






from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Dict    ,   UpperCamelCase_					:				List[str]=1e-1_2   ) ->   Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						jnp.divide(emb_a.T    ,   jnp.clip(jnp.linalg.norm(UpperCamelCase_    ,   axis=1   )    ,   a_min=UpperCamelCase_   )   ).T
						lowerCAmelCase__       =						jnp.divide(emb_a.T    ,   jnp.clip(jnp.linalg.norm(UpperCamelCase_    ,   axis=1   )    ,   a_min=UpperCamelCase_   )   ).T
						return jnp.matmul(UpperCamelCase_    ,   norm_emb_a.T   )


class      lowercase__     (			nn.Module	):
	a_         =42
	a_         =jnp.floataa

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						FlaxCLIPVisionModule(self.config.vision_config       )
							lowerCAmelCase__       =						nn.Dense(self.config.projection_dim		,    use_bias=__UpperCAmelCase		,    dtype=self.dtype       )

							lowerCAmelCase__       =						self.param("concept_embeds"		,    jax.nn.initializers.ones		,    (17, self.config.projection_dim)       )
							lowerCAmelCase__       =						self.param(
							    "special_care_embeds"		,    jax.nn.initializers.ones		,    (3, self.config.projection_dim)       )

							lowerCAmelCase__       =						self.param("concept_embeds_weights"		,    jax.nn.initializers.ones		,    (17,)       )
							lowerCAmelCase__       =						self.param("special_care_embeds_weights"		,    jax.nn.initializers.ones		,    (3,)       )


	def __call__(							self		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.vision_model(__UpperCAmelCase       )[1]
							lowerCAmelCase__       =						self.visual_projection(__UpperCAmelCase       )

							lowerCAmelCase__       =						jax_cosine_distance(__UpperCAmelCase		,    self.special_care_embeds       )
							lowerCAmelCase__       =						jax_cosine_distance(__UpperCAmelCase		,    self.concept_embeds       )

							# increase this value to create a stronger `nfsw` filter
							# at the cost of increasing the possibility of filtering benign image inputs
							lowerCAmelCase__       =						0.0

							lowerCAmelCase__       =						special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
							lowerCAmelCase__       =						jnp.round(__UpperCAmelCase		,    3       )
							lowerCAmelCase__       =						jnp.any(special_scores > 0		,    axis=1		,    keepdims=__UpperCAmelCase       )
							# Use a lower threshold if an image has any special care concept
							lowerCAmelCase__       =						is_special_care * 0.01

							lowerCAmelCase__       =						cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
							lowerCAmelCase__       =						jnp.round(__UpperCAmelCase		,    3       )
							lowerCAmelCase__       =						jnp.any(concept_scores > 0		,    axis=1       )

							return has_nsfw_concepts





class      lowercase__     (			_UpperCAmelCase	):
	a_         =CLIPConfig
	a_         ="""clip_input"""
	a_         =FlaxStableDiffusionSafetyCheckerModule

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 0		,    __UpperCAmelCase = jnp.floataa		,    __UpperCAmelCase = True		,    **__UpperCAmelCase		,    )->					Union[str, Any]:







							'''simple docstring'''





							if input_shape is None:
													lowerCAmelCase__       =						(1, 224, 224, 3)
							lowerCAmelCase__       =						self.module_class(config=__UpperCAmelCase		,    dtype=__UpperCAmelCase		,    **__UpperCAmelCase       )
							super().__init__(__UpperCAmelCase		,    __UpperCAmelCase		,    input_shape=__UpperCAmelCase		,    seed=__UpperCAmelCase		,    dtype=__UpperCAmelCase		,    _do_init=_do_init       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					FrozenDict:







							'''simple docstring'''





							lowerCAmelCase__       =						jax.random.normal(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						jax.random.split(__UpperCAmelCase       )
							lowerCAmelCase__       =						{"params": params_rng, "dropout": dropout_rng}

							lowerCAmelCase__       =						self.module.init(__UpperCAmelCase		,    __UpperCAmelCase       )["params"]

							return random_params


	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						jnp.transpose(__UpperCAmelCase		,    (0, 2, 3, 1)       )

							return self.module.apply(
							    {"params": params or self.params}		,    jnp.array(__UpperCAmelCase		,    dtype=jnp.floataa       )		,    rngs={}		,    )














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


a_							=    logging.get_logger(__name__)


a_							=    {
    '''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      lowercase__     (			_UpperCAmelCase	):
	a_         ="""codegen"""
	a_         ={
	    """max_position_embeddings""": """n_positions""",
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}

	def __init__(							self		,    __UpperCAmelCase=50400		,    __UpperCAmelCase=2048		,    __UpperCAmelCase=2048		,    __UpperCAmelCase=4096		,    __UpperCAmelCase=28		,    __UpperCAmelCase=16		,    __UpperCAmelCase=64		,    __UpperCAmelCase=None		,    __UpperCAmelCase="gelu_new"		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=0.0		,    __UpperCAmelCase=1E-5		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=True		,    __UpperCAmelCase=50256		,    __UpperCAmelCase=50256		,    __UpperCAmelCase=False		,    **__UpperCAmelCase		,    )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						vocab_size
							lowerCAmelCase__       =						n_ctx
							lowerCAmelCase__       =						n_positions
							lowerCAmelCase__       =						n_embd
							lowerCAmelCase__       =						n_layer
							lowerCAmelCase__       =						n_head
							lowerCAmelCase__       =						n_inner
							lowerCAmelCase__       =						rotary_dim
							lowerCAmelCase__       =						activation_function
							lowerCAmelCase__       =						resid_pdrop
							lowerCAmelCase__       =						embd_pdrop
							lowerCAmelCase__       =						attn_pdrop
							lowerCAmelCase__       =						layer_norm_epsilon
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						use_cache

							lowerCAmelCase__       =						bos_token_id
							lowerCAmelCase__       =						eos_token_id

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





class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = "default"		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False		,    )->					Any:







							'''simple docstring'''





							super().__init__(__UpperCAmelCase		,    task=__UpperCAmelCase		,    patching_specs=__UpperCAmelCase		,    use_past=__UpperCAmelCase       )
							if not getattr(self._config		,    "pad_token_id"		,    __UpperCAmelCase       ):
													# TODO: how to do that better?
													lowerCAmelCase__       =						0

	@property
	def 		UpperCAmelCase  (							self       )->					Mapping[str, Mapping[int, str]]:







							'''simple docstring'''





							lowerCAmelCase__       =						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
							if self.use_past:
													self.fill_with_past_key_values_(__UpperCAmelCase		,    direction="inputs"       )
													lowerCAmelCase__       =						{0: "batch", 1: "past_sequence + sequence"}
							else:
													lowerCAmelCase__       =						{0: "batch", 1: "sequence"}

							return common_inputs

	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return self._config.n_layer

	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return self._config.n_head

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = -1		,    __UpperCAmelCase = -1		,    __UpperCAmelCase = False		,    __UpperCAmelCase = None		,    )->					Mapping[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						super(__UpperCAmelCase		,    self       ).generate_dummy_inputs(
							    __UpperCAmelCase		,    batch_size=__UpperCAmelCase		,    seq_length=__UpperCAmelCase		,    is_pair=__UpperCAmelCase		,    framework=__UpperCAmelCase       )

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

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

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

							return ordered_inputs


	@property
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return 13






