
"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """microsoft/trocr-base-handwritten""": (
        """https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							__magic_name__     (		a__   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   'trocr'
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   ['past_key_values']
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   {
					    'num_attention_heads': 'decoder_attention_heads',
					    'hidden_size': 'd_model',
					    'num_hidden_layers': 'decoder_layers',
					}





					def __init__(				self   :							str							,						snake_case_   :							Union[str, Any]=50265							,						snake_case_   :							Any=1024							,						snake_case_   :							List[Any]=12							,						snake_case_   :							Dict=16							,						snake_case_   :							int=4096							,						snake_case_   :							List[Any]="gelu"							,						snake_case_   :							List[str]=512							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Union[str, Any]=0.0							,						snake_case_   :							Dict=2							,						snake_case_   :							List[str]=0.02							,						snake_case_   :							Optional[Any]=0.0							,						snake_case_   :							List[str]=True							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Union[str, Any]=True							,						snake_case_   :							Tuple=True							,						snake_case_   :							List[Any]=1							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							List[str]=2							,						**snake_case_   :							List[Any]							,						):
												__snake_case     			=       vocab_size
												__snake_case     			=       d_model
												__snake_case     			=       decoder_layers
												__snake_case     			=       decoder_attention_heads
												__snake_case     			=       decoder_ffn_dim
												__snake_case     			=       activation_function
												__snake_case     			=       max_position_embeddings
												__snake_case     			=       dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       init_std
												__snake_case     			=       decoder_layerdrop
												__snake_case     			=       use_cache
												__snake_case     			=       scale_embedding
												__snake_case     			=       use_learned_position_embeddings
												__snake_case     			=       layernorm_embedding

												super().__init__(
												    pad_token_id=_A							,						bos_token_id=_A							,						eos_token_id=_A							,						decoder_start_token_id=_A							,						**_A							,						)


"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      ["""text""", """image""", """audio"""]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__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((5_12, 5_12) ) )
														elif input_type == "audio":
																					inputs.append(torch.ones(30_00 ) )
														elif isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					inputs.append(create_inputs(SCREAMING_SNAKE_CASE ) )
														else:
																					raise ValueError(F'''Invalid type requested: {input_type}''' )

							return inputs
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

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

							return output_types







@is_tool_test
class 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												self.assertTrue(hasattr(self.tool							,						"inputs"    )    )
												self.assertTrue(hasattr(self.tool							,						"outputs"    )    )

												__snake_case     			=       self.tool.inputs
												for _input in inputs:
																			if isinstance(_input							,						snake_case_    ):
																										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 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												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 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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

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





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )

												__snake_case     			=       []

												for _input, input_type in zip(snake_case_							,						self.tool.inputs    ):
																			if isinstance(snake_case_							,						snake_case_    ):
																										_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(*snake_case_    )

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

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