import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import TvltImageProcessor






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_=10							,	lowerCamelCase_=10							,	lowerCamelCase_=False							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case              =      []
			for i in range(image_processor_tester.num_frames  ):
						video.append(np.random.randint(255							,	size=(image_processor_tester.num_channels, width, height)							,	dtype=np.uinta  )  )

			if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						_snake_case              =      [Image.fromarray(np.moveaxis(lowerCamelCase_							,	0							,	-1  )  ) for frame in video]

			if torchify:
						_snake_case              =      [torch.from_numpy(lowerCamelCase_  ) for frame in video]

			return video






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_=False							,	lowerCamelCase_=False							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			_snake_case              =      []
			for i in range(image_processor_tester.batch_size  ):
						if equal_resolution:
									_snake_case              =      _snake_case              =      image_processor_tester.max_resolution
						else:
									_snake_case       , _snake_case              =      np.random.choice(
									    np.arange(image_processor_tester.min_resolution							,	image_processor_tester.max_resolution  )							,	2  )
									_snake_case              =      prepare_video(
									    image_processor_tester=lowerCamelCase_							,	width=lowerCamelCase_							,	height=lowerCamelCase_							,	numpify=lowerCamelCase_							,	torchify=lowerCamelCase_							,	)
						video_inputs.append(lowerCamelCase_  )

			return video_inputs

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=7  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=3  ,					__SCREAMING_SNAKE_CASE    :				List[str]=4  ,					__SCREAMING_SNAKE_CASE    :				Tuple=18  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=30  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=400  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None  ,					__SCREAMING_SNAKE_CASE    :				int=True  ,					__SCREAMING_SNAKE_CASE    :				Any=[0.5, 0.5, 0.5]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=[0.5, 0.5, 0.5]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					):






					"""simple docstring"""
					_snake_case              =      size if size is not None else {"shortest_edge": 18}
					_snake_case              =      crop_size if crop_size is not None else {"height": 18, "width": 18}

					_snake_case              =      parent
					_snake_case              =      batch_size
					_snake_case              =      num_channels
					_snake_case              =      num_frames
					_snake_case              =      image_size
					_snake_case              =      min_resolution
					_snake_case              =      max_resolution
					_snake_case              =      do_resize
					_snake_case              =      size
					_snake_case              =      do_normalize
					_snake_case              =      image_mean
					_snake_case              =      image_std
					_snake_case              =      do_center_crop
					_snake_case              =      crop_size







		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}

@require_torch
@require_vision
class 	_SCREAMING_SNAKE_CASE							(     _lowercase      ,     unittest.TestCase							):






		"""simple docstring"""







		A_						 =      TvltImageProcessor if is_vision_available() else None



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      TvltImageProcessorTester(self   )



		@property
		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"image_mean"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"image_std"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_normalize"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_resize"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_center_crop"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"size"   )   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PIL videos
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					Image.Image   )

					# Test not batched input
					_snake_case              =      image_processor(video_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE  ,					numpify=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					np.ndarray   )

					# Test not batched input
					_snake_case              =      image_processor(video_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)







		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE  ,					torchify=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					torch.Tensor   )

					# Test not batched input
					_snake_case              =      image_processor(video_inputs[0]  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)

					# Test batched
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					return_tensors="pt"   ).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )  ,					)


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


_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)  # pylint: disable=invalid-name


_SCREAMING_SNAKE_CASE:						Dict    	=       '''lfs-multipart-upload'''

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@staticmethod
		def 				_a				(			__SCREAMING_SNAKE_CASE    :				ArgumentParser   ):






					"""simple docstring"""
					_snake_case              =      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=__SCREAMING_SNAKE_CASE  ,					help="Local path to repository you want to configure."   )
					enable_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE   : LfsEnableCommand(__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      parser.add_parser(
					    __SCREAMING_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 __SCREAMING_SNAKE_CASE   : LfsUploadCommand(__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					warnings.warn(
					    "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."   )
					_snake_case              =      os.path.abspath(self.args.path   )
					if not os.path.isdir(__SCREAMING_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=__SCREAMING_SNAKE_CASE  ,					cwd=__SCREAMING_SNAKE_CASE   )
					subprocess.run(
					    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()  ,					check=__SCREAMING_SNAKE_CASE  ,					cwd=__SCREAMING_SNAKE_CASE  ,					)
					print("Local repo set up for largefiles"   )






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      json.dumps(lowerCamelCase_  ) + "\n"
			sys.stdout.write(lowerCamelCase_  )
			sys.stdout.flush()






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      filepath
					_snake_case              =      seek_from
					_snake_case              =      read_limit
					_snake_case              =      0



		def __enter__(			self    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      open(self.filepath  ,					"rb"   )
					self.f.seek(self.seek_from   )
					return self



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      os.fstat(self.f.fileno()   ).st_size
					return min(self.read_limit  ,					total_length - self.seek_from   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any=-1   ):






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



		def __iter__(			self    :				str   ):






					"""simple docstring"""
					yield self.read(n=4 * 1_024 * 1_024   )







		def __exit__(			self    :				Dict  ,					*__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					self.f.close()

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# Immediately after invoking a custom transfer process, git-lfs
					# sends initiation data to the process over stdin.
					# This tells the process useful information about the configuration.
					_snake_case              =      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:
								_snake_case              =      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   )

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

								_snake_case              =      []
								for i, presigned_url in enumerate(__SCREAMING_SNAKE_CASE   ):
											with FileSlice(__SCREAMING_SNAKE_CASE  ,					seek_from=i * chunk_size  ,					read_limit=__SCREAMING_SNAKE_CASE   ) as data:
														_snake_case              =      requests.put(__SCREAMING_SNAKE_CASE  ,					data=__SCREAMING_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.

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

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

