





'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   tempfile.mkdtemp()

			# fmt: off
			lowerCAmelCase     :    Optional[Any]						=   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
			# fmt: on
			lowerCAmelCase     :    Optional[int]						=   os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]	)
			with open(self.vocab_file		,      "w"		,      encoding="utf-8"	) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

			lowerCAmelCase     :    Tuple						=   {
			    "do_resize": True,
			    "size": {"height": 18, "width": 18},
			    "do_normalize": True,
			    "image_mean": [0.5, 0.5, 0.5],
			    "image_std": [0.5, 0.5, 0.5],
			}
			lowerCAmelCase     :    Tuple						=   os.path.join(self.tmpdirname		,      lowerCamelCase__	)
			with open(self.image_processor_file		,      "w"		,      encoding="utf-8"	) as fp:
					json.dump(lowerCamelCase__		,      lowerCamelCase__	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return BertTokenizer.from_pretrained(self.tmpdirname		,      **lowerCamelCase__	)



	def   _lowerCamelCase    (   self		,      **a_	):
			return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **lowerCamelCase__	)



	def   _lowerCamelCase    (   self	):
			shutil.rmtree(self.tmpdirname	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   [np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta	)]

			lowerCAmelCase     :    str						=   [Image.fromarray(np.moveaxis(lowerCamelCase__		,      0		,      -1	)	) for x in image_inputs]

			return image_inputs



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.get_tokenizer()
			lowerCAmelCase     :    List[str]						=   self.get_image_processor()

			lowerCAmelCase     :    Optional[int]						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			processor.save_pretrained(self.tmpdirname	)
			lowerCAmelCase     :    Union[str, Any]						=   VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

			self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer.get_vocab()	)
			self.assertIsInstance(processor.tokenizer		,      (BertTokenizer, BertTokenizerFast)	)

			self.assertEqual(processor.image_processor.to_json_string()		,      image_processor.to_json_string()	)
			self.assertIsInstance(processor.image_processor		,      lowerCamelCase__	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   VisionTextDualEncoderProcessor(
			    tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()	)
			processor.save_pretrained(self.tmpdirname	)

			lowerCAmelCase     :    Dict						=   self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"	)
			lowerCAmelCase     :    Optional[int]						=   self.get_image_processor(do_normalize=lowerCamelCase__		,      padding_value=1.0	)

			lowerCAmelCase     :    int						=   VisionTextDualEncoderProcessor.from_pretrained(
			    self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCamelCase__		,      padding_value=1.0	)

			self.assertEqual(processor.tokenizer.get_vocab()		,      tokenizer_add_kwargs.get_vocab()	)
			self.assertIsInstance(processor.tokenizer		,      (BertTokenizer, BertTokenizerFast)	)

			self.assertEqual(processor.image_processor.to_json_string()		,      image_processor_add_kwargs.to_json_string()	)
			self.assertIsInstance(processor.image_processor		,      lowerCamelCase__	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.get_image_processor()
			lowerCAmelCase     :    Any						=   self.get_tokenizer()

			lowerCAmelCase     :    Optional[Any]						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			lowerCAmelCase     :    int						=   self.prepare_image_inputs()

			lowerCAmelCase     :    Dict						=   image_processor(lowerCamelCase__		,      return_tensors="np"	)
			lowerCAmelCase     :    List[str]						=   processor(images=lowerCamelCase__		,      return_tensors="np"	)

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()		,      input_processor[key].sum()		,      delta=1e-2	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   self.get_image_processor()
			lowerCAmelCase     :    str						=   self.get_tokenizer()

			lowerCAmelCase     :    Optional[Any]						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			lowerCAmelCase     :    Union[str, Any]						=   "lower newer"

			lowerCAmelCase     :    int						=   processor(text=lowerCamelCase__	)

			lowerCAmelCase     :    Optional[Any]						=   tokenizer(lowerCamelCase__	)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]		,      encoded_processor[key]	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   self.get_image_processor()
			lowerCAmelCase     :    str						=   self.get_tokenizer()

			lowerCAmelCase     :    Union[str, Any]						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			lowerCAmelCase     :    Any						=   "lower newer"
			lowerCAmelCase     :    List[str]						=   self.prepare_image_inputs()

			lowerCAmelCase     :    int						=   processor(text=lowerCamelCase__		,      images=lowerCamelCase__	)

			self.assertListEqual(list(inputs.keys()	)		,      ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

			# test if it raises when no input is passed
			with self.assertRaises(lowerCamelCase__	):
					processor()



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   self.get_image_processor()
			lowerCAmelCase     :    Any						=   self.get_tokenizer()

			lowerCAmelCase     :    Optional[Any]						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			lowerCAmelCase     :    int						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowerCAmelCase     :    str						=   processor.batch_decode(lowerCamelCase__	)
			lowerCAmelCase     :    str						=   tokenizer.batch_decode(lowerCamelCase__	)

			self.assertListEqual(lowerCamelCase__		,      lowerCamelCase__	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   self.get_image_processor()
			lowerCAmelCase     :    Any						=   self.get_tokenizer()

			lowerCAmelCase     :    Tuple						=   VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase__		,      image_processor=lowerCamelCase__	)

			lowerCAmelCase     :    List[Any]						=   "lower newer"
			lowerCAmelCase     :    List[str]						=   self.prepare_image_inputs()

			lowerCAmelCase     :    str						=   processor(text=lowerCamelCase__		,      images=lowerCamelCase__	)

			self.assertListEqual(list(inputs.keys()	)		,      processor.model_input_names	)










'''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"



