
"""simple docstring"""







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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE	       =      {
    """Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
    """Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
    """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
    """Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
    """Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
    """Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
    """Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
    """Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
    """Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
    """Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
    """Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
    """Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					int     							=   'codegen'
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   {
					    'max_position_embeddings': 'n_positions',
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}





					def __init__(				self   :							List[str]							,						snake_case_   :							Tuple=50400							,						snake_case_   :							Optional[int]=2048							,						snake_case_   :							int=2048							,						snake_case_   :							Tuple=4096							,						snake_case_   :							List[str]=28							,						snake_case_   :							int=16							,						snake_case_   :							Any=64							,						snake_case_   :							str=None							,						snake_case_   :							List[Any]="gelu_new"							,						snake_case_   :							Any=0.0							,						snake_case_   :							Union[str, Any]=0.0							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Dict=1e-5							,						snake_case_   :							Any=0.02							,						snake_case_   :							str=True							,						snake_case_   :							Any=50256							,						snake_case_   :							Union[str, Any]=50256							,						snake_case_   :							Tuple=False							,						**snake_case_   :							List[Any]							,						):
												__snake_case     			=       vocab_size
												__snake_case     			=       n_ctx
												__snake_case     			=       n_positions
												__snake_case     			=       n_embd
												__snake_case     			=       n_layer
												__snake_case     			=       n_head
												__snake_case     			=       n_inner
												__snake_case     			=       rotary_dim
												__snake_case     			=       activation_function
												__snake_case     			=       resid_pdrop
												__snake_case     			=       embd_pdrop
												__snake_case     			=       attn_pdrop
												__snake_case     			=       layer_norm_epsilon
												__snake_case     			=       initializer_range
												__snake_case     			=       use_cache

												__snake_case     			=       bos_token_id
												__snake_case     			=       eos_token_id

												super().__init__(
												    bos_token_id=snake_case_							,						eos_token_id=snake_case_							,						tie_word_embeddings=snake_case_							,						**snake_case_    )







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							List[str]							,						snake_case_   :							PretrainedConfig							,						snake_case_   :							str = "default"							,						snake_case_   :							List[PatchingSpec] = None							,						snake_case_   :							bool = False							,						):
												super().__init__(snake_case_							,						task=snake_case_							,						patching_specs=snake_case_							,						use_past=snake_case_    )
												if not getattr(self._config							,						"pad_token_id"							,						snake_case_    ):
																			# TODO: how to do that better?
																			__snake_case     			=       0





					@property
					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
												if self.use_past:
																			self.fill_with_past_key_values_(snake_case_							,						direction="inputs"    )
																			__snake_case     			=       {0: "batch", 1: "past_sequence + sequence"}
												else:
																			__snake_case     			=       {0: "batch", 1: "sequence"}

												return common_inputs





					@property
					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return self._config.n_layer





					@property
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												return self._config.n_head





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							PreTrainedTokenizer							,						snake_case_   :							int = -1							,						snake_case_   :							int = -1							,						snake_case_   :							bool = False							,						snake_case_   :							Optional[TensorType] = None							,						):
												__snake_case     			=       super(snake_case_							,						self    ).generate_dummy_inputs(
												    snake_case_							,						batch_size=snake_case_							,						seq_length=snake_case_							,						is_pair=snake_case_							,						framework=snake_case_    )

												# We need to order the input in the way they appears in the forward()
												__snake_case     			=       OrderedDict({"input_ids": common_inputs["input_ids"]}    )

												# Need to add the past_keys
												if self.use_past:
																			if not is_torch_available():
																										raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
																			else:
																										import torch

																										__snake_case			,   __snake_case     			=       common_inputs["input_ids"].shape
																										# Not using the same length for past_key_values
																										__snake_case     			=       seqlen + 2
																										__snake_case     			=       (
																										    batch,
																										    self.num_attention_heads,
																										    past_key_values_length,
																										    self._config.hidden_size // self.num_attention_heads,
																										)
																										__snake_case     			=       [
																										    (torch.zeros(snake_case_    ), torch.zeros(snake_case_    )) for _ in range(self.num_layers    )
																										]

												__snake_case     			=       common_inputs["attention_mask"]
												if self.use_past:
																			__snake_case     			=       ordered_inputs["attention_mask"].dtype
																			__snake_case     			=       torch.cat(
																			    [ordered_inputs["attention_mask"], torch.ones(snake_case_							,						snake_case_							,						dtype=snake_case_    )]							,						dim=1    )

												return ordered_inputs





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												return 13


"""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    )    )
