from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__lowerCamelCase					:	Optional[int]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Tuple       =  {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}






class 		a__						(					A__					):
					A	       =							'perceiver'

					def __init__(    self      :       List[Any],_A      :       Tuple=256,_A      :       str=1280,_A      :       List[Any]=768,_A      :       Union[str, Any]=1,_A      :       Union[str, Any]=26,_A      :       List[str]=8,_A      :       List[Any]=8,_A      :       List[Any]=None,_A      :       List[Any]=None,_A      :       Union[str, Any]="kv",_A      :       Any=1,_A      :       int=1,_A      :       Dict="gelu",_A      :       Any=0.1,_A      :       int=0.02,_A      :       int=1E-12,_A      :       Any=True,_A      :       Optional[Any]=262,_A      :       List[Any]=2048,_A      :       str=56,_A      :       Optional[int]=[368, 496],_A      :       Dict=16,_A      :       Tuple=1920,_A      :       List[Any]=16,_A      :       str=[1, 16, 224, 224],**_A      :       Optional[Any],):







							"""simple docstring"""






							super().__init__(**_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    num_latents
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    d_latents
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    d_model
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_blocks
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_self_attends_per_block
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_self_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    num_cross_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    qk_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    v_channels
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_shape_for_attention
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		Any				      =    cross_attention_widening_factor
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Any				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    layer_norm_eps
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    use_query_residual
							# masked language modeling attributes
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max_position_embeddings
							# image classification attributes
							SCREAMING_SNAKE_CASE_  :		Dict				      =    image_size
							# flow attributes
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    train_size
							# multimodal autoencoding attributes
							SCREAMING_SNAKE_CASE_  :		str				      =    num_frames
							SCREAMING_SNAKE_CASE_  :		Any				      =    audio_samples_per_frame
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    samples_per_patch
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    output_shape







class 		a__						(					A__					):

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    {0: "batch", 1: "choice", 2: "sequence"}
							else:
									SCREAMING_SNAKE_CASE_  :		str				      =    {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("inputs", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]    )

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







							"""simple docstring"""






							return 1E-4






					def 				__UpperCamelCase   (    self      :       List[str],_A      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],_A      :       int = -1,_A      :       int = -1,_A      :       int = -1,_A      :       bool = False,_A      :       Optional[TensorType] = None,_A      :       int = 3,_A      :       int = 40,_A      :       int = 40,):







							"""simple docstring"""






							if isinstance(_A,_A    ):
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    compute_effective_axis_dimension(
									    _A,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0    )
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    preprocessor.num_special_tokens_to_add(_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    compute_effective_axis_dimension(
									    _A,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=_A    )
									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [" ".join(["a"]    ) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE_  :		str				      =    dict(preprocessor(_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    inputs.pop("input_ids"    )
									return inputs
							elif isinstance(_A,_A    ) and preprocessor.model_input_names[0] == "pixel_values":
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									SCREAMING_SNAKE_CASE_  :		Any				      =    compute_effective_axis_dimension(_A,fixed_dimension=OnnxConfig.default_fixed_batch    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self._generate_dummy_images(_A,_A,_A,_A    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    dict(preprocessor(images=_A,return_tensors=_A    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    inputs.pop("pixel_values"    )
									return inputs
							else:
									raise ValueError(
									    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )


'''simple docstring'''







import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__lowerCAmelCase         =    (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


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





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Optional[int]			=   'https://pypi.org/pypi/diffusers/json'
   _a				:	int			=   json.loads(request.urlopen(__a ).read() )['releases'].keys()
   return sorted(__a ,	key=lambda __a : version.Version(__a ) )





def 					UpperCAmelCase_		():






   """simple docstring"""

   if HF_MODULES_CACHE in sys.path:
      return

   sys.path.append(__a )
   os.makedirs(__a ,	exist_ok=__a )
   _a				:	str			=   Path(__a ) / '__init__.py'
   if not init_path.exists():
      init_path.touch()





def 					UpperCAmelCase_		(__a      :    Union[str, os.PathLike] ):






   """simple docstring"""

   init_hf_modules()
   _a				:	Dict			=   Path(__a ) / name
   # If the parent module does not exist yet, recursively create it.
   if not dynamic_module_path.parent.exists():
      create_dynamic_module(dynamic_module_path.parent )
   os.makedirs(__a ,	exist_ok=__a )
   _a				:	Optional[int]			=   dynamic_module_path / '__init__.py'
   if not init_path.exists():
      init_path.touch()





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   with open(__a ,	'r' ,	encoding='utf-8' ) as f:
      _a				:	int			=   f.read()

   # Imports of the form `import .xxx`
   _a				:	Tuple			=   re.findall('^\s*import\s+\.(\S+)\s*$' ,	__a ,	flags=re.MULTILINE )
   # Imports of the form `from .xxx import yyy`
   relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import' ,	__a ,	flags=re.MULTILINE )
   # Unique-ify
   return list(set(__a ) )





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

   _a				:	Optional[int]			=   False
   _a				:	Optional[int]			=   [module_file]
   _a				:	List[str]			=   []

   # Let's recurse through all relative imports
   while not no_change:
      _a				:	str			=   []
      for f in files_to_check:
         new_imports.extend(get_relative_imports(__a ) )

      _a				:	Union[str, Any]			=   Path(__a ).parent
      _a				:	str			=   [str(module_path / m ) for m in new_imports]
      _a				:	Tuple			=   [f for f in new_import_files if f not in all_relative_imports]
      _a				:	Dict			=   [f"""{f}.py""" for f in new_import_files]

      _a				:	List[str]			=   len(__a ) == 0
      all_relative_imports.extend(__a )

   return all_relative_imports





def 					UpperCAmelCase_		(__a      :    Tuple ):






   """simple docstring"""

   with open(__a ,	'r' ,	encoding='utf-8' ) as f:
      _a				:	Dict			=   f.read()

   # Imports of the form `import xxx`
   _a				:	Optional[int]			=   re.findall('^\s*import\s+(\S+)\s*$' ,	__a ,	flags=re.MULTILINE )
   # Imports of the form `from xxx import yyy`
   imports += re.findall('^\s*from\s+(\S+)\s+import' ,	__a ,	flags=re.MULTILINE )
   # Only keep the top-level module
   _a				:	List[str]			=   [imp.split('.' )[0] for imp in imports if not imp.startswith('.' )]

   # Unique-ify and test we got them all
   _a				:	Optional[int]			=   list(set(__a ) )
   _a				:	List[str]			=   []
   for imp in imports:
      try:
         importlib.import_module(__a )
      except ImportError:
         missing_packages.append(__a )

   if len(__a ) > 0:
      raise ImportError(
          'This modeling file requires the following packages that were not found in your environment: '
          f"""{', '.join(__a )}. Run `pip install {' '.join(__a )}`""" )

   return get_relative_imports(__a )





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    str ):






   """simple docstring"""

   _a				:	Any			=   module_path.replace(os.path.sep ,	'.' )
   _a				:	Union[str, Any]			=   importlib.import_module(__a )

   if class_name is None:
      return find_pipeline_class(__a )
   return getattr(__a ,	__a )





def 					UpperCAmelCase_		(__a      :    Optional[int] ):






   """simple docstring"""

   from ..pipelines import DiffusionPipeline

   _a				:	List[str]			=   dict(inspect.getmembers(__a ,	inspect.isclass ) )

   _a				:	str			=   None
   for cls_name, cls in cls_members.items():
      if (
          cls_name != DiffusionPipeline.__name__
          and issubclass(cls ,	__a )
          and cls.__module__.split('.' )[0] != "diffusers"
      ):
         if pipeline_class is not None:
            raise ValueError(
                f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
                f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
                f""" {loaded_module}.""" )
         _a				:	Any			=   cls

   return pipeline_class





def 					UpperCAmelCase_		(__a      :    Union[str, os.PathLike] ,	__a      :    str ,	__a      :    Optional[Union[str, os.PathLike]] = None ,	__a      :    bool = False ,	__a      :    bool = False ,	__a      :    Optional[Dict[str, str]] = None ,	__a      :    Optional[Union[bool, str]] = None ,	__a      :    Optional[str] = None ,	__a      :    bool = False ,	):






   """simple docstring"""

   _a				:	str			=   str(__a )

   _a				:	Optional[Any]			=   os.path.join(__a ,	__a )

   if os.path.isfile(__a ):
      _a				:	Tuple			=   module_file_or_url
      _a				:	Optional[Any]			=   'local'
   elif pretrained_model_name_or_path.count('/' ) == 0:
      _a				:	int			=   get_diffusers_versions()
      # cut ".dev0"
      _a				:	Any			=   'v' + '.'.join(__version__.split('.' )[:3] )

      # retrieve github version that matches
      if revision is None:
         _a				:	Any			=   latest_version if latest_version[1:] in available_versions else 'main'
         logger.info(f"""Defaulting to latest_version: {revision}.""" )
      elif revision in available_versions:
         _a				:	Any			=   f"""v{revision}"""
      elif revision == "main":
         _a				:	Optional[int]			=   revision
      else:
         raise ValueError(
             f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
             f""" {', '.join(available_versions + ['main'] )}.""" )

      # community pipeline on GitHub
      _a				:	Tuple			=   COMMUNITY_PIPELINES_URL.format(revision=__a ,	pipeline=__a )
      try:
         _a				:	Any			=   cached_download(
             __a ,	cache_dir=__a ,	force_download=__a ,	proxies=__a ,	resume_download=__a ,	local_files_only=__a ,	use_auth_token=__a ,	)
         _a				:	List[Any]			=   'git'
         _a				:	Any			=   pretrained_model_name_or_path + '.py'
      except EnvironmentError:
         logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
         raise
   else:
      try:
         # Load from URL or cache if already cached
         _a				:	Optional[Any]			=   hf_hub_download(
             __a ,	__a ,	cache_dir=__a ,	force_download=__a ,	proxies=__a ,	resume_download=__a ,	local_files_only=__a ,	use_auth_token=__a ,	)
         _a				:	List[Any]			=   os.path.join('local' ,	'--'.join(pretrained_model_name_or_path.split('/' ) ) )
      except EnvironmentError:
         logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
         raise

    # Check we have all the requirements in our environment
   _a				:	Optional[int]			=   check_imports(__a )

   # Now we move the module inside our cached dynamic modules.
   _a				:	Optional[Any]			=   DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
   create_dynamic_module(__a )
   _a				:	Any			=   Path(__a ) / full_submodule
   if submodule == "local" or submodule == "git":
      # We always copy local files (we could hash the file to see if there was a change, and give them the name of
      # that hash, to only copy when there is a modification but it seems overkill for now).
      # The only reason we do the copy is to avoid putting too many folders in sys.path.
      shutil.copy(__a ,	submodule_path / module_file )
      for module_needed in modules_needed:
         _a				:	Dict			=   f"""{module_needed}.py"""
         shutil.copy(os.path.join(__a ,	__a ) ,	submodule_path / module_needed )
   else:
      # Get the commit hash
      # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
      if isinstance(__a ,	__a ):
         _a				:	Optional[Any]			=   use_auth_token
      elif use_auth_token is True:
         _a				:	List[Any]			=   HfFolder.get_token()
      else:
         _a				:	Dict			=   None

      _a				:	int			=   model_info(__a ,	revision=__a ,	token=__a ).sha

      # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
      # benefit of versioning.
      _a				:	Optional[int]			=   submodule_path / commit_hash
      _a				:	str			=   full_submodule + os.path.sep + commit_hash
      create_dynamic_module(__a )

      if not (submodule_path / module_file).exists():
         shutil.copy(__a ,	submodule_path / module_file )
      # Make sure we also have every file with relative
      for module_needed in modules_needed:
         if not (submodule_path / module_needed).exists():
            get_cached_module_file(
                __a ,	f"""{module_needed}.py""" ,	cache_dir=__a ,	force_download=__a ,	resume_download=__a ,	proxies=__a ,	use_auth_token=__a ,	revision=__a ,	local_files_only=__a ,	)
   return os.path.join(__a ,	__a )





def 					UpperCAmelCase_		(__a      :    Union[str, os.PathLike] ,	__a      :    str ,	__a      :    Optional[str] = None ,	__a      :    Optional[Union[str, os.PathLike]] = None ,	__a      :    bool = False ,	__a      :    bool = False ,	__a      :    Optional[Dict[str, str]] = None ,	__a      :    Optional[Union[bool, str]] = None ,	__a      :    Optional[str] = None ,	__a      :    bool = False ,	**__a      :    str ,	):






   """simple docstring"""

   _a				:	Dict			=   get_cached_module_file(
       __a ,	__a ,	cache_dir=__a ,	force_download=__a ,	resume_download=__a ,	proxies=__a ,	use_auth_token=__a ,	revision=__a ,	local_files_only=__a ,	)
   return get_class_in_module(__a ,	final_module.replace('.py' ,	'' ) )



