
"""simple docstring"""






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowercase							=				logging.get_logger(__name__)


if is_vision_available():
					import PIL

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			Optional[Any]          =   ["""pixel_values"""]
							def __init__(     self  ,    _lowerCamelCase = True  ,    _lowerCamelCase = None  ,    _lowerCamelCase = PILImageResampling.BICUBIC  ,    _lowerCamelCase = True  ,    _lowerCamelCase = None  ,    _lowerCamelCase = True  ,    _lowerCamelCase = 1 / 255  ,    _lowerCamelCase = True  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = True  ,    **_lowerCamelCase  ,    )			->							None:




										'''simple docstring'''
										super().__init__(**_lowerCamelCase       )
										__lowercase       =       size if size is not None else {"shortest_edge": 224}
										__lowercase       =       get_size_dict(_lowerCamelCase  ,    default_to_square=_lowerCamelCase       )
										__lowercase       =       crop_size if crop_size is not None else {"height": 224, "width": 224}
										__lowercase       =       get_size_dict(_lowerCamelCase  ,    default_to_square=_lowerCamelCase  ,    param_name="crop_size"       )

										__lowercase       =       do_resize
										__lowercase       =       size
										__lowercase       =       resample
										__lowercase       =       do_center_crop
										__lowercase       =       crop_size
										__lowercase       =       do_rescale
										__lowercase       =       rescale_factor
										__lowercase       =       do_normalize
										__lowercase       =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										__lowercase       =       image_std if image_std is not None else OPENAI_CLIP_STD
										__lowercase       =       do_convert_rgb
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = PILImageResampling.BICUBIC  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							np.ndarray:




										'''simple docstring'''
										__lowercase       =       get_size_dict(_lowerCamelCase  ,    default_to_square=_lowerCamelCase       )
										if "shortest_edge" not in size:
													raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
										__lowercase       =       get_resize_output_image_size(_lowerCamelCase  ,    size=size["shortest_edge"]  ,    default_to_square=_lowerCamelCase       )
										return resize(_lowerCamelCase  ,    size=_lowerCamelCase  ,    resample=_lowerCamelCase  ,    data_format=_lowerCamelCase  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							np.ndarray:




										'''simple docstring'''
										__lowercase       =       get_size_dict(_lowerCamelCase       )
										if "height" not in size or "width" not in size:
													raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
										return center_crop(_lowerCamelCase  ,    size=(size["height"], size["width"])  ,    data_format=_lowerCamelCase  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							List[str]:




										'''simple docstring'''
										return rescale(_lowerCamelCase  ,    scale=_lowerCamelCase  ,    data_format=_lowerCamelCase  ,    **_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    **_lowerCamelCase  ,    )			->							np.ndarray:




										'''simple docstring'''
										return normalize(_lowerCamelCase  ,    mean=_lowerCamelCase  ,    std=_lowerCamelCase  ,    data_format=_lowerCamelCase  ,    **_lowerCamelCase       )





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = None  ,    _lowerCamelCase = ChannelDimension.FIRST  ,    **_lowerCamelCase  ,    )			->							PIL.Image.Image:




										'''simple docstring'''
										__lowercase       =       do_resize if do_resize is not None else self.do_resize
										__lowercase       =       size if size is not None else self.size
										__lowercase       =       get_size_dict(_lowerCamelCase  ,    param_name="size"  ,    default_to_square=_lowerCamelCase       )
										__lowercase       =       resample if resample is not None else self.resample
										__lowercase       =       do_center_crop if do_center_crop is not None else self.do_center_crop
										__lowercase       =       crop_size if crop_size is not None else self.crop_size
										__lowercase       =       get_size_dict(_lowerCamelCase  ,    param_name="crop_size"  ,    default_to_square=_lowerCamelCase       )
										__lowercase       =       do_rescale if do_rescale is not None else self.do_rescale
										__lowercase       =       rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowercase       =       do_normalize if do_normalize is not None else self.do_normalize
										__lowercase       =       image_mean if image_mean is not None else self.image_mean
										__lowercase       =       image_std if image_std is not None else self.image_std
										__lowercase       =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										__lowercase       =       make_list_of_images(_lowerCamelCase       )

										if not valid_images(_lowerCamelCase       ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."       )

										if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."       )

										if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."       )

										if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."       )

										if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."       )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
													__lowercase       =       [convert_to_rgb(_lowerCamelCase       ) for image in images]

										# All transformations expect numpy arrays.
										__lowercase       =       [to_numpy_array(_lowerCamelCase       ) for image in images]

										if do_resize:
													__lowercase       =       [self.resize(image=_lowerCamelCase  ,    size=_lowerCamelCase  ,    resample=_lowerCamelCase       ) for image in images]

										if do_center_crop:
													__lowercase       =       [self.center_crop(image=_lowerCamelCase  ,    size=_lowerCamelCase       ) for image in images]

										if do_rescale:
													__lowercase       =       [self.rescale(image=_lowerCamelCase  ,    scale=_lowerCamelCase       ) for image in images]

										if do_normalize:
													__lowercase       =       [self.normalize(image=_lowerCamelCase  ,    mean=_lowerCamelCase  ,    std=_lowerCamelCase       ) for image in images]

										__lowercase       =       [to_channel_dimension_format(_lowerCamelCase  ,    _lowerCamelCase       ) for image in images]

										__lowercase       =       {"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase  ,    tensor_type=_lowerCamelCase       )







"""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


_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''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 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   """codegen"""
							_lowerCamelCase					:			List[str]          =   {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}
							def __init__(     self  ,    _lowerCamelCase=50_400  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=4_096  ,    _lowerCamelCase=28  ,    _lowerCamelCase=16  ,    _lowerCamelCase=64  ,    _lowerCamelCase=None  ,    _lowerCamelCase="gelu_new"  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=1e-5  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=True  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       vocab_size
										__lowercase       =       n_ctx
										__lowercase       =       n_positions
										__lowercase       =       n_embd
										__lowercase       =       n_layer
										__lowercase       =       n_head
										__lowercase       =       n_inner
										__lowercase       =       rotary_dim
										__lowercase       =       activation_function
										__lowercase       =       resid_pdrop
										__lowercase       =       embd_pdrop
										__lowercase       =       attn_pdrop
										__lowercase       =       layer_norm_epsilon
										__lowercase       =       initializer_range
										__lowercase       =       use_cache

										__lowercase       =       bos_token_id
										__lowercase       =       eos_token_id

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





class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = "default"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False  ,    )			->							str:




										'''simple docstring'''
										super().__init__(_lowerCamelCase  ,    task=_lowerCamelCase  ,    patching_specs=_lowerCamelCase  ,    use_past=_lowerCamelCase       )
										if not getattr(self._config  ,    "pad_token_id"  ,    _lowerCamelCase       ):
													# TODO: how to do that better?
													__lowercase       =       0
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										__lowercase       =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
										if self.use_past:
													self.fill_with_past_key_values_(_lowerCamelCase  ,    direction="inputs"       )
													__lowercase       =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													__lowercase       =       {0: "batch", 1: "sequence"}

										return common_inputs
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_layer
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_head
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    )			->							Mapping[str, Any]:




										'''simple docstring'''
										__lowercase       =       super(_lowerCamelCase  ,    self       ).generate_dummy_inputs(
										    _lowerCamelCase  ,    batch_size=_lowerCamelCase  ,    seq_length=_lowerCamelCase  ,    is_pair=_lowerCamelCase  ,    framework=_lowerCamelCase       )

										# We need to order the input in the way they appears in the forward()
										__lowercase       =       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

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

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

										return ordered_inputs





							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return 13





