





'''simple docstring'''






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "BlipImageProcessor"
	snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



	def __init__(   self		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   False
			super().__init__(a_		,      a_	)
			lowerCAmelCase     :    Tuple						=   self.image_processor



	def __call__(   self		,      a_ = None		,      a_ = None		,      a_ = True		,      a_ = False		,      a_ = None		,      a_ = None		,      a_ = 0		,      a_ = None		,      a_ = None		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = False		,      a_ = True		,      a_ = None		,      **a_		,      ):
			if images is None and text is None:
					raise ValueError("You have to specify either images or text."	)

			# Get only text
			if images is None:
					lowerCAmelCase     :    Union[str, Any]						=   self.tokenizer
					lowerCAmelCase     :    str						=   self.tokenizer(
					    text=a_		,      add_special_tokens=a_		,      padding=a_		,      truncation=a_		,      max_length=a_		,      stride=a_		,      pad_to_multiple_of=a_		,      return_attention_mask=a_		,      return_overflowing_tokens=a_		,      return_special_tokens_mask=a_		,      return_offsets_mapping=a_		,      return_token_type_ids=a_		,      return_length=a_		,      verbose=a_		,      return_tensors=a_		,      **a_		,      )
					return text_encoding

			# add pixel_values
			lowerCAmelCase     :    Union[str, Any]						=   self.image_processor(a_		,      return_tensors=a_	)

			if text is not None:
					lowerCAmelCase     :    List[str]						=   self.tokenizer(
					    text=a_		,      add_special_tokens=a_		,      padding=a_		,      truncation=a_		,      max_length=a_		,      stride=a_		,      pad_to_multiple_of=a_		,      return_attention_mask=a_		,      return_overflowing_tokens=a_		,      return_special_tokens_mask=a_		,      return_offsets_mapping=a_		,      return_token_type_ids=a_		,      return_length=a_		,      verbose=a_		,      return_tensors=a_		,      **a_		,      )
			else:
					lowerCAmelCase     :    str						=   None

			if text_encoding is not None:
					encoding_image_processor.update(a_	)

			return encoding_image_processor



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*a_		,      **a_	)





	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Tuple						=   self.tokenizer.model_input_names
			lowerCAmelCase     :    List[str]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_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"



