





'''simple docstring'''






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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor
class 		lowerCamelCase  (      unittest.TestCase       ):



	def __init__(   self		,      a_		,      a_=7		,      a_=3		,      a_=18		,      a_=30		,      a_=400		,      a_=True		,      a_=None		,      a_=True		,      a_=None		,      ):
			lowerCAmelCase     :    Any						=   size if size is not None else {"shortest_edge": 20}
			lowerCAmelCase     :    Any						=   crop_size if crop_size is not None else {"height": 18, "width": 18}
			lowerCAmelCase     :    Tuple						=   parent
			lowerCAmelCase     :    Optional[Any]						=   batch_size
			lowerCAmelCase     :    Optional[int]						=   num_channels
			lowerCAmelCase     :    List[str]						=   image_size
			lowerCAmelCase     :    Union[str, Any]						=   min_resolution
			lowerCAmelCase     :    Optional[int]						=   max_resolution
			lowerCAmelCase     :    int						=   do_resize
			lowerCAmelCase     :    Dict						=   size
			lowerCAmelCase     :    List[Any]						=   do_center_crop
			lowerCAmelCase     :    Optional[Any]						=   crop_size





	def   _lowerCamelCase    (   self	):
			return {
			    "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 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   MobileNetVaImageProcessor if is_vision_available() else None



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   MobileNetVaImageProcessingTester(self	)



	@property
	def   _lowerCamelCase    (   self	):
			return self.image_processor_tester.prepare_image_processor_dict()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.image_processing_class(**self.image_processor_dict	)
			self.assertTrue(hasattr(a_		,      "do_resize"	)	)
			self.assertTrue(hasattr(a_		,      "size"	)	)
			self.assertTrue(hasattr(a_		,      "do_center_crop"	)	)
			self.assertTrue(hasattr(a_		,      "crop_size"	)	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   self.image_processing_class.from_dict(self.image_processor_dict	)
			self.assertEqual(image_processor.size		,      {"shortest_edge": 20}	)
			self.assertEqual(image_processor.crop_size		,      {"height": 18, "width": 18}	)

			lowerCAmelCase     :    Any						=   self.image_processing_class.from_dict(self.image_processor_dict		,      size=42		,      crop_size=84	)
			self.assertEqual(image_processor.size		,      {"shortest_edge": 42}	)
			self.assertEqual(image_processor.crop_size		,      {"height": 84, "width": 84}	)



	def   _lowerCamelCase    (   self	):
			pass



	def   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    Tuple						=   self.image_processing_class(**self.image_processor_dict	)
			# create random PIL images
			lowerCAmelCase     :    int						=   prepare_image_inputs(self.image_processor_tester		,      equal_resolution=a_	)
			for image in image_inputs:
					self.assertIsInstance(a_		,      Image.Image	)

			# Test not batched input
			lowerCAmelCase     :    List[str]						=   image_processing(image_inputs[0]		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )

			# Test batched
			lowerCAmelCase     :    int						=   image_processing(a_		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )



	def   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    List[str]						=   self.image_processing_class(**self.image_processor_dict	)
			# create random numpy tensors
			lowerCAmelCase     :    int						=   prepare_image_inputs(self.image_processor_tester		,      equal_resolution=a_		,      numpify=a_	)
			for image in image_inputs:
					self.assertIsInstance(a_		,      np.ndarray	)

			# Test not batched input
			lowerCAmelCase     :    int						=   image_processing(image_inputs[0]		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )

			# Test batched
			lowerCAmelCase     :    Dict						=   image_processing(a_		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )





	def   _lowerCamelCase    (   self	):
			# Initialize image_processing
			lowerCAmelCase     :    Dict						=   self.image_processing_class(**self.image_processor_dict	)
			# create random PyTorch tensors
			lowerCAmelCase     :    Optional[int]						=   prepare_image_inputs(self.image_processor_tester		,      equal_resolution=a_		,      torchify=a_	)
			for image in image_inputs:
					self.assertIsInstance(a_		,      torch.Tensor	)

			# Test not batched input
			lowerCAmelCase     :    Union[str, Any]						=   image_processing(image_inputs[0]		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )

			# Test batched
			lowerCAmelCase     :    Tuple						=   image_processing(a_		,      return_tensors="pt"	).pixel_values
			self.assertEqual(
			    encoded_images.shape		,      (
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size["height"],
			        self.image_processor_tester.crop_size["width"],
			    )		,      )










'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand






def      __A						(							a_						:  int	):
		return EnvironmentCommand()






def      __A						(							a_						:  Tuple	):
		return EnvironmentCommand(args.accelerate_config_file	)

class 		lowerCamelCase  (      _A       ):



	@staticmethod
	def   _lowerCamelCase    (   a_	):
			lowerCAmelCase     :    int						=   parser.add_parser("env"	)
			download_parser.set_defaults(func=a_	)
			download_parser.add_argument(
			    "--accelerate-config_file"		,      default=a_		,      help="The accelerate config file to use for the default values in the launching script."		,      )
			download_parser.set_defaults(func=a_	)



	def __init__(   self		,      a_		,      *a_	):
			lowerCAmelCase     :    List[str]						=   accelerate_config_file



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "not installed"
			if is_safetensors_available():
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   safetensors.__version__
			elif importlib.util.find_spec("safetensors"	) is not None:
					import safetensors

					lowerCAmelCase     :    Optional[int]						=   F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    List[str]						=   "not found"
			if is_accelerate_available():
					import accelerate
					from accelerate.commands.config import default_config_file, load_config_from_file

					lowerCAmelCase     :    int						=   accelerate.__version__
					# Get the default from the config file.
					if self._accelerate_config_file is not None or os.path.isfile(a_	):
							lowerCAmelCase     :    int						=   load_config_from_file(self._accelerate_config_file	).to_dict()

					lowerCAmelCase     :    Optional[int]						=   (
					    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
					    if isinstance(a_		,      a_	)
					    else F'''\t{accelerate_config}'''
					)

			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Union[str, Any]						=   "NA"
			if is_torch_available():
					import torch

					lowerCAmelCase     :    Optional[Any]						=   torch.__version__
					lowerCAmelCase     :    List[Any]						=   torch.cuda.is_available()

			lowerCAmelCase     :    int						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_tf_available():
					import tensorflow as tf

					lowerCAmelCase     :    Optional[int]						=   tf.__version__
					try:
							# deprecated in v2.1
							lowerCAmelCase     :    List[str]						=   tf.test.is_gpu_available()
					except AttributeError:
							# returns list of devices, convert to bool
							lowerCAmelCase     :    List[Any]						=   bool(tf.config.list_physical_devices("GPU"	)	)

			lowerCAmelCase     :    Union[str, Any]						=   "not installed"
			lowerCAmelCase     :    Any						=   "not installed"
			lowerCAmelCase     :    str						=   "not installed"
			lowerCAmelCase     :    Dict						=   "NA"
			if is_flax_available():
					import flax
					import jax
					import jaxlib

					lowerCAmelCase     :    Union[str, Any]						=   flax.__version__
					lowerCAmelCase     :    int						=   jax.__version__
					lowerCAmelCase     :    Optional[Any]						=   jaxlib.__version__
					lowerCAmelCase     :    int						=   jax.lib.xla_bridge.get_backend().platform

			lowerCAmelCase     :    Optional[Any]						=   {
			    "`transformers` version": version,
			    "Platform": platform.platform(),
			    "Python version": platform.python_version(),
			    "Huggingface_hub version": huggingface_hub.__version__,
			    "Safetensors version": F'''{safetensors_version}''',
			    "Accelerate version": F'''{accelerate_version}''',
			    "Accelerate config": F'''{accelerate_config_str}''',
			    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
			    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
			    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
			    "Jax version": F'''{jax_version}''',
			    "JaxLib version": F'''{jaxlib_version}''',
			    "Using GPU in script?": "<fill in>",
			    "Using distributed or parallel set-up in script?": "<fill in>",
			}

			print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"	)
			print(self.format_dict(a_	)	)

			return info





	@staticmethod
	def   _lowerCamelCase    (   a_	):
			return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"



