
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			_lowerCAmelCase      (			__a      ,   __a ):




    @register_to_config
    def __init__(      self       ,       _UpperCamelCase = 128       ,       _UpperCamelCase = 256       ,       _UpperCamelCase = 2000.0       ,       _UpperCamelCase = 768       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 64       ,       _UpperCamelCase = 2_048       ,       _UpperCamelCase = 0.1       ,       )       ->   str:
       super().__init__()

       lowerCAmelCase_      			=			nn.Sequential(
           nn.Linear(_UpperCamelCase       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       nn.Linear(d_model * 4       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       )

       lowerCAmelCase_      			=			nn.Embedding(_UpperCamelCase       ,       _UpperCamelCase  )
       lowerCAmelCase_      			=			False

       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )

       lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )

       lowerCAmelCase_      			=			nn.ModuleList()
       for lyr_num in range(_UpperCamelCase  ):
          # FiLM conditional T5 decoder
          lowerCAmelCase_      			=			DecoderLayer(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
          self.decoders.append(_UpperCamelCase  )

       lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )

       lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			torch.mul(query_input.unsqueeze(-1  )       ,       key_input.unsqueeze(-2  )  )
       return mask.unsqueeze(-3  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Any:
       lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			decoder_input_tokens.shape
       assert decoder_noise_time.shape == (batch,)

       # decoder_noise_time is in [0, 1), so rescale to expected timing range.
       lowerCAmelCase_      			=			get_timestep_embedding(
           decoder_noise_time * self.config.max_decoder_noise_time       ,       embedding_dim=self.config.d_model       ,       max_period=self.config.max_decoder_noise_time       ,       ).to(dtype=self.dtype  )

       lowerCAmelCase_      			=			self.conditioning_emb(_UpperCamelCase  ).unsqueeze(1  )

       assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

       lowerCAmelCase_      			=			decoder_input_tokens.shape[1]

       # If we want to use relative positions for audio context, we can just offset
       # this sequence by the length of encodings_and_masks.
       lowerCAmelCase_      			=			torch.broadcast_to(
           torch.arange(_UpperCamelCase       ,       device=decoder_input_tokens.device  )       ,       (batch, seq_length)       ,       )

       lowerCAmelCase_      			=			self.position_encoding(_UpperCamelCase  )

       lowerCAmelCase_      			=			self.continuous_inputs_projection(_UpperCamelCase  )
       inputs += position_encodings
       lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

       # decoder: No padding present.
       lowerCAmelCase_      			=			torch.ones(
           decoder_input_tokens.shape[:2]       ,       device=decoder_input_tokens.device       ,       dtype=inputs.dtype  )

       # Translate encoding masks to encoder-decoder masks.
       lowerCAmelCase_      			=			[(x, self.encoder_decoder_mask(_UpperCamelCase       ,       _UpperCamelCase  )) for x, y in encodings_and_masks]

       # cross attend style: concat encodings
       lowerCAmelCase_      			=			torch.cat([x[0] for x in encodings_and_encdec_masks]       ,       dim=1  )
       lowerCAmelCase_      			=			torch.cat([x[1] for x in encodings_and_encdec_masks]       ,       dim=-1  )

       for lyr in self.decoders:
          lowerCAmelCase_      			=			lyr(
              _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       encoder_attention_mask=_UpperCamelCase       ,       )[0]

       lowerCAmelCase_      			=			self.decoder_norm(_UpperCamelCase  )
       lowerCAmelCase_      			=			self.post_dropout(_UpperCamelCase  )

       lowerCAmelCase_      			=			self.spec_out(_UpperCamelCase  )
       return spec_out





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   Dict:
       super().__init__()
       lowerCAmelCase_      			=			nn.ModuleList()

       # cond self attention: layer 0
       self.layer.append(
           TaLayerSelfAttentionCond(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )  )

       # cross attention: layer 1
       self.layer.append(
           TaLayerCrossAttention(
               d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase       ,       )  )

       # Film Cond MLP + dropout: last layer
       self.layer.append(
           TaLayerFFCond(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase  )  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Any:
       lowerCAmelCase_      			=			self.layer[0](
           _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

       if encoder_hidden_states is not None:
          lowerCAmelCase_      			=			torch.where(encoder_attention_mask > 0       ,       0       ,       -1e1_0  ).to(
              encoder_hidden_states.dtype  )

          lowerCAmelCase_      			=			self.layer[1](
              _UpperCamelCase       ,       key_value_states=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

       # Apply Film Conditional Feed Forward layer
       lowerCAmelCase_      			=			self.layer[-1](_UpperCamelCase       ,       _UpperCamelCase  )

       return (hidden_states,)





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
       super().__init__()
       lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )
       lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
       lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Dict:
       # pre_self_attention_layer_norm
       lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )

       if conditioning_emb is not None:
          lowerCAmelCase_      			=			self.FiLMLayer(_UpperCamelCase       ,       _UpperCamelCase  )

       # Self-attention block
       lowerCAmelCase_      			=			self.attention(_UpperCamelCase  )

       lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )

       return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
       super().__init__()
       lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
       lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   int:
       lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
       lowerCAmelCase_      			=			self.attention(
           _UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       attention_mask=attention_mask.squeeze(1  )       ,       )
       lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
       return layer_output





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
       super().__init__()
       lowerCAmelCase_      			=			TaDenseGatedActDense(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
       lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
       lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Tuple:
       lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
       if conditioning_emb is not None:
          lowerCAmelCase_      			=			self.film(_UpperCamelCase       ,       _UpperCamelCase  )

       lowerCAmelCase_      			=			self.DenseReluDense(_UpperCamelCase  )
       lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
       return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, Any]:
       super().__init__()
       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
       lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )
       lowerCAmelCase_      			=			NewGELUActivation()




    def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			self.act(self.wi_a(_UpperCamelCase  )  )
       lowerCAmelCase_      			=			self.wi_a(_UpperCamelCase  )
       lowerCAmelCase_      			=			hidden_gelu * hidden_linear
       lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

       lowerCAmelCase_      			=			self.wo(_UpperCamelCase  )
       return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   int:
       super().__init__()
       lowerCAmelCase_      			=			nn.Parameter(torch.ones(_UpperCamelCase  )  )
       lowerCAmelCase_      			=			eps




    def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
       # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
       # Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
       # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
       # half-precision inputs is done in fp32

       lowerCAmelCase_      			=			hidden_states.to(torch.floataa  ).pow(2  ).mean(-1       ,       keepdim=_UpperCamelCase  )
       lowerCAmelCase_      			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

       # convert into half-precision if necessary
       if self.weight.dtype in [torch.floataa, torch.bfloataa]:
          lowerCAmelCase_      			=			hidden_states.to(self.weight.dtype  )

       return self.weight * hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




    def   __a		(      self       ,       _UpperCamelCase  )       ->   torch.Tensor:
       return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044715 * torch.pow(_UpperCamelCase       ,       3.0  ))  ))





class 			_lowerCAmelCase      (			nn.Module ):




    def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
       super().__init__()
       lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       out_features * 2       ,       bias=_UpperCamelCase  )




    def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
       lowerCAmelCase_      			=			self.scale_bias(_UpperCamelCase  )
       lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.chunk(_UpperCamelCase       ,       2       ,       -1  )
       lowerCAmelCase_      			=			x * (1 + scale) + shift
       return x





import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


_A     =  re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
_A     =  10
_A     =  2_56






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			if len(__lowerCAmelCase       ) < MIN_NUM_TOKENS:
						return None
			lowerCAmelCase_      			=			MinHash(num_perm=__lowerCAmelCase       )
			for token in set(__lowerCAmelCase       ):
						min_hash.update(token.encode()       )
			return min_hash






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			return {t for t in NON_ALPHA.split(__lowerCAmelCase       ) if len(t.strip()       ) > 0}





class 			_lowerCAmelCase      :




				def __init__(      self       ,       *,
				    _UpperCamelCase = 0.85       ,       )       ->   Dict:
							lowerCAmelCase_      			=			duplication_jaccard_threshold
							lowerCAmelCase_      			=			NUM_PERM
							lowerCAmelCase_      			=			MinHashLSH(threshold=self._duplication_jaccard_threshold       ,       num_perm=self._num_perm  )

							lowerCAmelCase_      			=			defaultdict(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self._index.query(_UpperCamelCase  )
							if code_key in self._index.keys:
										print(f"""Duplicate key {code_key}"""  )
										return

							self._index.insert(_UpperCamelCase       ,       _UpperCamelCase  )
							if len(_UpperCamelCase  ) > 0:
										for base_duplicate in close_duplicates:
													if base_duplicate in self._duplicate_clusters:
																self._duplicate_clusters[base_duplicate].add(_UpperCamelCase  )
																break
										else:
													self._duplicate_clusters[close_duplicates[0]].add(_UpperCamelCase  )




				def   __a		(      self  )       ->   List[List[Dict]]:
							lowerCAmelCase_      			=			[]
							for base, duplicates in self._duplicate_clusters.items():
										lowerCAmelCase_      			=			[base] + list(_UpperCamelCase  )
										# reformat the cluster to be a list of dict
										lowerCAmelCase_      			=			[{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
										duplicate_clusters.append(_UpperCamelCase  )
							return duplicate_clusters




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.get_duplicate_clusters()
							with open(_UpperCamelCase       ,       "w"  ) as f:
										json.dump(_UpperCamelCase       ,       _UpperCamelCase  )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_			,						lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			get_min_hash([t for t in NON_ALPHA.split(data["content"]       ) if len(t.strip()       ) > 0]       )
			if min_hash is not None:
						return (index, data["repo_name"], data["path"]), min_hash






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]       ):




			"""simple docstring"""


			with mp.Pool() as pool:
						for data in pool.imap_unordered(
						    _compute_min_hash					,							ThreadedIterator(__lowerCAmelCase					,							max_queue_size=10000       )					,							chunksize=100					,							):
									if data is not None:
												yield data






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			DuplicationIndex(duplication_jaccard_threshold=__lowerCAmelCase       )

			for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__lowerCAmelCase       )       )					,							max_queue_size=100       )       ):
						di.add(__lowerCAmelCase					,							__lowerCAmelCase       )

			# Returns a List[Cluster] where Cluster is List[str] with the filenames.
			return di.get_duplicate_clusters()






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			lowerCAmelCase_      			=			get_tokens(__lowerCAmelCase       )
			return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


_A     =  None






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			for elementa in cluster:
						lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
						for elementa in extremes:
									lowerCAmelCase_      			=			_shared_dataset[elementa["base_index"]]["content"]
									if jaccard_similarity(__lowerCAmelCase					,							__lowerCAmelCase       ) >= jaccard_threshold:
												elementa["copies"] += 1
												break
						else:
									lowerCAmelCase_      			=			1
									extremes.append(__lowerCAmelCase       )
			return extremes






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						List[str]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			global _shared_dataset
			lowerCAmelCase_      			=			dataset
			lowerCAmelCase_      			=			[]
			lowerCAmelCase_      			=			partial(_find_cluster_extremes_shared					,							jaccard_threshold=__lowerCAmelCase       )
			with mp.Pool() as pool:
						for extremes in tqdm(
						    pool.imap_unordered(
						        __lowerCAmelCase					,							__lowerCAmelCase					,							)					,							total=len(__lowerCAmelCase       )					,							):
									extremes_list.append(__lowerCAmelCase       )
			return extremes_list






def      lowerCamelCase__       (       __lowerCAmelCase						:						Type[Dataset]					,							__lowerCAmelCase						:						float = 0.85       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			make_duplicate_clusters(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_      			=			{x["base_index"] for cluster in duplicate_clusters for x in cluster}
			lowerCAmelCase_      			=			{}
			lowerCAmelCase_      			=			find_extremes(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )
			for extremes in extremes_clusters:
						for element in extremes:
									lowerCAmelCase_      			=			element
			lowerCAmelCase_      			=			duplicate_indices - set(extreme_dict.keys()       )
			lowerCAmelCase_      			=			dataset.filter(lambda __lowerCAmelCase					,							__lowerCAmelCase       : idx not in remove_indices					,							with_indices=__lowerCAmelCase       )

			# update duplicate_clusters
			for cluster in duplicate_clusters:
						for element in cluster:
									lowerCAmelCase_      			=			element["base_index"] in extreme_dict
									if element["is_extreme"]:
												lowerCAmelCase_      			=			extreme_dict[element["base_index"]]["copies"]

			print(F"""Original dataset size: {len(__lowerCAmelCase       )}"""       )
			print(F"""Number of duplicate clusters: {len(__lowerCAmelCase       )}"""       )
			print(F"""Files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Unique files in duplicate cluster: {len(__lowerCAmelCase       )}"""       )
			print(F"""Filtered dataset size: {len(__lowerCAmelCase       )}"""       )

			return ds_filter, duplicate_clusters



