_snake_case						,    """num_encoder_blocks"""		)		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case						,    _snake_case=13						,    _snake_case=64						,    _snake_case=3						,    _snake_case=4						,    _snake_case=[2, 2, 2, 2]						,    _snake_case=[8, 4, 2, 1]						,    _snake_case=[16, 32, 64, 128]						,    _snake_case=[1, 4, 8, 16]						,    _snake_case=[1, 2, 4, 8]						,    _snake_case=True						,    _snake_case=True						,    _snake_case="gelu"						,    _snake_case=0.1						,    _snake_case=0.1						,    _snake_case=0.02						,    _snake_case=16						,    _snake_case=3						,    _snake_case=None						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   parent
									_lowerCAmelCase      =   batch_size
									_lowerCAmelCase      =   image_size
									_lowerCAmelCase      =   num_channels
									_lowerCAmelCase      =   num_encoder_blocks
									_lowerCAmelCase      =   sr_ratios
									_lowerCAmelCase      =   depths
									_lowerCAmelCase      =   hidden_sizes
									_lowerCAmelCase      =   downsampling_rates
									_lowerCAmelCase      =   num_attention_heads
									_lowerCAmelCase      =   is_training
									_lowerCAmelCase      =   use_labels
									_lowerCAmelCase      =   hidden_act
									_lowerCAmelCase      =   hidden_dropout_prob
									_lowerCAmelCase      =   attention_probs_dropout_prob
									_lowerCAmelCase      =   initializer_range
									_lowerCAmelCase      =   decoder_hidden_size
									_lowerCAmelCase      =   num_labels
									_lowerCAmelCase      =   scope




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_lowerCAmelCase      =   None
									if self.use_labels:
														_lowerCAmelCase      =   ids_tensor([self.batch_size, self.image_size, self.image_size]						,    self.num_labels		)

									_lowerCAmelCase      =   self.get_config()
									return config, pixel_values, labels




				def 							snake_case       (							self		):



									"""simple docstring"""





									return GLPNConfig(
									    image_size=self.image_size						,    num_channels=self.num_channels						,    num_encoder_blocks=self.num_encoder_blocks						,    depths=self.depths						,    hidden_sizes=self.hidden_sizes						,    num_attention_heads=self.num_attention_heads						,    hidden_act=self.hidden_act						,    hidden_dropout_prob=self.hidden_dropout_prob						,    attention_probs_dropout_prob=self.attention_probs_dropout_prob						,    initializer_range=self.initializer_range						,    decoder_hidden_size=self.decoder_hidden_size						,    )




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									_lowerCAmelCase      =   model(_snake_case		)
									_lowerCAmelCase      =   _lowerCAmelCase      =   self.image_size // (self.downsampling_rates[-1] * 2)
									self.parent.assertEqual(
									    result.last_hidden_state.shape						,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.num_labels
									_lowerCAmelCase      =   GLPNForDepthEstimation(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									_lowerCAmelCase      =   model(_snake_case		)
									self.parent.assertEqual(result.predicted_depth.shape						,    (self.batch_size, self.image_size, self.image_size)		)
									_lowerCAmelCase      =   model(_snake_case						,    labels=_snake_case		)
									self.parent.assertEqual(result.predicted_depth.shape						,    (self.batch_size, self.image_size, self.image_size)		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.prepare_config_and_inputs()
									_lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase      =   config_and_inputs
									_lowerCAmelCase      =   {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class  __lowerCAmelCase			(					lowerCamelCase__	,						lowerCamelCase__	,						unittest.TestCase	):
				__lowerCamelCase			=    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
				__lowerCamelCase			=    (
				    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
				)

				__lowerCamelCase			=    False
				__lowerCamelCase			=    False
				__lowerCamelCase			=    False




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNModelTester(self		)
									_lowerCAmelCase      =   GLPNConfigTester(self						,    config_class=_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_snake_case		)




				@unittest.skip("""GLPN does not use inputs_embeds"""		)
				def 							snake_case       (							self		):



									"""simple docstring"""





									pass




				@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""		)
				def 							snake_case       (							self		):



									"""simple docstring"""





									pass




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase     ,     _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase      =   model_class(_snake_case		)
														_lowerCAmelCase      =   inspect.signature(model.forward		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_lowerCAmelCase      =   [*signature.parameters.keys()]

														_lowerCAmelCase      =   ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,    _snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase     ,     _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCAmelCase      =   True

									for model_class in self.all_model_classes:
														_lowerCAmelCase      =   True
														_lowerCAmelCase      =   False
														_lowerCAmelCase      =   True
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase      =   model(**self._prepare_for_class(_snake_case						,    _snake_case		)		)
														_lowerCAmelCase      =   outputs.attentions

														_lowerCAmelCase      =   sum(self.model_tester.depths		)
														self.assertEqual(len(_snake_case		)						,    _snake_case		)

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														_lowerCAmelCase      =   True
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase      =   model(**self._prepare_for_class(_snake_case						,    _snake_case		)		)
														_lowerCAmelCase      =   outputs.attentions

														self.assertEqual(len(_snake_case		)						,    _snake_case		)

														# verify the first attentions (first block, first layer)
														_lowerCAmelCase      =   (self.model_tester.image_size // 4) ** 2
														_lowerCAmelCase      =   (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
														self.assertListEqual(
														    list(attentions[0].shape[-3:]		)						,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,    )

														# verify the last attentions (last block, last layer)
														_lowerCAmelCase      =   (self.model_tester.image_size // 32) ** 2
														_lowerCAmelCase      =   (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
														self.assertListEqual(
														    list(attentions[-1].shape[-3:]		)						,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]						,    )
														_lowerCAmelCase      =   len(_snake_case		)

														# Check attention is always last and order is fine
														_lowerCAmelCase      =   True
														_lowerCAmelCase      =   True
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.eval()
														with torch.no_grad():
																			_lowerCAmelCase      =   model(**self._prepare_for_class(_snake_case						,    _snake_case		)		)

														self.assertEqual(out_len + 1						,    len(_snake_case		)		)

														_lowerCAmelCase      =   outputs.attentions

														self.assertEqual(len(_snake_case		)						,    _snake_case		)
														# verify the first attentions (first block, first layer)
														_lowerCAmelCase      =   (self.model_tester.image_size // 4) ** 2
														_lowerCAmelCase      =   (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]		)						,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]						,    )




				def 							snake_case       (							self		):



									"""simple docstring"""





									def check_hidden_states_output(_snake_case						,    _snake_case						,    _snake_case		):
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.eval()

														with torch.no_grad():
																			_lowerCAmelCase      =   model(**self._prepare_for_class(_snake_case						,    _snake_case		)		)

														_lowerCAmelCase      =   outputs.hidden_states

														_lowerCAmelCase      =   self.model_tester.num_encoder_blocks
														self.assertEqual(len(_snake_case		)						,    _snake_case		)

														# verify the first hidden states (first block)
														self.assertListEqual(
														    list(hidden_states[0].shape[-3:]		)						,    [
														        self.model_tester.hidden_sizes[0],
														        self.model_tester.image_size // 4,
														        self.model_tester.image_size // 4,
														    ]						,    )

									_lowerCAmelCase     ,     _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														_lowerCAmelCase      =   True
														check_hidden_states_output(_snake_case						,    _snake_case						,    _snake_case		)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_lowerCAmelCase      =   True

														check_hidden_states_output(_snake_case						,    _snake_case						,    _snake_case		)




				def 							snake_case       (							self		):



									"""simple docstring"""





									if not self.model_tester.is_training:
														return

									_lowerCAmelCase     ,     _lowerCAmelCase      =   self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCAmelCase      =   True

									for model_class in self.all_model_classes:
														if model_class in get_values(_snake_case		):
																			continue
														# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
														# this can then be incorporated into _prepare_for_class in test_modeling_common.py
														if model_class.__name__ == "GLPNForDepthEstimation":
																			_lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase     ,     _lowerCAmelCase      =   inputs_dict["""pixel_values"""].shape
																			_lowerCAmelCase      =   torch.zeros(
																			    [self.model_tester.batch_size, height, width]						,    device=_snake_case		).long()
														_lowerCAmelCase      =   model_class(_snake_case		)
														model.to(_snake_case		)
														model.train()
														_lowerCAmelCase      =   self._prepare_for_class(_snake_case						,    _snake_case						,    return_labels=_snake_case		)
														_lowerCAmelCase      =   model(**_snake_case		).loss
														loss.backward()




				@slow
				def 							snake_case       (							self		):



									"""simple docstring"""





									for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_lowerCAmelCase      =   GLPNModel.from_pretrained(_snake_case		)
														self.assertIsNotNone(_snake_case		)


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
					return image





@require_torch
@require_vision
@slow
class  __lowerCAmelCase			(					unittest.TestCase	):




				@slow
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
									_lowerCAmelCase      =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_snake_case		)

									_lowerCAmelCase      =   prepare_img()
									_lowerCAmelCase      =   image_processor(images=_snake_case						,    return_tensors="""pt"""		).to(_snake_case		)

									# forward pass
									with torch.no_grad():
														_lowerCAmelCase      =   model(**_snake_case		)

									# verify the predicted depth
									_lowerCAmelCase      =   torch.Size([1, 480, 640]		)
									self.assertEqual(outputs.predicted_depth.shape						,    _snake_case		)

									_lowerCAmelCase      =   torch.tensor(
									    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		).to(_snake_case		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]						,    _snake_case						,    atol=1e-4		)		)








import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


A__     				=	logging.get_logger(__name__)  # pylint: disable=invalid-name


A__     				=	"""lfs-multipart-upload"""





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@staticmethod
				def 							snake_case       (							_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   parser.add_parser(
									    """lfs-enable-largefiles"""						,    help=(
									        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
									    )						,    )
									enable_parser.add_argument("""path"""						,    type=_snake_case						,    help="""Local path to repository you want to configure."""		)
									enable_parser.set_defaults(func=lambda _snake_case		: LfsEnableCommand(_snake_case		)		)

									_lowerCAmelCase      =   parser.add_parser(
									    _snake_case						,    help=(
									        """Deprecated: use `huggingface-cli` instead. """
									        """Command will get called by git-lfs, do not call it directly."""
									    )						,    )
									upload_parser.set_defaults(func=lambda _snake_case		: LfsUploadCommand(_snake_case		)		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									warnings.warn(
									    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""		)
									_lowerCAmelCase      =   os.path.abspath(self.args.path		)
									if not os.path.isdir(_snake_case		):
														print("""This does not look like a valid git repo."""		)
														exit(1		)
									subprocess.run(
									    """git config lfs.customtransfer.multipart.path transformers-cli""".split()						,    check=_snake_case						,    cwd=_snake_case		)
									subprocess.run(
									    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()						,    check=_snake_case						,    cwd=_snake_case						,    )
									print("""Local repo set up for largefiles"""		)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   json.dumps(snake_case      ) + """\n"""
					sys.stdout.write(snake_case      )
					sys.stdout.flush()


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()      )

					if "terminate" in (msg.get("""type"""      ), msg.get("""event"""      )):
										# terminate message received
										return None

					if msg.get("""event"""      ) not in ("download", "upload"):
										logger.critical("""Received unexpected message"""      )
										sys.exit(1      )

					return msg





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   filepath
									_lowerCAmelCase      =   seek_from
									_lowerCAmelCase      =   read_limit
									_lowerCAmelCase      =   0




				def __enter__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   open(self.filepath						,    """rb"""		)
									self.f.seek(self.seek_from		)
									return self




				def __len__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   os.fstat(self.f.fileno()		).st_size
									return min(self.read_limit						,    total_length - self.seek_from		)




				def 							snake_case       (							self						,    _snake_case=-1		):



									"""simple docstring"""





									if self.n_seen >= self.read_limit:
														return b""
									_lowerCAmelCase      =   self.read_limit - self.n_seen
									_lowerCAmelCase      =   self.f.read(remaining_amount if n < 0 else min(_snake_case						,    _snake_case		)		)
									self.n_seen += len(_snake_case		)
									return data




				def __iter__(							self		):



									"""simple docstring"""





									yield self.read(n=4 * 1024 * 1024		)




				def __exit__(							self						,    *_snake_case		):



									"""simple docstring"""





									self.f.close()





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()		)
									if not (init_msg.get("""event"""		) == "init" and init_msg.get("""operation"""		) == "upload"):
														write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}		)
														sys.exit(1		)

									# The transfer process should use the information it needs from the
									# initiation structure, and also perform any one-off setup tasks it
									# needs to do. It should then respond on stdout with a simple empty
									# confirmation structure, as follows:
									write_msg({}		)

									# After the initiation exchange, git-lfs will send any number of
									# transfer requests to the stdin of the transfer process, in a serial sequence.
									while True:
														_lowerCAmelCase      =   read_msg()
														if msg is None:
																			# When all transfers have been processed, git-lfs will send
																			# a terminate event to the stdin of the transfer process.
																			# On receiving this message the transfer process should
																			# clean up and terminate. No response is expected.
																			sys.exit(0		)

														_lowerCAmelCase      =   msg["""oid"""]
														_lowerCAmelCase      =   msg["""path"""]
														_lowerCAmelCase      =   msg["""action"""]["""href"""]
														_lowerCAmelCase      =   msg["""action"""]["""header"""]
														_lowerCAmelCase      =   int(header.pop("""chunk_size"""		)		)
														_lowerCAmelCase      =   list(header.values()		)

														_lowerCAmelCase      =   []
														for i, presigned_url in enumerate(_snake_case		):
																			with FileSlice(_snake_case						,    seek_from=i * chunk_size						,    read_limit=_snake_case		) as data:
																								_lowerCAmelCase      =   requests.put(_snake_case						,    data=_snake_case		)
																								r.raise_for_status()
																								parts.append(
																								    {
																								        """etag""": r.headers.get("""etag"""		),
																								        """partNumber""": i + 1,
																								    }		)
																								# In order to support progress reporting while data is uploading / downloading,
																								# the transfer process should post messages to stdout
																								write_msg(
																								    {
																								        """event""": """progress""",
																								        """oid""": oid,
																								        """bytesSoFar""": (i + 1) * chunk_size,
																								        """bytesSinceLast""": chunk_size,
																								    }		)
																								# Not precise but that's ok.

														_lowerCAmelCase      =   requests.post(
														    _snake_case						,    json={
														        """oid""": oid,
														        """parts""": parts,
														    }						,    )
														r.raise_for_status()

														write_msg({"""event""": """complete""", """oid""": oid}		)



