





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


snake_case_           =      ['text', 'image', 'audio']






def lowerCamelCase__   (							snake_case_     :  List[str]	)				->    Dict:
   __snake_case								=       []

   for input_type in input_types:
      if input_type == "text":
         inputs.append('''Text input'''	)
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
      elif input_type == "audio":
         inputs.append(torch.ones(3000	)	)
      elif isinstance(snake_case_	,				snake_case_	):
         inputs.append(create_inputs(snake_case_	)	)
      else:
         raise ValueError(f"""Invalid type requested: {input_type}"""	)

   return inputs






def lowerCamelCase__   (							snake_case_     :  List	)				->    Any:
   __snake_case								=       []

   for output in outputs:
      if isinstance(snake_case_	,				(str, AgentText)	):
         output_types.append('''text'''	)
      elif isinstance(snake_case_	,				(Image.Image, AgentImage)	):
         output_types.append('''image'''	)
      elif isinstance(snake_case_	,				(torch.Tensor, AgentAudio)	):
         output_types.append('''audio'''	)
      else:
         raise ValueError(f"""Invalid output: {output}"""	)

   return output_types







@is_tool_test
class 		SCREAMING_SNAKE_CASE__    :


      def        a			(self	:				Dict				):





         """simple docstring"""



         self.assertTrue(hasattr(self.tool    ,      '''inputs'''				)				)
         self.assertTrue(hasattr(self.tool    ,      '''outputs'''				)				)

         __snake_case								=       self.tool.inputs
         for _input in inputs:
            if isinstance(_input    ,      a__				):
               for __input in _input:
                  self.assertTrue(__input in authorized_types				)
            else:
               self.assertTrue(_input in authorized_types				)

         __snake_case								=       self.tool.outputs
         for _output in outputs:
            self.assertTrue(_output in authorized_types				)


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)
         __snake_case								=       self.tool(*a__				)

         # There is a single output
         if len(self.tool.outputs				) == 1:
            __snake_case								=       [outputs]

         self.assertListEqual(output_types(a__				)    ,      self.tool.outputs				)


      def        a			(self	:				str				):





         """simple docstring"""



         self.assertTrue(hasattr(self.tool    ,      '''description'''				)				)
         self.assertTrue(hasattr(self.tool    ,      '''default_checkpoint'''				)				)
         self.assertTrue(self.tool.description.startswith('''This is a tool that'''				)				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)
         __snake_case								=       self.tool(*a__				)

         if not isinstance(a__    ,      a__				):
            __snake_case								=       [outputs]

         self.assertEqual(len(a__				)    ,      len(self.tool.outputs				)				)

         for output, output_type in zip(a__    ,      self.tool.outputs				):
            __snake_case								=       AGENT_TYPE_MAPPING[output_type]
            self.assertTrue(isinstance(a__    ,      a__				)				)


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)

         __snake_case								=       []

         for _input, input_type in zip(a__    ,      self.tool.inputs				):
            if isinstance(a__    ,      a__				):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
            else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
         __snake_case								=       self.tool(*a__				)

         if not isinstance(a__    ,      a__				):
            __snake_case								=       [outputs]

         self.assertEqual(len(a__				)    ,      len(self.tool.outputs				)				)








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   'sew-d'


      def __init__(self	:				str    ,      a__	:				List[Any]=32    ,      a__	:				Optional[int]=768    ,      a__	:				Union[str, Any]=12    ,      a__	:				Tuple=12    ,      a__	:				int=3072    ,      a__	:				List[Any]=2    ,      a__	:				Dict=512    ,      a__	:				Dict=256    ,      a__	:				List[str]=True    ,      a__	:				List[str]=True    ,      a__	:				str=("p2c", "c2p")    ,      a__	:				str="layer_norm"    ,      a__	:				str="gelu_python"    ,      a__	:				List[Any]=0.1    ,      a__	:				Dict=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				Dict=0.1    ,      a__	:				int=0.0_2    ,      a__	:				str=1E-7    ,      a__	:				Union[str, Any]=1E-5    ,      a__	:				Union[str, Any]="group"    ,      a__	:				Optional[Any]="gelu"    ,      a__	:				List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,      a__	:				Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,      a__	:				Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=128    ,      a__	:				Union[str, Any]=16    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=0.0_5    ,      a__	:				Tuple=10    ,      a__	:				str=2    ,      a__	:				int=0.0    ,      a__	:				Optional[Any]=10    ,      a__	:				List[Any]=0    ,      a__	:				Optional[int]="mean"    ,      a__	:				List[str]=False    ,      a__	:				Tuple=False    ,      a__	:				Optional[int]=256    ,      a__	:				str=0    ,      a__	:				List[Any]=1    ,      a__	:				int=2    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__    ,      pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__				)
         __snake_case								=       hidden_size
         __snake_case								=       feat_extract_norm
         __snake_case								=       feat_extract_activation
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       conv_bias
         __snake_case								=       num_conv_pos_embeddings
         __snake_case								=       num_conv_pos_embedding_groups
         __snake_case								=       len(self.conv_dim				)
         __snake_case								=       num_hidden_layers
         __snake_case								=       intermediate_size
         __snake_case								=       squeeze_factor
         __snake_case								=       max_position_embeddings
         __snake_case								=       position_buckets
         __snake_case								=       share_att_key
         __snake_case								=       relative_attention
         __snake_case								=       norm_rel_ebd
         __snake_case								=       list(a__				)
         __snake_case								=       hidden_act
         __snake_case								=       num_attention_heads
         __snake_case								=       hidden_dropout
         __snake_case								=       attention_dropout
         __snake_case								=       activation_dropout
         __snake_case								=       feat_proj_dropout
         __snake_case								=       final_dropout
         __snake_case								=       layer_norm_eps
         __snake_case								=       feature_layer_norm_eps
         __snake_case								=       initializer_range
         __snake_case								=       vocab_size

         if (
             (len(self.conv_stride				) != self.num_feat_extract_layers)
             or (len(self.conv_kernel				) != self.num_feat_extract_layers)
             or (len(self.conv_dim				) != self.num_feat_extract_layers)
         ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect.'''
                '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                f"""but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"""
                f"""= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case								=       apply_spec_augment
         __snake_case								=       mask_time_prob
         __snake_case								=       mask_time_length
         __snake_case								=       mask_time_min_masks
         __snake_case								=       mask_feature_prob
         __snake_case								=       mask_feature_length
         __snake_case								=       mask_feature_min_masks

         # ctc loss
         __snake_case								=       ctc_loss_reduction
         __snake_case								=       ctc_zero_infinity

         # sequence classification
         __snake_case								=       use_weighted_layer_sum
         __snake_case								=       classifier_proj_size


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





         """simple docstring"""



         return functools.reduce(operator.mul    ,      self.conv_stride    ,      1				)

