





import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def 							_lowerCAmelCase (  _a   :					Optional[Any]			)	->			Optional[int]:
     assert "mock" in _fsspec_registry
     assert "bz2" in _fsspec_registry






def 							_lowerCAmelCase (  )	->			Union[str, Any]:
     assert "mock" not in _fsspec_registry
     assert "bz2" in _fsspec_registry






def 							_lowerCAmelCase (  )	->			Optional[Any]:
     lowerCAmelCase_					:       Optional[Any]    =		"""mock-s3-bucket"""
     lowerCAmelCase_					:       str    =		F's3://{mock_bucket}'
     lowerCAmelCase_					:       Optional[Any]    =		extract_path_from_uri(_a			)
     assert dataset_path.startswith("""s3://"""			) is False

     lowerCAmelCase_					:       List[str]    =		"""./local/path"""
     lowerCAmelCase_					:       Optional[Any]    =		extract_path_from_uri(_a			)
     assert dataset_path == new_dataset_path






def 							_lowerCAmelCase (  _a   :					Tuple			)	->			int:
     lowerCAmelCase_					:       int    =		is_remote_filesystem(_a			)
     assert is_remote is True

     lowerCAmelCase_					:       str    =		fsspec.filesystem("""file"""			)

     lowerCAmelCase_					:       Optional[Any]    =		is_remote_filesystem(_a			)
     assert is_remote is False






@pytest.mark.parametrize("""compression_fs_class"""					,   _a			)
def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					List[Any]					,   _a   :					Tuple					,   _a   :					List[Any]					,   _a   :					int					,   _a   :					Dict					,   _a   :					Optional[Any]			)	->			List[Any]:
     lowerCAmelCase_					:       Optional[int]    =		{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_file, """bz2""": bza_file, """lz4""": lza_file}
     lowerCAmelCase_					:       Union[str, Any]    =		input_paths[compression_fs_class.protocol]
     if input_path is None:
          lowerCAmelCase_					:       Any    =		F'for \'{compression_fs_class.protocol}\' compression protocol, '
          if compression_fs_class.protocol == "lz4":
               reason += require_lza.kwargs["reason"]
          elif compression_fs_class.protocol == "zstd":
               reason += require_zstandard.kwargs["reason"]
          pytest.skip(_a			)
     lowerCAmelCase_					:       Any    =		fsspec.filesystem(compression_fs_class.protocol					,   fo=_a			)
     assert isinstance(_a					,   _a			)
     lowerCAmelCase_					:       int    =		os.path.basename(_a			)
     lowerCAmelCase_					:       List[str]    =		expected_filename[: expected_filename.rindex("""."""			)]
     assert fs.glob("""*"""			) == [expected_filename]
     with fs.open(_a					,   """r"""					,   encoding="""utf-8"""			) as f, open(_a					,   encoding="""utf-8"""			) as expected_file:
          assert f.read() == expected_file.read()






@pytest.mark.parametrize("""protocol"""					,   ["""zip""", """gzip"""]			)
def 							_lowerCAmelCase (  _a   :					Optional[Any]					,   _a   :					Optional[int]					,   _a   :					Optional[Any]			)	->			str:
     lowerCAmelCase_					:       Optional[int]    =		{"""zip""": zip_jsonl_path, """gzip""": jsonl_gz_path}
     lowerCAmelCase_					:       Optional[Any]    =		compressed_file_paths[protocol]
     lowerCAmelCase_					:       Union[str, Any]    =		"""dataset.jsonl"""
     lowerCAmelCase_					:       Optional[int]    =		F'{protocol}://{member_file_path}::{compressed_file_path}'
     lowerCAmelCase_   ,  *lowerCAmelCase_					:       Optional[int]    =		fsspec.get_fs_token_paths(_a			)
     assert fs.isfile(_a			)
     assert not fs.isfile("""non_existing_""" + member_file_path			)






@pytest.mark.integration
def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					int					,   _a   :					str					,   _a   :					Optional[Any]			)	->			str:
     lowerCAmelCase_					:       int    =		hf_api.dataset_info(_a					,   token=_a			)
     lowerCAmelCase_					:       str    =		HfFileSystem(repo_info=_a					,   token=_a			)
     assert sorted(hffs.glob("""*"""			)			) == [".gitattributes", "data"]
     assert hffs.isdir("""data"""			)
     assert hffs.isfile(""".gitattributes"""			) and hffs.isfile("""data/text_data.txt"""			)
     with open(_a			) as f:
          assert hffs.open("""data/text_data.txt"""					,   """r"""			).read() == f.read()







def 							_lowerCAmelCase (  )	->			List[str]:
     lowerCAmelCase_					:       Tuple    =		"""bz2"""

     # Import module
     import datasets.filesystems

     # Overwrite protocol and reload
     register_implementation(_a					,   _a					,   clobber=_a			)
     with pytest.warns(_a			) as warning_info:
          importlib.reload(datasets.filesystems			)

     assert len(_a			) == 1
     assert (
         str(warning_info[0].message			)
         == F'A filesystem protocol was already set for {protocol} and will be overwritten.'
     )







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


UpperCAmelCase_					:   Optional[Any] 							=						logging.get_logger(__name__)

UpperCAmelCase_					:   Any 							=						{
    """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      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """perceiver"""







  def __init__(	self    ,						_lowercase=256    ,						_lowercase=1_280    ,						_lowercase=768    ,						_lowercase=1    ,						_lowercase=26    ,						_lowercase=8    ,						_lowercase=8    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase="kv"    ,						_lowercase=1    ,						_lowercase=1    ,						_lowercase="gelu"    ,						_lowercase=0.1    ,						_lowercase=0.02    ,						_lowercase=1e-12    ,						_lowercase=True    ,						_lowercase=262    ,						_lowercase=2_048    ,						_lowercase=56    ,						_lowercase=[368, 496]    ,						_lowercase=16    ,						_lowercase=1_920    ,						_lowercase=16    ,						_lowercase=[1, 16, 224, 224]    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

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





class      lowercase__    (							__A  ):







  @property
  def 	UpperCAmelCase__		(	self						):
       if self.task == "multiple-choice":
            lowerCAmelCase_					:       Optional[Any]    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            lowerCAmelCase_					:       Optional[int]    =		{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""inputs""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return 1e-4







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = -1    ,						_lowercase = False    ,						_lowercase = None    ,						_lowercase = 3    ,						_lowercase = 40    ,						_lowercase = 40    ,						):
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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